TL;DR
An LLMOps or ML platform engineer is the engineer who builds the shared platform that lets teams train, version, deploy, monitor and govern models and LLM applications reliably: Kubernetes and Ray compute, model and prompt registries, CI/CD for models, evaluation gates, observability and audit trails.
To hire one in India, look for someone who has built a platform other teams used, not only pipelines for one model, and who can show deployment speed, reliability and auditability improving because of it.
Key points
- LLMOps extends MLOps to LLM applications: prompts, retrieval indexes, model versions and evaluation sets all need versioning and release control, not just model weights.
- The strongest candidates are platform engineers first. Kubernetes, infrastructure as code and CI/CD depth matter more than familiarity with any one ML tool.
- In banks and regulated firms the platform is how model inventory, lineage and audit requirements are met, which makes this seat central to governance.
- The pool in India is larger than for LLM or inference engineers, drawn from DevOps, SRE, data platform and MLOps backgrounds.
- On SYTalent Market Data for 2027, fixed pay runs from ₹20 lakh to ₹38 lakh at mid level to ₹55 lakh to ₹85 lakh at staff level.
What the role owns
An LLMOps or ML platform engineer owns the paved road every model and AI application travels from experiment to production. The table sets out the areas most mandates cover.
| Area | What they own | How success is measured |
|---|---|---|
| Compute platform | Kubernetes clusters with GPU scheduling, Ray clusters, quotas and cost allocation by team | GPU utilisation, queue times, cost visible per team |
| Training and pipelines | Orchestrated training, fine-tuning and batch pipelines with Kubeflow, Ray or Airflow | Reproducible runs any team can launch |
| Registries and versioning | Model registry, prompt registry, dataset and feature versioning, lineage | Every production model and prompt traceable to its data and code |
| CI/CD for models | Automated tests, evaluation gates, canary and shadow releases, rollback | Deployment frequency up, failed releases down |
| Observability | Tracing of LLM calls, latency and cost dashboards, drift and quality monitoring, alerting | Incidents caught before users report them |
| Governance and security | Access control, secrets, audit logs, model inventory, guardrail and gateway policies | Audits passed without manual evidence gathering |
How it differs from adjacent roles
The distinction is scope. An LLMOps engineer builds for many models and teams; adjacent roles optimise one model, one serving path or general infrastructure.
| Role | Primary concern | Typical output | Measured on |
|---|---|---|---|
| LLMOps or ML platform engineer | Reliable, governed path to production for all models | Platform, registries, CI/CD, observability | Deployment velocity, reliability, auditability |
| Inference engineer | Speed and cost of serving | Tuned serving stack | Latency, throughput, cost per token |
| DevOps or SRE engineer | General infrastructure and uptime | Clusters, pipelines, runbooks | Uptime and error budgets |
| AI evaluation engineer | Whether model outputs are good enough | Test sets, eval harnesses, quality reports | Regressions caught, decision quality |
Small companies combine these seats; split them once several product teams depend on the platform.
The evidence bar
Credible platform work is measured by what other teams could do because of it.
- Platform, not pipeline. "Built a model deployment platform on Kubernetes and KServe used by several product teams" shows scope. "Deployed a model with Docker" does not.
- Named systems and choices. Which registry, orchestrator and tracing tools were chosen, what was rejected and why.
- LLM-specific versioning. How prompts, system instructions, retrieval index versions and model versions were tied together, so a regression could be traced to a single change.
- Evaluation gates. Automated evaluations in CI that block a release, and how the thresholds were agreed with product and risk teams.
- Observability. Tracing of multi-step LLM and agent calls with OpenTelemetry or an LLM observability tool, plus cost per request and per team.
- Reliability and cost. GPU scheduling, autoscaling, spot or reserved capacity, and an incident they handled with what changed afterwards.
- Security and governance. Secrets management, role-based access, audit logs, PII redaction before logging, and an LLM gateway enforcing policy.
Red flags
- All experience is running notebooks or a single team's scripts, with no platform used by others.
- Can list MLflow, Kubeflow and Ray but cannot explain how a model moved from training to production in their last role.
