We are seeking a Senior MLOps Engineer to build and maintain the infrastructure that takes machine learning models from experimentation to production. You will design robust CI/CD pipelines for ML workflows, automate model training and deployment, and ensure our AI systems are reliable, observable, and scalable. This role sits at the intersection of software engineering, DevOps, and data science.
- Design, build, and operate end-to-end ML pipelines from data ingestion and feature engineering to model training, validation, and serving.
- Implement CI/CD workflows for ML models including automated testing, versioning, and canary deployments.
- Manage and optimize model serving infrastructure (batch and real-time) for latency, throughput, and cost.
- Build monitoring and alerting systems for data drift, model performance degradation, and infrastructure health.
- Maintain experiment tracking platforms and model registries to ensure reproducibility and governance.
- Automate infrastructure provisioning using IaC tools (Terraform, Pulumi, CloudFormation).
- Collaborate with data scientists to containerize models and standardize training environments.
- Implement feature stores, data versioning, and lineage tracking for ML data assets.
- Develop internal tooling and self-service platforms to accelerate the ML development lifecycle.
- Ensure security, compliance,



and access controls across the ML platform.
- 5-8 years of experience in DevOps, platform engineering, or MLOps, with significant focus on ML systems.
- Robust programming skills in Python; working knowledge of Go, Bash, or similar.
- Hands-on experience with container orchestration (Kubernetes, Docker) and cloud services (AWS, GCP, or Azure).
- Proficiency with CI/CD tools (GitHub Actions, GitLab CI, Jenkins, Argo Workflows, or similar).
- Experience with ML pipeline frameworks (Kubeflow, Airflow, Prefect, Metaflow, or equivalent).
- Familiarity with model serving tools (TorchServe, Triton, TF Serving, Seldon, BentoML, or similar).
- Solid understanding of monitoring and observability (Prometheus, Grafana, Datadog, or equivalent).
- Bachelors degree in Computer Science, Software Engineering, or a related field.
- Experience building or managing GPU clusters for training workloads.
- Familiarity with feature store platforms (Feast, Tecton, or equivalent).
- Knowledge of data versioning tools (DVC, LakeFS, or similar).
- Experience with ML experiment tracking tools (MLflow, Weights & Biases, Neptune).
- Background in cost optimization for cloud-based ML workloads.
- Exposure to LLM deployment, vector databases, or RAG infrastructure.

📌 MLOps Engineer (Bengaluru)
🏢 Bounteous
📍 Bengaluru