We are seeking a Senior MLOps Engineer to lead the design and scaling of enterprise-grade ML infrastructure. You will architect end-to-end ML lifecycle automation and guide teams in building robust, production-ready ML systems. This role requires deep expertise in MLOps tools, distributed systems, and cloud-native infrastructure.

Roles and Responsibilities :

- Lead the design and implementation of scalable MLOps platforms.

- Architect end-to-end ML workflows (data ingestion - feature engineering - training - deployment -monitoring).

- Own model versioning, model registry, and CI/CD automation at scale.

- Drive adoption of best practices for reproducibility, governance, and compliance.

- Mentor junior engineers and collaborate with ML researchers and DevOps teams.

- Optimize multi-cloud and hybrid ML deployments.

- Implement advanced monitoring (drift detection, explainability, lineage tracking).

- Evaluate and integrate cutting-edge MLOps tools (Ray, Flyte, BentoML, KServe).

Technical Skills Required :

- Experience with feature stores, federated learning pipelines, and model observability platforms.

- Familiarity with compliance-driven ML (GDPR, Responsible AI, bias detection).

- Exposure to LLMOps (large language model deployment & optimization).

- Experience leading cross-functional ML platform teams.

Qualification and Experience :

- Bachelor's/Master's in Computer Science, Data Engineering, or AI/ML.

- 6-10 years of experience in DevOps/MLOps with proven production ML deployment.

- Strong programming skills in Python, Go, Bash.

- Expertise in Kubernetes, Kubeflow, MLflow, Airflow, Terraform, CI/CD pipelines.

- Hands-on with cloud-native ML stacks (AWS Sagemaker, GCP Vertex AI, Azure ML, Databricks MLflow).

- Strong knowledge of distributed training, inference scaling, and model optimization.