- No answer for how a prompt change is versioned, tested and rolled back.
- Logs full prompts and responses with customer data and has not thought about redaction.
- Has never been on call for the systems they built.
- Picks tools by popularity rather than by team size, cloud and governance needs.
Stack and tools
Assess platform principles and accept equivalent tools.
| Layer | Tools commonly assessed |
|---|---|
| Compute and orchestration | Kubernetes, Ray and KubeRay, Kueue or Volcano for GPU scheduling, Slurm in research settings |
| Pipelines | Kubeflow Pipelines, Airflow, Argo Workflows, Dagster, Prefect |
| Experiment tracking and registry | MLflow, including its model and prompt registry, Weights & Biases, cloud registries in SageMaker, Vertex AI or Azure Machine Learning |
| Serving and gateways | KServe, Ray Serve, vLLM, LiteLLM or other LLM gateways, Envoy-based AI gateways |
| CI/CD and infrastructure as code | GitHub Actions or GitLab CI, Argo CD, Terraform, Helm |
| LLM observability and evaluation | OpenTelemetry, Langfuse, Arize Phoenix, LangSmith, promptfoo, Ragas, DeepEval |
| Monitoring | Prometheus, Grafana, Datadog, Evidently for drift |
| Data and features | Delta Lake or Apache Iceberg, Feast, vector databases such as pgvector, Milvus or Qdrant |
| Security | Vault or cloud secret managers, OPA policies, PII redaction tooling |
Interview loop that works
Test platform design and operational judgement, then a hands-on build. Four or five stages is enough.
| Stage | What it tests |
|---|---|
| Technical screen | One platform they built, who used it, what improved |
| Platform design | Designing a model and prompt release path for several teams |
| Practical exercise | Hands-on CI/CD with an evaluation gate |
| Operations and incident round | Debugging a failed deployment, on-call judgement |
| Hiring manager round | Working with ML, product, security and risk teams |
Practical exercise. Give a small repository containing an LLM application, a prompt file, a short evaluation set and a local Kubernetes cluster. Ask the candidate, over half a day, to build a pipeline that versions the prompt and model, runs the evaluation on every change, blocks the release if quality drops, deploys with a canary, and emits traces with cost per request. Score the release and rollback design and the written trade-offs more than the tooling chosen.
Where they come from in India
India has a deep pool of DevOps, SRE and data platform engineers, and most LLMOps hires convert from there. The scarce part is LLM-specific release and evaluation experience.
- GCCs. Capability centres of banks, retailers and technology companies run large internal ML platforms, often the best source for governance-heavy roles.
- Product companies and unicorns. ML platform teams supporting recommendation, search, fraud and now LLM features.
- AI-native startups. Smaller teams where one engineer built the whole platform, strong on breadth.
- IT services AI practices. Large pools of MLOps engineers; screen for ownership of a platform rather than delivery of client tickets.
- BFSI and FinTech. Platform engineers who already work within audit, access control and model inventory rules, as our Head of Platform guide describes.
- Trading firms. Infrastructure engineers with strong Kubernetes and reliability depth, smaller in number.
Bengaluru, Hyderabad and Pune have the deepest pools, followed by Delhi NCR, Chennai and Mumbai. See AI jobs in India for the wider market and our AI engineer search for a live adjacent brief.
What they cost
LLMOps pay tracks senior platform engineering with a modest AI premium for genuine LLM release and evaluation experience. The ranges below are SYTalent Market Data on 2027 fixed pay at Indian product companies and GCCs in Bengaluru, from our software engineering and AI salaries benchmark.
| Level | Experience | SYTalent Market Data, 2027 fixed pay |
|---|---|---|
| Entry | 0 to 2 years | ₹10 lakh to ₹18 lakh |
| Mid | 3 to 5 years | ₹20 lakh to ₹38 lakh |
| Senior | 6 to 9 years | ₹35 lakh to ₹60 lakh |
| Staff or lead | 10 to 14 years | ₹55 lakh to ₹85 lakh |
On the same data, IT services pay roughly 0.55 to 0.75 times these figures, and Big Tech 1.2 to 1.5 times before RSUs. Hyderabad, where GCC competition is intense, often prices senior platform seats at Bengaluru levels.
