Lead AI Engineer

Job Purpose

We are seeking a hands-on Lead AI Engineer to build, integrate, and operationalize Agenitc AI capabilities, pre dominantly 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

AI Engineering

-- Build agentic solution with conversational agents at front end & agentic automation for data retrieval at the backend. -- Build Text to SQL AI capability -- Build Graph Database from SQL as well as unstructured data -- Build and optimize RAG pipelines, Graph RAG capabilities etc -- Evaluate LLMs (primarily AWS Bedrock based, additionally other Cloud and on-prem LLMs) for performance, scaling & cost perspectives -- Knowledge on GPU based LLM installation/management is a plus -- Knowledge of MLOps and methodologies of model/agentic deployment is requisite

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

Job Specification / Skills

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 -- Leading a small of 2 4 members. -- Customer Management, Requirements analysis -- Solution of AI business problems

Nice to have

-- Experience with healthcare or sensitive data systems -- Exposure to AI evaluation frameworks -- Multi-model orchestration experience -- Bachelors or Masters 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 cloud platform AWS and related AI/ML, LLM services. -- Strong understanding of statistics, probability, and data modeling.

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