Hire ML Engineer India 2026: Real INR Costs + Red Flags
Founder & Lead Developer, Codingclave · 200+ projects since 2017
A Bengaluru-based D2C founder we worked with messaged me on WhatsApp last quarter at 10:40pm. Quote in hand from a Hyderabad "ML agency." Fourteen lakhs for a churn prediction and lifetime-value model. Four months timeline. Zero detail on which features they would engineer, no mention of a feature store, no eval methodology beyond "we will use accuracy." He had paid 40% upfront. Five months in, all he had was a Jupyter notebook that scored 89% accuracy on a leaked holdout set and a Streamlit dashboard nobody on his team logged into.
We rebuilt the same system in six weeks for ₹5.8L fixed. F1 of 0.71 on a properly held-out cohort, deployed as a real-time API, monitored for drift, retrained monthly. Within 90 days his retention team recovered ₹38L in at-risk revenue using the churn scores routed through their existing WhatsApp flow.
This guide is what I wish that founder had read before he signed. Real INR pricing for hiring ML engineers in India in 2026. Honest tradeoffs. The exact red flags I see every week from founders who already torched their first ML budget on a notebook that never made it to production.
If you want to skip the reading and just talk to a human about your ML scope: WhatsApp me directly.
TL;DR: Hiring Model vs Cost vs Best For
| Hiring Model | 2026 Cost (INR) | Pros | Cons | Best For Stage |
|---|---|---|---|---|
| Freelancer (Upwork/Toptal/Arc) | ₹1,800-9,000/hr or ₹2-10L total | Fast start, cheap, flexible scope | No accountability, single point of failure, hard to scale | Prototype, PoC, one-off model |
| Indian agency (fixed-price) | ₹3-30L per project | Team accountability, delivery SLA, multi-skill | Less direct control, agency margin built in | Production ML features, post-PMF SaaS |
| Indian agency (dedicated) | ₹2.5-6L/month per engineer | Embedded team, faster iteration | Monthly commitment, scope creep risk | Continuous ML roadmap, 6-month+ work |
| Full-time mid-level | ₹20-40 LPA + benefits | Deepest context, equity-aligned | 8-14 week hiring, retention risk | ML is core product IP |
| Full-time senior LLM/MLOps | ₹50-90 LPA + ESOP | Senior IC who shapes ML strategy | 14-22 week hiring, ₹2 Cr top end | Series B+ with ML as moat |
| EOR (Deel, Remote, Wisemonk) | Salary + 8-12% EOR fee | Hire without entity setup | Slightly higher ongoing cost | US/UK founders hiring first India ML hire |
The mistake I see most: founders pick "full-time hire" because it sounds committed, then spend 5 months not hiring while their ML-enabled competitor ships fraud detection, recommendations, and demand forecasting. Speed of learning matters more than headcount in 2026.
Real Cost Breakdown: India ML Engineer Pricing in 2026
I have benchmarked this across 50+ scope conversations with Indian SMBs and Gulf founders over the past 14 months. These are the numbers that actually clear contracts and that show up on the Levels.fyi, AmbitionBox, and Glassdoor India data my recruiters pull weekly.
Full-Time Salary by Experience
- Fresher (0-1 year, Kaggle competitions or capstone projects in portfolio): ₹6-12 LPA at services firms (TCS, Infosys, Wipro, HCL), ₹10-18 LPA at AI-first product companies like Razorpay, Zomato, PhonePe, Swiggy, Cred, Sarvam, Krutrim.
- Mid-level (3-5 years, at least one model shipped to production): ₹20-40 LPA standard, ₹25-45 LPA if they have LLM serving or MLOps in production at scale.
- Senior (5-8 years, owns ML systems end-to-end): ₹35-70 LPA. LLM specialists and MLOps leaders touch ₹60-95 LPA at Series B+ startups.
- Staff / Principal IC (8+ years, model architecture and team leverage): ₹90 LPA to ₹2.4 Cr at FAANG India, Google Research India, Microsoft Research India, and well-funded AI startups.
City premium adds 25-45% in Bangalore and Hyderabad. Mumbai fintech premium adds 35-55% for finance ML roles like fraud, credit scoring, and risk. Tier-2 cities (Lucknow, Jaipur, Indore, Coimbatore, Kochi) discount the same skill by 30-50%, which is the arbitrage we run from Lucknow.
Freelancer Hourly Rates
- Junior on Upwork or Truelancer: ₹600-1,500/hr, risky unless your scope is tiny and you have an internal reviewer.