Hiring in GCCs, banks and regulated firms
In a bank or NBFC the ML platform is where governance becomes real, so the LLMOps engineer is effectively building the control system that risk and audit teams rely on.
The RBI's draft model risk management guidance of 24 June 2026 applies to all models used by regulated entities, including AI and third-party models. Its practical demands, such as a model inventory, validation before use and monitoring after it, are platform features: registries, lineage, evaluation gates and audit logs. The RBI's FREE-AI committee report of 13 August 2025 points the same way with its recommendation of board-approved AI policies.
Data rules shape the architecture too. The RBI's payment data storage rules require payment data to be stored only in India, with limited exceptions, and the Digital Personal Data Protection Rules, 2025 govern personal data in prompts, logs and training sets. Expect in-region deployment, PII redaction before logging, and retention policies for traces. GCCs add the parent's AI policy and approved tool list. The Head of AI guide covers the governance seat above this one, and our AI jobs in fintech and banking page covers the market. SYTalent's AI, deep tech and quantum practice runs platform searches; to start one, brief us or browse other role guides.
Sources
Regulatory references come from the official sources below. Pay ranges are SYTalent Market Data.
| Source | Publisher | Date |
|---|---|---|
| Draft Guidance on Regulatory Principles for Model Risk Management | Reserve Bank of India | 24 June 2026 |
| FREE-AI committee report, press release | Reserve Bank of India | 13 August 2025 |
| Storage of Payment System Data, FAQs | Reserve Bank of India | Accessed 10 November 2026 |
| Digital Personal Data Protection Rules, 2025 | Press Information Bureau | November 2025 |
Common questions
What does an LLMOps engineer do?
An LLMOps engineer builds and runs the platform that takes LLM applications from experiment to production. The work covers compute on Kubernetes and Ray, versioning of models, prompts and retrieval indexes, CI/CD with automated evaluation gates, canary releases and rollback, tracing and cost monitoring of LLM calls, and the access controls and audit logs that governance teams need. The aim is that every team ships safely without rebuilding the plumbing.
How do I hire an LLMOps or ML platform engineer in India?
Source from GCC ML platform teams, product company infrastructure teams, AI-native startups and strong DevOps or SRE engineers who have moved into ML. Screen for a platform other teams used, not just one pipeline. Run a platform design round and a hands-on exercise building a release pipeline with an evaluation gate, and check on-call experience for systems the candidate built.
What is the salary of an MLOps or LLMOps engineer in India?
On SYTalent Market Data for 2027, an MLOps or LLMOps engineer in India earns about ₹10 lakh to ₹18 lakh in fixed pay at entry level, ₹20 lakh to ₹38 lakh at three to five years, ₹35 lakh to ₹60 lakh at senior level and ₹55 lakh to ₹85 lakh at staff level. Big Tech pays above these ranges and IT services below.
LLMOps vs MLOps: what is the difference?
MLOps covers the lifecycle of trained models: pipelines, registries, deployment and monitoring. LLMOps adds what LLM applications need on top: versioning prompts and retrieval indexes alongside models, automated evaluation of generated text, tracing multi-step agent calls, token cost tracking, gateways that route between providers, and guardrails. Most LLMOps engineers start in MLOps or platform engineering and add these skills.
What tools should an ML platform engineer know?
Kubernetes is the foundation, with Ray, Kubeflow or Airflow for pipelines, MLflow or a cloud registry for models and prompts, KServe or Ray Serve for deployment, Terraform and Argo CD for infrastructure and releases, and OpenTelemetry with an LLM observability tool such as Langfuse or Arize Phoenix. Judge how candidates chose and combined tools rather than how many they list.
Why do banks need ML platform engineers for AI governance?
Model risk rules turn into platform requirements. The RBI's June 2026 draft model risk guidance covers all models used by regulated entities, including AI and third-party models, which implies an inventory, validation and ongoing monitoring. A platform with registries, lineage, evaluation gates and audit logs produces that evidence automatically, instead of through manual spreadsheets before every audit.