AI Infrastructure Architect

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 12 year(s) of experience is required
Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

- Experienced and senior AI/LLM Technology Architecture Engineer, responsible for designing and delivering end-to-end AI platform architectures on AWS.
- Own the technical architecture for modern AI systems spanning classical machine learning, generative AI, LLM applications, RAG, agentic workflows and enterprise AI platform integration.
- Act as the technical authority for one or more architecture domains such as agentic application design, AI security and trust, AI operations and observability, data and knowledge engineering, model platforms and inference.
- Bring practical industry experience in banking, insurance, healthcare, retail, telecom or capital markets to shape domain-grounded solutions, define controls, and ensure the AI architecture aligns with real business processes and enterprise standards.

Key Responsibilities
- Translate business strategy and product goals into a technical vision, architecture blueprint, non-functional requirements and implementation roadmap.
- Lead stakeholder workshops to align on feasibility, project scope, solution boundaries, delivery dependencies and client-facing expectations.
- For AWS, define AWS-native GenAI platform architecture evaluate build-versus-buy choices across Bedrock, SageMaker and open-source frameworks design RAG and agent patterns using S3, Knowledge Bases and OpenSearch establish guardrails, auditability, identity boundaries, token budgets and cost controls for production LLM workloads.
- Architect model and tool agnostic multi-agent systems, including orchestration, tool use, agent memory, context management, MCP/control-plane patterns and reusable service abstractions.
- Design the end-to-end data and context layer including ingestion, preprocessing, synchronization, chunking, embeddings, vector search, knowledge graphs and semantic retrieval for reliable RAG.
- Define evaluation frameworks for accuracy, relevance, faithfulness, groundedness, latency, cost, safety, security and operational reliability.
- Establish AI security, governance and observability as centrally enforced design controls including guardrails, prompt-injection defense, PII protection, access control, audit logging and OpenTelemetry-style tracing.
- Maintain architecture decision records, component diagrams, sequence diagrams, design specifications, integration patterns and reusable reference architecture assets.

Required Qualifications
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
- Minimum 10+ years of experience in software engineering, data engineering, AI/ML engineering or technology architecture.
- Minimum 5+ years of experience designing and deploying enterprise-grade advanced AI or cloud data solutions using at least one cloud vendor.
- Minimum 2+ years of experience in agentic AI, LLM and generative AI solution architecture or engineering delivery.
- Minimum 4+ years of coding experience using Python and experience with APIs, distributed systems, reusable frameworks and cloud-native application patterns.
- Minimum 4+ years of experience in ML, deep learning, NLP, data engineering, analytical engineering or AI product delivery.
- Demonstrated experience as a solution/technology architect in industry contexts such as banking, insurance, healthcare, retail, telecom or capital markets.

Required Skills/ Experience
- Hands-on architecture and engineering experience with Amazon Bedrock, Bedrock Agents/AgentCore, Knowledge Bases, Guardrails, Bedrock model evaluation, SageMaker, Lambda, API Gateway, Step Functions, EventBridge, OpenSearch Serverless/Vector Engine, S3, IAM, VPC, KMS, CloudWatch and CloudTrail.
- Strong knowledge of LLM architecture patterns including RAG, embeddings, vector databases, prompt engineering, model routing, fine-tuning/adaptation, function calling, tool integration and agent orchestration.
- Ability to define enterprise AI platform patterns for performance, scalability, security, reliability, observability, governance, cost optimization and operational support.
- Experience designing reusable agent services, memory services, API gateways, integration adapters, orchestration layers, evaluation harnesses and deployment pipelines.
- Experience with CI/CD, infrastructure-as-code, automated testing, model evaluation, MLOps/LLMOps, monitoring and production release governance.
- Strong stakeholder management skills with ability to communicate architecture trade-offs, risks and recommendations to engineering, product, security and leadership teams.

Good to Have Skills
- AWS Certified Solutions Architect Professional, Machine Learning Specialty or GenAI related certification experience with AWS CDK/Terraform, EKS, Bedrock AgentCore, Amazon Q, private connectivity and regulated workload architecture.
- Exposure to open-source AI and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, MLflow, FastAPI, Docker and Kubernetes.
- Experience with responsible AI, model risk management, AI governance boards, red-teaming, human-in-the-loop review, A/B testing and GenAI FinOps.
- Experience building reusable enterprise reference architectures, estimation models, accelerators, playbooks and architecture governance frameworks.