Lead I - ML Engineering (ML Ops , Agentic AI and AWS)

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. • Strong problem-solving and stakeholder management skills.