- Mid-level on Upwork or Arc: ₹1,800-3,500/hr.
- Senior on Toptal India: ₹4,000-8,000/hr.
- Specialist LLM serving, RecSys, CV, or MLOps freelancer with public portfolio: ₹6,500-12,000/hr.
Watch out: under ₹1,500/hr is almost always a fresher or someone who will subcontract to one. Over ₹15,000/hr on platforms is usually a US-based freelancer with Indian heritage charging Western rates through an Indian profile.
Agency Monthly and Fixed-Price
- Dedicated ML engineer, T1 agency (TCS Digital, Infosys Cobalt, Wipro AI, Mu Sigma): ₹4.5-7L per month per engineer, 3-month minimum.
- Dedicated ML engineer, mid-tier agency: ₹2.5-4.5L per month.
- Dedicated ML engineer, boutique (us included): ₹2-3.5L per month.
- Fixed-price churn or fraud model MVP: ₹1.5-4L (3-6 weeks).
- Fixed-price production recommendation or forecasting system: ₹6-14L (8-12 weeks).
- Fixed-price full MLOps platform with monitoring and retraining: ₹12-30L (10-18 weeks).
Fixed-Price Project Ranges by Use Case
- Customer churn or LTV model with batch scoring: ₹1.5-3L.
- Fraud detection model with real-time inference: ₹5-10L.
- Recommendation engine for D2C or content: ₹6-14L.
- Demand forecasting for retail or supply chain: ₹4-9L.
- Computer vision quality control (manufacturing, agritech): ₹6-18L.
- Document AI / OCR + extraction pipeline: ₹4-12L.
- Full MLOps platform with feature store + monitoring: ₹15-30L.
These are the numbers I quote on calls. Anyone wildly under or over should explain why on a feature-by-feature basis.
When to Hire Freelancer vs Agency vs Full-Time
A decision matrix from 8 years of watching founders get this wrong.
Hire a Freelancer When
- Scope fits one model and you can write a one-page spec.
- Project ends in under 12 weeks.
- You or someone on your team can code-review notebooks and PRs.
- Budget is under ₹10L total.
- You can absorb the risk of the freelancer disappearing mid-project.
Best fits: a demand forecasting PoC, a one-off recommendation model, a Kaggle-style competition entry, fine-tuning an open-source LLM on your dataset, a CV labeling pipeline.
Hire an Agency When
- You need ML plus data engineering plus DevOps plus monitoring shipped together.
- Delivery accountability matters more than the lowest hourly rate.
- You do not want to manage a freelancer's calendar, dependencies, or vacation.
- Project is production-bound with eval criteria, latency SLAs, and uptime requirements.
- You want one throat to choke (mine, in our case) when something breaks.
Best fits: production fraud detection for fintech, recommendation engines for D2C, demand forecasting for retail, healthcare ML with compliance overlay (ABDM, HIPAA), Gulf businesses with VAT and Arabic data nuances.
Hire Full-Time When
- ML is your core IP, the product literally IS the model.
- You have at least 18 months of continuous ML work mapped out.
- You can offer ₹25L+ LPA plus ESOP and a clear technical ladder.
- You have a senior engineer who can interview, hire, and mentor.
- You can absorb 8-22 weeks of hiring time.
Best fits: an AI-first startup post Series A, a marketplace whose recommendation quality is the moat, a fintech where credit scoring is the differentiator, a healthtech where the diagnostic model is the product.
If you are not sure, default to a fixed-price agency engagement for the first model. Once the use case is proven and you know what good looks like, hire full-time to own the roadmap.
ML Engineer Skill Checklist + Interview Questions
The questions I run on every senior interview, regardless of agency or full-time. Score honestly.
Technical ML Skills
- PyTorch or JAX proficiency, ideally both. Tensorflow alone in 2026 is a yellow flag for new hires.
- Distributed training experience with Horovod, DeepSpeed, or FSDP. Ask which one and why.
- Classical ML depth, can they explain when XGBoost beats a neural net for tabular data.
- Feature engineering judgment, can they walk you through how they would build features for a churn model in 30 minutes.
- Model evaluation, can they explain ROC-AUC vs PR-AUC and when each matters, and stratified k-fold vs time-series split.
MLOps Skills
- Feature store experience (Feast, Tecton, Hopsworks, or in-house).
- Model registry (MLflow, Weights and Biases, Kubeflow).
- Online vs batch serving tradeoffs (BentoML, KServe, Triton, SageMaker, Vertex AI).
