What are the responsibilities and job description for the GEN AI Engineer position at Hadiamondstar Software Solutions LLC?
We are seeking a high-caliber Principal AI Engineer for a contract engagement to accelerate the implementation of Agentic AI solutions. This is a ""builder"" role requiring a rare blend of deep Generative AI expertise, full-stack Python mastery, and AWS cloud
architecture. You will be responsible for moving AI from experimental prototypes to production-grade autonomous agents.
Experience Requirements:
Total Experience: 10 years in Software Engineering.
Python Mastery: 7 years of professional backend development.
AI/GenAI: 2 years of hands-on implementation with LLMs (Claude, GPT,
Llama).
AWS: 5 years architecting cloud-native applications.
Key Deliverables
Agentic Workflows: Design and deploy multi-agent systems using LLM orchestration frameworks (e.g., LangGraph, CrewAI) to automate complex cross-functional business processes, targeting measurable efficiency gains.
Production RAG: Build and optimize high-performance Retrieval-Augmented Generation pipelines using Amazon Bedrock and vector databases (e.g., OpenSearch, Pinecone), with clear latency and accuracy targets.
AI Integration: Develop robust FastAPI backends that make model outputs actionable for end-users. Collaborate with frontend engineers on React-based AI interfaces and streaming UI components.
Responsible AI & Guardrails: Implement prompt safety mechanisms, output filtering, bias evaluation, and content moderation to ensure production LLM systems meet security and compliance standards.
Engineering Excellence: Implement automated AI evaluation frameworks (e.g., Ragas, DeepEval), observability tooling (e.g., LangSmith, OpenTelemetry), and CI/CD pipelines for LLM prompts and code.
Technical Stack
AI: Claude, GPT-series, Hugging Face Transformers, PEFT, and LLM orchestration frameworks (e.g., LangChain, LangGraph).
Agents: Experience with autonomous tool-use, function calling, and state management.
Backend: Python 3.10 , Pydantic, FastAPI, and asynchronous programming.
Cloud: Amazon Bedrock, SageMaker, Lambda (Serverless AI), and RDS/pgvector.
Observability: Experience with AI-specific monitoring, tracing, and evaluation tooling.