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

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.
• Strong problem-solving and stakeholder management skills.

Skills

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