Lead MLOps Engineer

Experience

6-10 years Project Location(s) : Trivandrum / Kochi / Remote

Work mode

Hybrid/ Remote ( Quarterly 5 days in office for Remote Employees)

Number of Openings

1

Job Description

We are looking for a Lead MLOps Engineer with strong experience in building, deploying, and managing scalable machine learning platforms and production-grade ML/AI solutions.

The role will be responsible for operationalizing the end-to-end ML lifecycle, automating ML workflows, establishing robust deployment and monitoring practices, and ensuring security, governance, and reliability across ML platforms.

The ideal candidate will have strong hands-on experience with Python, MLflow or Kubeflow, AWS SageMaker, AWS Bedrock, ECS, Docker, CI/CD, and Infrastructure as Code (IaC).

Key Responsibilities

• -----------------------
• Design, build, deploy, and manage end-to-end ML lifecycle pipelines.
• Automate model training, testing, validation, deployment, and monitoring workflows.
• Implement experiment tracking, model versioning, model registry, and lifecycle management solutions.
• Develop and maintain scalable MLOps platforms and ML deployment infrastructure.
• Monitor model performance, data/model drift, system health, and operational metrics in production.
• Implement observability and alerting mechanisms for ML workloads.
• Collaborate closely with Data Engineering, Data Science, DevOps, and application teams to operationalize ML/AI solutions.
• Establish and maintain security, governance, access control, auditability, and compliance processes for ML platforms.
• Implement CI/CD and DevSecOps practices for ML pipelines and infrastructure.
• Optimize ML infrastructure and deployment workflows for performance, scalability, reliability, and cost efficiency.
• Troubleshoot production ML pipelines, deployments, infrastructure, and model-serving issues.
• Contribute to the adoption of modern MLOps, LLMOps, and Generative AI practices.

Required SkillsProgramming \& ML Lifecycle

• Strong proficiency in Python.
• 4+ years of hands-on experience in MLOps / ML lifecycle management.
• Experience taking ML/AI solutions from Proof of Concept (PoC) to Production.
• Strong understanding of model training, deployment, versioning, monitoring, and lifecycle management.

MLOps \& ML Platforms Hands-on experience with

• MLflow or Kubeflow
• Amazon SageMaker
• AWS Bedrock
• Amazon ECS (Elastic Container Service)

Cloud \& Infrastructure

• Strong hands-on experience with AWS.
• Good knowledge of Docker and Kubernetes.
• Experience with Infrastructure as Code (IaC) tools such as
• Terraform
• AWS CloudFormation
• or equivalent tools.

DevOps \& Security

• Experience with CI/CD pipelines and automation.
• Understanding of DevSecOps practices.
• Knowledge of cloud security, access control, secrets management, and auditability.
• Experience implementing production-grade deployment and operational practices.

Monitoring \& Observability

• Understanding of model monitoring and observability.
• Experience monitoring
• Model performance
• Data/model drift
• Pipeline health
• Infrastructure health
• Production failures
• Knowledge of performance optimization and reliability engineering for ML systems.

Preferred Skills

• Hands-on experience with AWS ML/AI services and cloud-native architectures.
• Knowledge or hands-on experience with Amazon Bedrock AgentCore (AgentCore).
• Exposure to data engineering technologies such as
• Databricks
• Apache Spark
• Apache Airflow
• Apache Kafka
• Snowflake
• Understanding of Responsible AI, model governance, risk management, and compliance requirements.
• Exposure to Generative AI, LLMOps, and RAG-based solutions.
• Experience with production deployment and monitoring of LLM/GenAI applications.

Key Competencies

• MLOps \& ML Lifecycle Management
• Python
• MLflow or Kubeflow
• AWS SageMaker
• AWS Bedrock
• ECS
• Docker
• CI/CD \& DevSecOps
• Infrastructure as Code
• ML Monitoring \& Observability
• Model Governance \& Security
• Generative AI / LLMOps
• Production ML Systems