Role Description

Lead MLOps Engineer Relevant Experience: 4+ Years Industrial Experience- 7+ Years Key Responsibilities

- Build, deploy, and manage end-to-end ML lifecycle pipelines.

- Automate model training, testing, deployment, and monitoring workflows.

- Implement model versioning, experiment tracking, and model registry solutions.

- Monitor model performance, drift, and operational health in production environments.

- Collaborate with Data Engineers, and DevOps teams to operationalize ML solutions.

- Establish governance, security, access control, and auditability processes for ML platforms.

Required

Skills

- Strong Python programming skills.

- Experience with ML lifecycle management and deployment automation.

- Hands-on expertise with: o MLflow o Kubeflow o Amazon SageMaker o AWS Step functions o ECS (Elastic Container Services)

- Knowledge of Docker and Kubernetes.

- Experience with CI/CD tools and DevSecOps practices.

- Familiarity with Terraform, CloudFormation, or similar IaC tools.





- Understanding of model monitoring, observability, and performance optimization.

Preferred

Skills

- Hands on experience with AWS.

- Knowledge or hands on experience of Agentcore.

- Knowledge of data engineering tools such as Databricks, Spark, Airflow, Kafka, or Snowflake.

- Understanding of Responsible AI, model governance, and compliance requirements.

- Exposure to Generative AI, LLMOps, and RAG-based solutions. Qualifications

- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field.

- Experience taking ML/AI solutions from Proof of Concept (PoC) to Production.

- Robust problem-solving and stakeholder management skills.

Skills

AWS, Amazon SageMaker, Observability, MLOps, AI Governance, DevSecOps, AWS Step Functions, Amazon ECS, Python

📌 Lead I - ML Engineering (Bengaluru)
🏢 UST
📍 Bengaluru