MLOps Engineer

Level: Senior | Experience: 8–10 years | Location: Noida (hybrid)

Budget: Around 20 LPA

About the role

You own the infrastructure that keeps our AI products running — and running accurately — long after launch. This is a combined DevOps + MLOps role because the two disciplines now overlap heavily in production AI systems, and we'd rather have engineers who think across both than two silos that hand work over a wall.

You build the CI/CD pipelines, manage the cloud infrastructure, and operate the model lifecycle: deployment, monitoring, drift detection, retraining, and rollback. Our commercial model includes keeping client AI systems from silently degrading the 10–15% that unmonitored chatbots typically drop within months of launch.

This is a production-first role. If something breaks in a BFSI client's loan workflow at 2am, you're the person who finds out before they do.

Required qualifications

- 8–10 years in DevOps, SRE, or platform engineering, with the last 2+ years operating production ML or LLM systems (not just shipping models from notebooks to a REST endpoint).

- Robust Python (for tooling and pipeline code) and strong Bash. Working knowledge of Go or one statically-typed language is a plus.

- Production experience with at least one managed ML platform — AWS SageMaker, Azure ML, or Google Vertex AI — including endpoints, pipelines, and model registries.





- Production experience with Kubernetes and Terraform (or equivalent IaC).

- Hands-on experience with at least one CI/CD platform — GitHub Actions, GitLab CI, Jenkins, CircleCI, Argo CD.

- Demonstrable experience with observability tooling — Prometheus/Grafana, Datadog, CloudWatch, or the equivalent on Azure/GCP.

- Hands-on experience with drift detection and model monitoring — Evidently, WhyLabs, Arize, Fiddler, or a custom-built equivalent you can speak to in detail.

- Solid grasp of cloud cost optimisation — you've actually cut cloud bills, not just talked about it.

Preferred qualifications

- LLM-specific observability experience — LangSmith, LangFuse, Helicone, Arize Phoenix, or equivalent.

- Experience deploying and operating open-source LLMs on private infrastructure — Llama 3, Mistral, vLLM, TGI, Ollama, on bare metal or VPC.

- GPU infrastructure experience — provisioning, scheduling, cost management (A100/H100/L4 fleets).

- Experience with feature stores — Feast, Tecton, SageMaker Feature Store.

- Familiarity with DPDP Act 2023, HIPAA, and sector-specific compliance requirements (RBI cybersecurity guidelines, IRDAI, etc.).

- Prior on-call experience for production systems with paying clients.

- Experience supporting consulting or services-led engagement models.

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