Job Summary
We are looking for a hands-on AI/ML Engineer with strong experience in Python, Generative AI, Agentic AI, Machine Learning, and AWS to build and productionize enterprise AI solutions. The ideal candidate will have experience developing LLM/RAG applications, agentic workflows, ML pipelines, APIs, and cloud-native AI services.
Key Responsibilities
• Design, develop, test, deploy, and support end-to-end AI/ML solutions using Python and AWS.
• Build production-grade Python APIs, services, workers, data pipelines, and AI/LLM integrations using FastAPI, Pydantic, boto3, SQLAlchemy, pandas, NumPy, and scikit-learn.
• Develop RAG pipelines including document ingestion, parsing, chunking, metadata, embeddings, vector/hybrid search, reranking, retrieval, grounding, citations, and access-aware filtering.
• Build Agentic AI workflows with tools, state, memory, structured outputs, human-in-the-loop approvals, retries, error handling, and observability.
• Work with LangChain, LangGraph, CrewAI, AWS Strands Agents, or equivalent agent frameworks.
• Develop Generative AI solutions using Amazon Bedrock, foundation models, Knowledge Bases, Agents/AgentCore, and Guardrails.
• Implement classical ML solutions using scikit-learn, PyTorch, or TensorFlow, including training, evaluation, inference, monitoring, and model lifecycle management.
• Develop AI/ML evaluation frameworks covering golden datasets, prompt testing, retrieval evaluation, LLM evaluation, regression testing, and human review.
• Implement secure and scalable AWS solutions using SageMaker, Lambda, Step Functions, S3, OpenSearch, API Gateway, ECS/Fargate, EKS, EventBridge, SQS/SNS, RDS/Aurora, DynamoDB, and Glue.
• Implement IAM, KMS, Secrets Manager, VPC, encryption, CloudWatch, CloudTrail, logging, monitoring, and security controls.
• Support CI/CD, MLOps, Infrastructure as Code, and automated deployment using Terraform, AWS CDK/CloudFormation, GitHub Actions, CodeBuild/CodePipeline, and ECR.
• Develop unit, integration, contract, security, performance, and end-to-end tests using pytest.
• Optimize AI applications for latency, throughput, token consumption, cost, reliability, and scalability.
• Collaborate with architects, data engineers, QA, DevOps, security, and HCLS teams.
• Mentor other engineers and contribute to engineering best practices.
Required Qualifications
• Strong hands-on experience with Python.
• Experience developing FastAPI and REST APIs.
• Strong experience with Generative AI and LLMs.
• Experience with prompt and context engineering.
• Hands-on experience with RAG, embeddings, vector search, and hybrid search.
• Experience developing Agentic AI and AI agent solutions.
• Experience with LangChain, LangGraph, CrewAI, AWS Strands, or equivalent agent frameworks.
• Experience with Amazon Bedrock, including Knowledge Bases, Agents, and Guardrails.
• Experience with Amazon SageMaker.
• Hands-on experience with AWS services including Lambda, Step Functions, S3, OpenSearch, and API Gateway.
• Strong SQL skills.
• Experience with pytest and automated testing.
• Experience with Docker and Kubernetes.
• Experience with Terraform, AWS CDK, or CloudFormation.
• Experience with CI/CD and MLOps practices.
• Knowledge of IAM, KMS, Secrets Manager, and CloudWatch.
• Strong understanding of Machine Learning fundamentals.
• Experience with LLM/AI evaluation and observability.
Preferred Qualifications
• Experience in Healthcare and Life Sciences.
• Experience with HIPAA, PHI, or PII security requirements.
• Experience with AWS AgentCore.
• Experience with PyTorch or TensorFlow.
• Experience with PostgreSQL or DynamoDB.
• Experience with event-driven architecture.
• Knowledge of Responsible AI and AI security.
• Experience developing human-in-the-loop AI systems.
