Role: MLOps Engineer

Location: Pune

Experience: 7-15 Years

Detailed JD is given below:

Key Responsibilities

- Design, build, and maintain scalable MLOps platforms for training, deploying, monitoring, and managing machine learning models.
- Develop and optimize CI/CD pipelines for ML applications using GitLab CI, Jenkins etc.
- Containerize applications using Docker and orchestrate workloads using Kubernetes.
- Build and maintain ML pipelines using Kubeflow, MLflow, Airflow, Argo Workflows, or similar orchestration tools.
- Implement model versioning, experiment tracking, model registry, and reproducible ML workflows.
- Deploy ML models as REST/gRPC APIs using FastAPI, Flask, or similar frameworks.
- Configure monitoring, logging, and alerting using Prometheus, Grafana, Dynatrace, or Splunk.
- Optimize model serving performance, scalability, and resource utilization.
- Work closely with Data Scientists, Data Engineers, Platform Engineers, and Software Developers to productionize ML solutions.
- Ensure platform security, governance, and compliance following DevSecOps best practices.
- Troubleshoot production issues and perform root cause analysis for ML workloads.

Required Technical Skills

- Strong programming experience in Python.
- Hands-on experience with Docker, Kubernetes,



Helm, and containerized deployments.
- Robust knowledge of Git, branching strategies, merge requests, and CI/CD.
- Experience with MLflow, Kubeflow, Airflow, or similar MLOps tools.
- Experience with Spark/PySpark and distributed data processing.
- Knowledge of REST APIs, FastAPI, Flask, and microservices architecture.
- Experience with Linux, Bash scripting, and automation.
- Familiarity with model monitoring, drift detection, feature stores, and model governance.
- Experience working with SQL and NoSQL databases.
- Strong understanding of software engineering principles, design patterns, testing, and code quality.

Preferred Skills

- Experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and AI orchestration frameworks such as LangChain or lang-fuse.
- Experience with NVIDIA GPU workloads and model optimization.
- Knowledge of Kafka, RabbitMQ, or event-driven architectures.
- Exposure to feature stores such as Feast.
- Experience with OpenShift, Red Hat ecosystem, or enterprise Kubernetes platforms.
- Knowledge of security scanning tools such as Trivy, SonarQube, and vulnerability management.

📌 MLOps Engineer (Pune)
🏢 Mississippi Consultants Pune
📍 Pune