ML Ops Engineer

M37Labs; Gurugram District; Hybrid; INR 1,500,000 - 2,100,000; Full time

Benefits offered by this job

Cutting-edge AI platforms

Collaborative culture

Competitive compensation with growth

Remote-friendly hybrid work

Job summary

M37Labs is hiring an MLOps Engineer to own how AI and ML systems are built, deployed and kept healthy in production. You will bridge ML and infrastructure, setting up pipelines, cloud environments and monitoring for scalable, safe services used by clients daily.

You will work on CI/CD pipelines, model/versioning, RAG infrastructure, and deployment across AWS, GCP or Azure with Docker and Kubernetes, plus IaC and runbooks for repeatable releases.

Qualifications

• Bachelor's degree or equivalent practical experience in CS/Engineering.
• 4+ years in DevOps, MLOps or ML engineering with 2+ years running ML/LLM systems in production.
• Proven track record deploying production ML/AI systems.

Responsibilities

• Build and maintain CI/CD pipelines for ML models and LLM features.
• Set up model/versioning, experiment tracking and a model registry.
• Run LLM operations: API usage, cost control, evaluation pipelines and guardrails.
• Deploy and scale inference services on AWS, GCP or Azure using Docker and Kubernetes.
• Write infrastructure as code and runbooks for repeatable deployments.
• Monitor latency, cost, accuracy and data drift in production; implement alerts.

Skills

Python

Linux & shell scripting

CI/CD practices

Cross-functional collaboration

Agile ceremonies

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Docker

Kubernetes

Cloud (AWS/GCP/Azure)

MLflow, Weights & Biases, Kubeflow, Airflow

Vector DBs (pgvector, Pinecone, Qdrant)

Job description

We are seeking a skilled MLOps Engineer to own how our AI and ML systems are built,

deployed and kept healthy in production. This role is ideal for someone who enjoys taking

models and LLM-powered features from experimentation to reliable, scalable services used

by clients every day.

The successful candidate will sit at the intersection of machine learning and infrastructure,

setting up the pipelines, cloud environments and monitoring that let our AI products and

client solutions ship quickly and run safely.

Key Responsibilities

ML & LLM Operations

• Build and maintain CI/CD pipelines for ML models and LLM-based features.
• Set up model and prompt versioning, experiment tracking and a model registry.
• Run LLM operations: API usage and cost control, evaluation pipelines and guardrails.
• Build and manage RAG infrastructure, including vector databases and document processing

Infrastructure & Deployment

• Deploy and scale model and inference services on AWS, GCP or Azure using Docker and Kubernetes.
• Write infrastructure as code and clear runbooks so deployments are repeatable.
• Keep systems secure and compliant: secrets management, access control and client data isolation.

Monitoring & Reliability

• Monitor models in production for latency, cost, accuracy, data drift and failures, with alerting.
• Troubleshoot production issues and drive root-cause fixes.

Cross-Functional Collaboration

• Work with the AI Development Lead, developers and project managers to make releases faster and safer.
• Participate in code reviews, sprint planning and Agile ceremonies.

Must-Have Skills

• Strong Python, plus solid Linux and shell scripting.
• Hands-on experience with Docker, Kubernetes and at least one major cloud (AWS/GCP/Azure).
• Experience with MLOps tools such as MLflow, Weights & Biases, Kubeflow or Airflow.
• Experience serving models or LLM APIs (FastAPI, vLLM, BentoML, SageMaker, Vertex AI or similar).
• Working knowledge of vector databases (pgvector, Pinecone, Qdrant) and RAG pipelines.
• CI/CD (GitHub Actions, GitLab CI) and monitoring tools (Prometheus,

Nice-to-Have

• Experience with LLM observability tools (Langfuse, LangSmith, Arize).
• Fine-tuning experience (LoRA, PEFT).
• Infrastructure as Code with Terraform.
• Cost optimisation of GPU or LLM API workloads.
• Experience in a consulting or multi-client environment.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
• 4+ years in DevOps, MLOps or ML engineering, with at least 2 years running ML or LLM systems in production.
• Proven track record of production ML/AI deployments.

What We Offer

• Opportunity to work on cutting-edge AI-first, mobile-web integrated platforms.
• Dynamic, collaborative culture that values innovation and rapid execution.
• Competitive compensation with performance-based growth.
• Flexible remote-friendly work environment with hybrid collaboration where required.