Back to Jobs 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 solid 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

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.

📌 Lead MLOps Engineer (Thiruvananthapuram)
🏢 InApp
📍 Thiruvananthapuram