- Monitoring for drift (EvidentlyAI, WhyLabs, Arize, in-house).
- CI/CD for ML, can they describe their last pipeline from PR to canary to prod.
Data and Infrastructure
- SQL deep enough to write window functions and CTEs without Stack Overflow.
- Comfort with Spark, Airflow, or dbt for data pipelines.
- Cloud experience on at least one of AWS (SageMaker, Bedrock), GCP (Vertex, Dataflow), or Azure (Azure ML).
- Comfort with Docker, Kubernetes basics, and at least one IaC tool.
Interview Questions That Actually Work
- Walk me through the last model you shipped to production. What was the business KPI it moved and by how much?
- Describe a time a model worked in notebooks but failed in production. What did you do?
- How would you design a recommendation system for our use case at 10M users with under 200ms p95 latency?
- What is your default eval setup for a tabular classification problem with 5% positive class?
- When would you NOT use a deep learning model?
- How do you decide when to retrain a model in production?
- Pick a paper from the last 6 months you actually read. What would you implement differently than the authors?
If they cannot name the eval metric they optimized last time and the business KPI it moved, they have not shipped real ML, full stop.
Cost Comparison: India vs US, UK, Singapore, Dubai
A side-by-side for the same skill level (senior ML engineer, 5-8 years, shipped production models, LLM or MLOps specialty).
| Geography | Full-Time Annual | Freelancer/hr | Agency Engineer/Month |
|---|---|---|---|
| United States | $250K-$400K total comp | $120-$220 | $25K-$45K |
| United Kingdom | £130K-£200K total | £85-£160 | £18K-£32K |
| Germany | EUR 110K-170K | EUR 90-150 | EUR 17K-28K |
| Singapore | SGD 180K-280K | SGD 110-200 | SGD 22K-36K |
| Dubai/UAE | AED 380K-560K | AED 350-700 | AED 60K-95K |
| India | ₹35-70 LPA ($42K-$84K) | ₹1,800-9,000 ($22-$108) | ₹2.5-6L ($3K-$7K) |
The arbitrage is real, but it is not free. Quality variance in India is wider than any other geography in this table. The top 5% of Indian ML talent ships at par with top 30% of Bay Area engineers. The bottom quartile will lose you 60% of your budget on Jupyter screenshots. Vet hard on shipped production models with real users and measurable business KPI movement, not on certifications, courses completed, or Kaggle ranks alone.
Quick math for a US founder hiring a senior ML engineer in India: full-time at ₹50 LPA plus 10% EOR fee comes out to roughly $66K-$72K all-in vs $280K-$340K equivalent in the US. That is a 4-5x cost wedge with quality you can verify in 2 weeks of interviewing. The same wedge holds for fixed-price agency work: a ₹12L production fraud model build with us costs roughly $14K vs $90K-$140K at a US ML boutique.
Red Flags When Hiring an ML Engineer or Agency
I see these every week. Each one alone is a yellow flag. Three or more and you should walk.
- AI and ML used interchangeably without distinguishing GenAI, classical ML, and MLOps. Real practitioners separate these in the first 5 minutes of conversation.
- No public artifacts, no GitHub, no Kaggle, no blog, no shipped product with a live URL you can click.
- Vanity case studies, claims like "100+ ML projects delivered" but every case study shows Jupyter screenshots and accuracy numbers with no production deployment, no monitoring, no business KPI movement.
- No opinion on model selection, if they say "we will use whatever works" instead of comparing XGBoost vs LightGBM vs a neural net for your specific tabular problem, they have not shipped models that mattered.
- Sub-₹1,500/hr senior rates or ₹50K/month full-stack ML engineer, that is a fresher in disguise or someone who will subcontract to one.
- No eval methodology, if they cannot articulate train-validation-holdout split, stratified k-fold vs time-series split, or how they handle class imbalance, they have not built real models.
- No MLOps mention, models that ship without monitoring rot inside 8 weeks. If they treat deployment as "we will containerize the notebook," walk.
- Refusal to share code mid-project, or insistence on their proprietary tooling that locks you in.
- No senior engineer or founder on calls past the sales pitch, the bait-and-switch is real in Indian agencies. Confirm in writing who will lead delivery.
- No conversation about data quality or labels, if they assume your data is clean and ready, they have never shipped against real enterprise data.
If three or more apply, walk away. Most founders rationalize their way past two or three red flags and pay for it 4 months later.
