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

UST; Bengaluru; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

UST is seeking a Lead MLOps Engineer to build and manage end-to-end ML lifecycle pipelines in Bengaluru, India. You will automate training, testing, deployment, and monitoring, ensuring model versioning and governance across production environments.

The role requires strong Python skills, hands-on MLflow/Kubeflow/SageMaker expertise, knowledge of Docker/Kubernetes, and experience with IaC and CI/CD/DevSecOps practices. Collaboration with Data Engineers and DevOps teams is essential.

Qualifications

• Strong Python programming skills.
• Experience with ML lifecycle management and deployment automation.
• Hands-on expertise with MLflow, Kubeflow, SageMaker, AWS Step Functions and ECS.
• 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.

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.

Skills

Python

ML lifecycle management

Deployment automation

CI/CD

Docker

Kubernetes

MLflow

Kubeflow

SageMaker

AWS

Observability

Governance

DevSecOps

Education

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field

Tools

MLflow

Kubeflow

SageMaker

AWS Step Functions

ECS

Docker

Kubernetes

CI/CD tooling

Terraform

CloudFormation

Databricks

Airflow

Job description

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


• 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.

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