ML Ops Developer

(5 - 12 Years of Experience)

Locations : Bangalore, Hyderabad, Pune, Delhi, Chennai, Vishakhapatnam/Vizag

Role Overview :

The ML Ops Developer will be instrumental in bridging the gap between machine learning model development and production deployment. This role involves designing, implementing, and managing robust MLOps pipelines, ensuring seamless integration, continuous delivery, and reliable operation of Machine Learning models. Working closely with Data Scientists, ML Engineers, and Software Development teams, you will drive the automation of model lifecycle management from experimentation to deployment and monitoring. Your contributions will directly impact the speed at which innovative ML-driven solutions reach our users and customers, significantly enhancing business outcomes and maintaining our competitive edge.

Key Responsibilities :

- Design and implement scalable and reliable MLOps platforms and CI/CD pipelines for Machine Learning models, enabling rapid experimentation and deployment for Data Science teams.

- Automate the end-to-end Machine Learning model lifecycle, including data ingestion, model training, versioning, deployment, and monitoring, to ensure operational efficiency and model performance.

- Collaborate with Data Scientists and ML Engineers to containerize and orchestrate ML applications using technologies like Docker and Kubernetes, facilitating consistent deployment across environments.

- Develop and maintain monitoring systems for deployed ML models, tracking performance, drift, and data quality to ensure models continue to deliver accurate predictions and business value.

- Establish best practices for model governance, security, and compliance within the MLOps framework, safeguarding data integrity and regulatory adherence.

- Optimize infrastructure and resource utilization for ML workloads on cloud platforms (AWS, Azure, GCP), reducing operational costs and improving scalability.

Required Skillset :

- Demonstrated expertise in designing, building, and managing MLOps platforms and CI/CD pipelines for Machine Learning applications.

- Proficiency in programming languages such as Python, coupled with strong experience in cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).

- Solid understanding of Machine Learning algorithms, model development lifecycle, and performance monitoring techniques.

- Ability to collaborate effectively with cross-functional teams, including Data Scientists, Software Engineers, and Product Managers, to translate business requirements into technical solutions.

- Exceptional problem-solving skills and a proactive approach to identifying and resolving complex technical challenges in a fast-paced environment.

- Strong knowledge of MLOps principles and the end-to-end ML lifecycle : data preparation / training / validation / deployment/serving / monitoring/refresh pipelines.

- Design and implement CI/CD (and CT/continuous training) pipelines for ML workflows, including testing, promotion, rollback, and reproducible builds.

- Hands-on with containerization and orchestration (e.g., Docker/Kubernetes) and ML pipeline tooling such as MLflow/Kubeflow (or equivalent).

- Monitoring & observability for ML systems : service + data + model health tracking, drift checks (feature/target/concept), alerts/triggers, and root-cause analysis.

- Cloud platform experience (AWS/Azure/GCP) to deploy and run ML workloads using managed services and cloud-native components (e.g., GKE, BigQuery, Cloud Storage, Vertex AI capabilities).

- Security, governance, and access controls : authentication/authorization, encryption, policy/guardrails, and compliance-focused logging/traceability for production ML.

- Cross-functional collaboration with data scientists, engineers, and platform teams to productionize models following best practices for repeatability, standardization, and operational efficiency.

- Proficiency in programming languages such as Python, .Net or Java, with experience in relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Keras).