The Codingclave ML Engineering Offering
We run a fixed-price ML build model for Indian SMBs, D2C brands, healthcare, fintech, recharge platforms, and Gulf businesses. Three tiers, transparent pricing, founder on every call.
Starter (₹1.5-3L, 3-5 weeks)
One production model end-to-end. Use cases: customer churn, lifetime value, lead scoring, demand forecasting, recommendation MVP, fraud baseline, computer vision PoC. Includes feature engineering, model training with proper holdout eval, batch inference deployed on AWS Lambda or Cloud Run, model card, retraining playbook, and a 30-minute handover call. Best for: validating that ML actually moves your business KPI before investing in MLOps.
Growth (₹4-10L, 6-10 weeks)
Real-time inference API with under 200ms p95 latency, feature store on Feast or DynamoDB, MLflow model registry, drift monitoring with EvidentlyAI, A/B testing harness, integrates with your product or CRM (HubSpot, Salesforce, Zoho, in-house). Best for: D2C brands shipping personalization, fintech shipping fraud or credit scoring, healthtech shipping risk stratification, retail shipping demand forecasting.
Scale (₹10-22L, 10-16 weeks)
Full MLOps pipeline from training to deployment to monitoring. Multi-model serving on KServe or BentoML, automated retraining triggered by drift thresholds, SOC2-friendly deployment with VPC isolation, on-call runbook, model card, and a 30-day post-launch tuning window. Best for: Series A+ startups where ML is core product IP, healthcare platforms with ABDM or HIPAA overlap, Gulf businesses with multilingual data nuances.
We do not sell hours. We sell shipped models with measurable business impact. Founder Ashish Sharma stays on every project call until production handover and the first retraining cycle. If the model does not move the KPI we agreed on, we keep working at no extra cost until it does.
WhatsApp +91 92771 84741 to scope your problem in 20 minutes.
Client Story: Bengaluru D2C Churn Model
A Bengaluru-based D2C beauty brand we worked with (₹85 Cr ARR, 320K active subscribers) came to us after a Hyderabad agency had spent four months and ₹14L on a churn model that lived in a notebook. The accuracy was 89% on a holdout split that turned out to be leaked (the same customers appeared in both train and test). The Streamlit dashboard their CRM team never opened was the only production artifact.
Scope we ran:
- Week 1: data audit. We found 11 features in their event stream that the previous team had ignored, and we removed 4 that were post-event leakage.
- Week 2-3: feature engineering and model selection. Tried LightGBM, XGBoost, and a small TabNet. LightGBM won on F1 and inference latency.
- Week 4: built a proper time-series holdout (last 6 weeks unseen), F1 of 0.71, precision-recall AUC 0.78.
- Week 5: deployed as a FastAPI inference service on Cloud Run, behind their existing API gateway, p95 latency 140ms.
- Week 6: integrated churn scores into their WhatsApp retention flow via webhook, drift monitoring with EvidentlyAI, retraining scheduled monthly.
Fixed price: ₹5.8L. Timeline: 6 weeks. Within 90 days the retention team recovered ₹38L in at-risk revenue using the churn scores routed to their existing win-back WhatsApp flow. They now run with us on a ₹2.5L/month dedicated engagement to ship their next two models (cross-sell recommendation and shipping-delay-aware demand forecasting).
This is what I mean by founder-to-founder. Real numbers, real outcomes, no agency theatre.
Ready to Hire? Let's Talk
If you are hiring an ML engineer or agency in India in 2026 and you want a 20-minute scope call with a founder who has shipped ML for D2C, fintech, healthcare, and Gulf businesses, message me on WhatsApp.
WhatsApp Ashish about your ML scope
I will tell you honestly whether you need a freelancer, an agency, or a full-time hire. If we are not the right fit I will tell you that too.
About the Author
I am Ashish Sharma, founder of Codingclave, a Top Rated Upwork agency based in Lucknow. Eight years building custom software and ML systems for Indian SMBs, D2C brands, healthcare platforms, fintech startups, and Gulf businesses. I have personally led delivery on 40+ ML projects spanning churn, fraud, recommendation, demand forecasting, computer vision, and document AI. Find me on LinkedIn or WhatsApp +91 92771 84741.
