This role is responsible for designing, deploying, and operating scalable AI/ML and Generative AI platforms on AWS. The engineer will enable robust MLOps, LLMOps, and DevOps practices,

supporting production-grade AI solutions (including LLMs and agent-based systems) across lending,

risk, and customer journeys.

Key Responsibilities

- AI/ML & GenAI Operations (AI Ops)

 Operate and support production ML and GenAI workloads on AWS

 Monitor:

o Model performance, drift, and reliability o LLM outputs (hallucination, latency, response quality)

 Ensure high availability of AI services powering lending workflows

- MLOps & LLMOps (AWS Native)

 Build and manage end-to-end ML pipelines using:

o Amazon SageMaker (training, deployment, pipelines)

o SageMaker Model Registry for versioning

 Implement:

o CI/CD pipelines for ML and GenAI workloads o Experiment tracking and reproducibility

 Enable LLMOps practices:

o Prompt lifecycle management o Evaluation frameworks for GenAI outputs

- DevOps & Cloud Engineering (AWS)

 Design and manage infrastructure using:

o Terraform / AWS CloudFormation

 Implement CI/CD pipelines using:

o AWS CodePipeline, CodeBuild, GitHub Actions

 Orchestrate workloads with:

o EKS (Kubernetes) and Docker

 Ensure scalability, resilience, and cost optimisation

- GenAI & Agentic AI Enablement

 Deploy and manage:

o Amazon Bedrock (LLMs, foundation models)

o RAG pipelines using vector databases (OpenSearch, Pinecone, etc.)

 Enable runtime support for:

o AI agents and multi-agent workflows

 Integrate AI systems with:

o APIs, event-driven services, and enterprise platforms

- Data & Pipeline Integration





 Build pipelines using:

o AWS Glue, Lambda, Step Functions

 Manage data storage and access via:

o S3, Redshift, DynamoDB

 Enable real-time and batch AI workflows

- Monitoring, Observability & Reliability

 Implement monitoring using:

o CloudWatch, Prometheus, Grafana

 Track:

o Model metrics, pipeline performance, system health

 Define SLAs/SLOs and manage incident response

- Security, Risk & Compliance

 Ensure secure AI deployments using:

o IAM, KMS, Secrets Manager

 Implement data governance and privacy controls

 Enforce Responsible AI and model governance standards

- Collaboration & Enablement

 Work with:

o AI Architects, Data Scientists, Platform Engineers

 Enable teams with:

o Reusable MLOps templates and frameworks o Self-service AI deployment capabilities

Key Skills and Experience

AWS AI/ML & Cloud Stack

 Robust experience with:

o Amazon SageMaker (end-to-end ML lifecycle)

o Amazon Bedrock (GenAI / LLMs)

 Familiarity with:

o OpenSearch, S3, Lambda, API Gateway

MLOps / LLMOps

 Experience implementing:

o ML pipelines, model registries, CI/CD for ML

 Knowledge of:

o Prompt engineering workflows o GenAI evaluation techniques

DevOps & Platform Engineering

 Hands-on experience with:

o Docker, Kubernetes (EKS)

o Terraform / CloudFormation

 CI/CD:

o CodePipeline, Jenkins, GitHub Actions

Programming

 Python (primary), Bash scripting

 Experience building APIs (FastAPI preferred)

Monitoring & Reliability

 Experience with:

o CloudWatch, ELK stack, Prometheus

 Understanding of:

o AI system observability and logging

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📌 AI/ML [Sagemaker with AWS] - 5+yrs (Bengaluru)
🏢 Nasugroup.com
📍 Bengaluru