Senior AI Engineer – Generative AI and AWS

LetzBizz; Chennai; Hybrid; INR 4,500,000 - 6,000,000; Full time

Job summary

LetzBizz is seeking an experienced AI/ML engineer to develop and deploy AI applications on AWS, blending generative AI, autonomous agents and ML. You will move solutions from experimentation to production, ensuring reliability, evaluation, security and ongoing operation.

Hybrid or Remote work with Chennai/Kerala/Tamil Nadu locations. The role requires 6–8 years of AI/ML or software engineering experience, strong Python, and hands-on Bedrock and GenAI/RAG expertise.

Qualifications

• 6–8 years of experience in AI/ML, data science or software engineering.
• Strong Python programming and production engineering skills.
• Hands-on experience with Bedrock, including foundation models, Guardrails, evaluation and agent/tool-calling implementations.
• Strong understanding of GenAI and RAG, embeddings, vector databases, retrieval strategies, reranking and prompt engineering.
• Experience with agent orchestration, planning, memory, tool invocation and human approvals.

Responsibilities

• Develop AI agents capable of planning tasks, using tools, maintaining context and coordinating workflows.
• Build retrieval-augmented generation (RAG) solutions covering document ingestion, chunking, embeddings, metadata, vector and hybrid search, reranking and grounded responses.
• Connect AI applications to enterprise APIs, data platforms and AWS services through secure, permission-controlled integrations.
• Implement prompts, structured responses, conversation state, routing, fallbacks and error recovery.
• Establish automated evaluations for response accuracy, groundedness, retrieval relevance, safety, task completion, latency and cost.
• Apply guardrails, personal-data protection, audit logging and human review controls.
• Develop conventional ML workflows including feature engineering, training, tuning, deployment, monitoring and retraining.
• Implement testing, CI/CD, infrastructure automation, model and prompt versioning, observability, alerting and operational documentation.
• Work with architects, data engineers, application teams, domain specialists and client stakeholders to define and deliver testable capabilities.
• Support demonstrations, user acceptance testing, production releases, troubleshooting and knowledge transfer.

Skills

Python programming

AWS Bedrock

GenAI & RAG

Agent orchestration

CI/CD & testing

US stakeholder collaboration

Knowledge of AWS services

Tools

Amazon Bedrock

OpenSearch

S3

Git

CI/CD tools

Terraform / AWS CDK

LangChain

Strands Agents

Job description

About The Role

We are looking for an experienced engineer to develop and deploy AI applications on AWS, combining generative AI, autonomous agents and machine learning. You will take solutions from experimentation through production, with responsibility for reliability, evaluation, security and ongoing operation.

6–8 Years

Hybrid / Remote

Chennai / Kerala / Tamil Nadu

Hiring Partner: LetzBizz

Client Location: Poland

Location: Chennai / Kerala / Tamil Nadu

Work Mode: Hybrid or Remote

Role: Hands‑on engineering with client interaction

Preferred domain: Life Sciences or Healthcare

Key Responsibilities

• Develop AI agents capable of planning tasks, using tools, maintaining context and coordinating workflows, including human approvals where required.
• Build retrieval‑augmented generation (RAG) solutions covering document ingestion, chunking, embeddings, metadata, vector and hybrid search, reranking and grounded responses.
• Connect AI applications to enterprise APIs, data platforms and AWS services through secure, permission‑controlled integrations.
• Implement prompts, structured responses, conversation state, routing, fallbacks and error recovery.
• Establish automated evaluations for response accuracy, groundedness, retrieval relevance, safety, task completion, latency and cost.
• Apply guardrails, personal‑data protection, audit logging and human review controls.
• Develop conventional ML workflows where needed, including feature engineering, training, tuning, deployment, monitoring and retraining.
• Implement testing, CI/CD, infrastructure automation, model and prompt versioning, observability, alerting and operational documentation.
• Work with architects, data engineers, application teams, domain specialists and client stakeholders to define and deliver testable capabilities.
• Support demonstrations, user acceptance testing, production releases, troubleshooting, performance improvements and knowledge transfer.

Required Skills And Experience

• 6–8 years of experience in AI/ML, data science or software engineering, with strong Python programming and production engineering skills.
• Hands‑on experience with Amazon Bedrock, including foundation models, Knowledge Bases, Guardrails, model evaluation and agent or tool‑calling implementations.
• Strong practical understanding of GenAI and RAG, including embeddings, vector databases, retrieval strategies, reranking and prompt engineering.
• Experience with agent orchestration, planning, memory, tool invocation, human approval workflows and multi‑agent coordination.
• Working knowledge of supervised learning, feature engineering, model evaluation, deployment, drift detection and monitoring.
• Experience integrating solutions using AWS Lambda, Step Functions, OpenSearch, S3 and APIs, with secure access to enterprise data.
• Hands‑on experience with Git, automated testing, CI/CD, containers and infrastructure or configuration automation.
• Ability to work directly with US-based business and technical stakeholders, explaining solution behaviour, evaluation results, limitations and engineering trade-offs.

Preferred Experience

• Amazon SageMaker for developing and operating ML pipelines.
• Amazon Bedrock AgentCore, Strands Agents, LangGraph, LangChain or comparable agent frameworks.
• OpenSearch vector search or knowledge graphs.
• AI delivery in Life Sciences or Healthcare, particularly research, clinical operations, medical affairs, commercial operations, manufacturing or pharmacovigilance.
• Familiarity with regulated environments and GxP requirements.
• Responsible AI, red teaming, synthetic evaluation data, LLM observability, fine tuning or multimodal applications.
• Terraform or AWS CDK.
• Relevant AWS certifications in AI or machine learning.

What Success Looks Like

Delivering secure, reliable AI applications with measurable quality, effective agent execution, monitored performance and cost, reusable components, and a successful transition from proof of concept to production.