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Frequently asked questions
Three real price points depending on engagement model. Full-time salary: freshers cost ₹6-12 LPA at services firms and ₹10-18 LPA at AI-first product companies. Mid-level (3-5 years, shipped models in production) sits at ₹20-40 LPA. Senior (5-8 years) at ₹35-70 LPA. Staff or principal IC with LLM serving or foundation-model experience touches ₹1.2-2 Cr at FAANG India, Sarvam, Krutrim, and Series B+ AI startups. Freelancer hourly: ₹1,800-3,500/hr mid-level, ₹4,000-9,000/hr senior with shipped portfolio. Agency model: most credible Indian ML agencies charge ₹2.5L-6L/month per dedicated engineer or fixed-price builds at ₹3L-30L depending on scope (churn prediction model vs full MLOps platform). At Codingclave we run fixed-price ML builds starting ₹1.5L for a churn or fraud model MVP and ₹6-22L for production MLOps pipelines. Anyone quoting ₹40K/month for a senior ML engineer is selling you a fresher in disguise.
Decide by the shape of the work, not the rate card. Pick a freelancer when scope is well-defined, sub-3-month, and you have an internal engineer who can review code (think one-off recommendation model, demand forecasting prototype, computer vision PoC). Budget ₹2-10L total. Pick an agency when you need delivery accountability, a multi-skill team (ML engineer plus data engineer plus DevOps), and you do not want to manage anyone. Best fit for production ML features inside SaaS, fraud detection, personalization engines, demand forecasting, or compliance-bound systems in healthcare and fintech. Budget ₹3-30L fixed-price. Pick a full-time hire when ML is your core IP, you have at least 18 months of work for them, and you can offer ₹25L+ LPA plus ESOP. Most Indian SMBs lose money trying to hire full-time too early because they cannot retain a ₹45 LPA senior ML engineer past month 14 when bigger offers land in their inbox.
Forget generic Python and scikit-learn buzzwords. Four bars that actually matter. One, ML system design under realistic constraints, can they architect a recommendation system that handles 10M users with under 200ms p95 latency and cold-start. Two, applied math intuition, can they explain when to use gradient boosting versus deep learning versus a logistic regression for tabular data without reciting textbook lines. Three, MLOps fluency, do they know feature stores like Feast or Tecton, model registries like MLflow, monitoring for drift with EvidentlyAI or WhyLabs. Four, production debugging, ask about a model that worked in notebooks but failed in production and what they did. Cross-cutting skills: SQL deep enough to write window functions, PyTorch or JAX proficiency (not just Keras tutorials), comfort with distributed training (Horovod, DeepSpeed, FSDP), and at least one shipped model with real users. If they cannot name the eval metric they optimized and the business KPI it moved, they have not shipped.
India runs roughly 14-20% of US total cost at senior levels. Specific 2026 numbers for a senior ML engineer with 5-8 years experience. US base $180K-$240K plus equity, total comp $250K-$400K. UK £90K-£140K plus benefits. Singapore SGD 150K-230K. Dubai AED 380K-560K. India ₹35-70 LPA which converts to roughly $42K-$84K fully loaded. Freelancer hourly: US $120-$220/hr, UK £85-£160/hr, India ₹1,800-9,000/hr (about $22-$108/hr). Agencies follow the same gap, Western boutiques quote $140-$200/hr while Indian agencies run $28-$75/hr. Important caveat: quality variance is much wider in India. The top 5% of Indian ML engineers ship at par with top 30% of Bay Area engineers, but the bottom quartile of Indian agencies will burn 60% of your budget on Streamlit demos. Vet on shipped production models with real users, not certifications or Kaggle ranks alone.
Nine red flags that have cost our clients money before they came to us. One, anyone using AI and ML as one bundled term without distinguishing GenAI from classical ML from MLOps. Two, no public GitHub, no Kaggle, no shipped product they can show running live. Three, claims of 100+ ML projects delivered but case studies all show Jupyter screenshots with no production deployment. Four, no opinion on model selection, if they say we will use whatever works instead of comparing XGBoost vs LightGBM vs a neural net for your tabular problem, walk. Five, hourly rate under ₹1,500/hr or full-stack ML engineer under ₹50K/month, that is almost always a fresher subcontract. Six, no eval methodology, if they cannot articulate train-test-validation split, k-fold cross validation, or holdout strategy, they have not built real models. Seven, no MLOps mention, models that ship without monitoring rot inside 8 weeks. Eight, refusal to share code mid-project or insistence on their tooling that locks you in. Nine, no senior engineer or founder on calls past the sales pitch. If three or more apply, walk away.
