Lead AI Engineer

Role

Lead AI Engineer

Mandatory Skills

Python, Multi-LLM system, RAG, AWS Lambda, Amazon API Gateway, Claude models, Bedrock-hosted models (AWS Bedrock, AWS Bedrock Knowledgebase)

Primary Skills

Python, Multi-LLM system, RAG, AWS Lambda, Claude models, Bedrock-hosted models (AWS Bedrock, AWS Bedrock Knowledgebase)

Good to Have

AWS

Total Experience

8+ years

Relevant Experience

7+ years

Work Location

Kochi / TVM / Remote

Qualification

Bachelor's Degree

Job Purpose

We are seeking a hands-on Lead AI Engineer to build, integrate, and operationalize Agentic AI capabilities, predominantly with Gen AI. This is a core engineering role responsible for day-to-day implementation of AI features using AWS-native tooling.

Job Description / Duties and Responsibilities

AI Engineering

Build agentic solutions with conversational agents at the front end and agentic automation for data retrieval at the backend.

Build Text-to-SQL AI capability.

Build graph databases from SQL as well as unstructured data.

Build and optimize RAG pipelines and Graph RAG capabilities.

Evaluate LLMs (primarily AWS Bedrock-based, and additionally other cloud and on-prem LLMs) for performance, scalability, and cost.

Knowledge of GPU-based LLM installation and management is a plus.

Knowledge of MLOps and methodologies for model and agentic deployment is required.

Backend Development

Develop scalable services using:

AWS OpenSearch Service

AWS S3

Amazon DynamoDB

AWS CloudWatch

Deploy via:

AWS Lambda

Amazon API Gateway

Amazon CloudFront

Model Optimization

Prompt engineering

Retrieval tuning

Latency optimization

Output quality improvement

Adhere to the Information Security Management policies and procedures.

Job Specification / Skills and Competencies

Required Skills

Hands-on experience in GenAI application development.

RAG implementation.

Graph implementation.

LLM integration.

Strong AWS serverless experience.

Experience working with Bedrock-based architectures.

Ability to work independently in embedded teams.

Experience leading a small team of 2 to 4 members.

Customer management and requirements analysis.

Ability to solve AI business problems.

Nice to Have

Experience with healthcare or sensitive data systems.

Exposure to AI evaluation frameworks.

Multi-model orchestration experience.

Bachelor's or Master's degree in Computer Science, or a related field.

Strong coding skills in Python and ML libraries such as TensorFlow, PyTorch, or scikit-learn.

Experience with data preprocessing, feature engineering, and model lifecycle management.

Proficiency in ML pipelines and deployment using MLflow, Docker, Kubernetes, or similar.

Familiarity with AWS cloud platform and related AI/ML/LLM services.

Strong understanding of statistics, probability, and data modeling.