AI Infrastructure Architect

Project Role : AI Infrastructure Architect

Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.

Must have skills : Large Language Models (LLMs)

Good to have skills : Amazon Web Services (AWS)

Minimum 7.5 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

Engineer role in AI LLM Technology Architecture. Hands-on engineering role focused on designing, building, integrating, testing and operationalizing enterprise-grade LLM, GenAI and agentic AI components across active client engagements.

Own platform-specific engineering on AWS, translating high-level architecture into working, production-quality components for LLM-driven applications, RAG pipelines, multi-agent workflows and AI platform integrations.

Bring practical industry experience in banking, insurance, healthcare, retail, telecom or capital markets to identify domain data, process constraints, controls and adoption risks while designing GenAI solutions that are safe, scalable and relevant.

Operate as a hands-on technical lead or engineering lead, contributing code, design decisions, reusable patterns and engineering documentation.

Key Responsibilities

Design and build LLM application components including prompts, tools, agents, orchestration flows, memory/context handling, retrieval pipelines and evaluation harnesses.

Design agent workflows using Bedrock and serverless AWS patterns integrate enterprise APIs through Lambda and API Gateway build secure RAG over S3, OpenSearch and Knowledge Bases tune prompts and evaluation test suites for accuracy, relevance, faithfulness and safety.

Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search and retrieval workflows for structured and unstructured enterprise content.

Engineer safety and control components including PII detection/redaction, prompt-injection defenses, content filters, guardrails, authentication, authorization, lineage and audit logging.

Collaborate with architects, data engineers, product owners and security stakeholders to convert solution designs into tested, observable and maintainable software components.

Maintain technical artifacts such as component designs, integration specifications, deployment runbooks, evaluation results and reusable engineering patterns.