Realistic timelines assuming you know what you want. Freelancer (Upwork, Toptal, Arc): 5-14 days from job post to first paid milestone. Agency engagement: 3-10 days from first call to signed SOW and kickoff if your scope is crisp. Full-time hire via in-house recruiting: 8-14 weeks for mid-level, 14-22 weeks for senior with LLM or MLOps specialization because top candidates juggle 3-5 active offers in 2026. Full-time via Employer of Record (Deel, Remote, Wisemonk, Velocity Global): 5-7 business days once you have identified the candidate. The bottleneck is almost never sourcing, it is defining the role precisely. We have watched founders take 5 months to hire because they oscillated between we need an ML researcher and we need an MLOps engineer. Write a scope document first, then start interviews. If you want speed, agency or freelancer beats full-time every single time.
City premium is real but smaller than people assume in 2026 post-remote. Bangalore and Hyderabad pay 25-45% above national average for ML roles because product company and GCC density is highest there. Mumbai pays 35-55% fintech premium for finance ML roles like fraud and credit scoring. Pune and Chennai run at national median. Delhi NCR sits 10-20% above median for enterprise ML at Microsoft, Adobe, and IBM Research. Tier-2 cities (Lucknow, Jaipur, Indore, Coimbatore, Bhubaneswar, Kochi) run 30-50% below Bangalore for equivalent skill, and remote-first hiring has unlocked that pool. We are based in Lucknow and we hire Tier-2 senior ML engineers at ₹20-28 LPA who would cost ₹40-50 LPA in Bangalore. For freelancer or agency work, city is almost irrelevant, focus on time zone overlap with your team and the portfolio of shipped models. Remote-first is the dominant model for ML engineering hires in India in 2026.
Yes, and the gap closed faster than most US founders expected. As of 2026, Indian engineering teams have shipped production ML at scale for Fortune 500s. Flipkart runs recommendation at over 400M users with India-led teams. Razorpay ships fraud models from Bangalore. Microsoft Research India, Google Research India, and Adobe MAX India lead serious ML research. The top 10% of Indian ML talent ships large-scale recommendation, fraud detection, demand forecasting, computer vision, NLP at scale, and fine-tuned LLMs at the same quality as Bay Area equivalents. Where India still lags: pure foundational research roles are fewer (most Indian PhDs go to US/EU labs), and senior IC engineers with 10+ years of LLM serving experience are scarce because the field is only 4-5 years old at scale. For 95% of business ML use cases, Indian teams deliver equal quality at 30-40% of Western cost. Always ask for a live production deployment URL, not a Loom demo or notebook screenshot.
Six clauses that save real pain. One, IP assignment in writing, all code, trained model weights, fine-tuned checkpoints, prompt templates, and eval datasets transfer to you on payment. Indian default is work-for-hire but make it explicit. Two, milestone-based payment, never 100% upfront and never 100% on delivery. Standard split: 30% kickoff, 40% mid-milestone, 30% on production handover with monitoring live. Three, defined success criteria with numbers, accuracy or F1 thresholds, latency targets (p95 under 200ms for online inference, under 2s for batch scoring), uptime SLA. Without numbers, model works well is unenforceable. Four, model and cloud account ownership, you own the AWS/GCP/Azure account, MLflow registry, and any API keys. Their account under their billing equals dependency forever. Five, knowledge transfer clause, last milestone includes documentation, model card, runbook, retraining playbook, and a recorded handover call. Six, retraining cadence, who retrains the model and at what frequency, and what triggers a retrain (drift threshold, business KPI drop). For freelancers add a non-compete clause, they cannot deploy the same model or pipeline for direct competitors for 12 months. Use Indian arbitration in your home city for disputes.
We run a fixed-price ML build model for Indian SMBs, D2C brands, healthcare, fintech, recharge platforms, and Gulf businesses. Three tiers. Starter (₹1.5-3L, 3-5 weeks): one production model (churn, fraud, demand forecast, recommendation, or computer vision), eval suite, batch inference deployed on AWS Lambda or Cloud Run, model card and retraining playbook. Growth (₹4-10L, 6-10 weeks): real-time inference API with under 200ms p95, feature store on Feast or DynamoDB, MLflow model registry, drift monitoring with EvidentlyAI, A/B testing harness, integrates with your product or CRM. Scale (₹10-22L, 10-16 weeks): full MLOps pipeline (training to deployment to monitoring), multi-model serving on KServe or BentoML, automated retraining triggered by drift, SOC2-friendly deployment, on-call runbook. We do not sell hours, we sell shipped models with measurable business impact. Founder Ashish Sharma stays on every project call until production handover and the first retraining cycle. WhatsApp +91 92771 84741 to scope your problem in 20 minutes.