Lead I - ML Engineering

Job Summary

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

• Bachelors or Masters 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.