AI Infrastructure Architect

Job Summary

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: AWS AI Services
• Good to have skills: Large Language Models (LLMs)
• Minimum Experience: 7.5 Year(s)
• Educational Qualification: 15 years full time education

About the Role

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 Level 9 technical lead or Level 8 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.

Requirements

Educational Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.

Experience Levels

• Level 9: typically 5+ years of software/data/AI engineering experience, including 2+ years in cloud-native engineering and 1+ year in GenAI, LLM, NLP or agentic AI delivery.
• Level 8: typically 7+ years of software/data/AI engineering experience, including 3+ years in cloud-native architecture/engineering and 1–2+ years in GenAI, LLM, NLP or agentic AI delivery.

Required Skills & Experience

• Hands-on coding experience in Python and strong understanding of APIs, distributed systems, CI/CD, testing, observability and secure SDLC practices.
• Experience delivering AI/ML or data products in at least one industry domain such as banking, insurance, healthcare, retail, telecom or capital markets.
• Hands-on experience with Amazon Bedrock, Bedrock Agents/AgentCore, Knowledge Bases, Guardrails, Lambda, API Gateway, Step Functions, OpenSearch Serverless/Vector Engine, SageMaker, IAM, CloudWatch, CloudTrail, VPC, KMS, S3.
• Strong understanding of LLM application architecture patterns including RAG, function/tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases and evaluation metrics.
• Ability to implement traditional ML and GenAI components across ingestion, feature/data preparation, model integration, deployment, monitoring and continuous improvement.
• Practical knowledge of security, privacy, governance, performance, scalability, reliability and cost controls for production AI systems.
• Experience with Git-based development, automated testing, CI/CD pipelines, infrastructure-as-code and agile delivery in client-facing environments.

Good to Have Skills

• AWS Solutions Architect or Machine Learning Specialty certification experience with CDK/Terraform, EKS, Bedrock model evaluation, Amazon Q, responsible AI controls and FinOps for GenAI workloads.
• Exposure to open-source frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, MLflow, FastAPI, Docker and Kubernetes.
• Experience with Responsible AI, model risk management, synthetic data generation, human-in-the-loop review, A/B testing and GenAI cost optimization.