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
18 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
- Senior Architect / Lead-Principal AI Architect for AI LLM Technology Architecture, serving as the accountable architecture authority for enterprise AI solutions on AWS.
- Own the complete, end-to-end architecture of advanced AI platforms and solutions spanning classical machine learning, generative AI, LLM applications, agentic systems, context engineering, model platforms, inference, AI operations and enterprise integration.
- Operate at executive level with CIOs, CTOs, senior business leaders and practice leadership to shape AI strategy, define transformation roadmaps and ensure investments are purposeful, sequenced and aligned to business outcomes.
- Bring strong industry experience in banking, insurance, healthcare, retail, telecom or capital markets to ensure the AI architecture addresses domain workflows, regulatory expectations, risk controls, data realities, adoption considerations and measurable value delivery.
- Lead multiple domain architects and senior SMEs across agentic application design, AI security and trust, AI operations and observability, data and knowledge engineering, model platforms and inference to create a cohesive enterprise-ready architecture.
Key Responsibilities
- Partner with client executives and business leaders to define the enterprise AI strategy, target-state architecture and investment roadmap across platforms, data, models, applications and operating model.
- Lead enterprise AI assessments, technology comparisons, platform selection, reference architecture definition, modernization opportunities and implementation sequencing for complex transformations.
- For AWS, set the AWS-native enterprise AI platform strategy define Bedrock-led foundation architecture for GenAI applications, federated agent systems, AI gateway patterns, governed RAG and secure model inference make build-versus-buy decisions across Bedrock, SageMaker and open-source frameworks establish multi-account, security, observability, resilience and GenAI FinOps standards.
- Own the complete end-to-end technical solution for complex AI platforms, ensuring every domain is designed cohesively against business objectives, enterprise standards and non-functional requirements.
- Translate governing architecture principles into a concrete, defensible technical solution that platform, data, AI/ML and application engineering teams can build against.
- Set architectural direction for model- and tool-agnostic multi-agent systems, including orchestration, memory, tool/skill use, agent registry, AI gateway/control plane, risk scoring and certification gates.
- Define the enterprise context layer architecture across knowledge graphs, ontologies, vector search, semantic retrieval, prompt/context assembly, conversational state and reusable memory services.
- Establish identity, authorization, layered guardrails, prompt-injection defense, PII protection, audit logging, lineage and defense-in-depth controls for AI agents, tools, data and models.
- Mandate productized evaluation and observability practices covering accuracy, relevance, groundedness, model quality, latency, cost, safety, reliability, production support and continuous improvement.
- Establish FinOps as a first-class AI concern, including usage labelling, token budgets, gateway-enforced budgets, cost-per-archetype planning, threshold alerts and optimization levers.
- Produce and steward authoritative architecture assets including enterprise AI blueprints, ADRs, sequence diagrams, solution patterns, interface specifications, reference architectures and governance playbooks.
Required Qualifications
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
- Minimum 15+ years of overall experience across software engineering, data engineering, AI/ML engineering, cloud architecture, enterprise architecture or technology leadership.
- Minimum 8+ years of experience designing and deploying enterprise-grade advanced AI, data, analytics or cloud-native solutions using at least one cloud vendor.
- Minimum 2+ years of experience in LLM and generative AI solution architecture, including agentic systems, RAG, prompt engineering, model integration and evaluation patterns.
- Minimum 2+ years of experience architecting and operationalizing LLM-driven application architecture patterns in enterprise-scale or production environments.
- Minimum 6+ years of experience in engineering, machine learning, deep learning, NLP solutions, data engineering or large-scale analytical engineering applications.
- Minimum 6+ years of experience as a machine learning / data / AI architect designing large-scale analytical engineering solutions in industry contexts such as banking, insurance, healthcare, retail, telecom or capital markets.
Required Skills/ Experience
- Deep architecture and hands-on engineering experience with Amazon Bedrock, Bedrock Agents/AgentCore, Knowledge Bases, Bedrock Guardrails, Bedrock model evaluation, SageMaker, Lambda, API Gateway, Step Functions, EventBridge, OpenSearch Serverless/Vector Engine, S3, IAM, VPC, KMS, CloudWatch, CloudTrail and AWS Well-Architected practices.
- Strong expertise in enterprise AI platform architecture covering RAG, embeddings, vector databases, semantic retrieval, context engineering, model routing, agent orchestration, memory, tool calling, AI gateways and model evaluation.
- Ability to set enterprise NFRs and architectural controls for performance, scalability, security, privacy, reliability, governance, observability, resiliency, cost optimization and operational readiness.
- Experience making definitive, evidence-based decisions on design patterns, reference architectures, frameworks, technology selections, foundation models and deployment approaches.
- Experience establishing agent registry and certification models, AI control planes, access models, guardrails, production evaluation stacks, model risk controls and cross-platform governance.
- Strong executive communication, architecture governance and thought leadership skills with ability to influence business, technology, security, product and delivery leadership teams.
Good to Have Skills
- AWS Certified Solutions Architect Professional, Machine Learning Specialty or Generative AI related certification experience with AWS CDK/Terraform, EKS, Bedrock AgentCore, Amazon Q, private connectivity, landing zones, regulated workloads and enterprise FinOps.
- 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, synthetic data, human-in-the-loop review, A/B testing and GenAI FinOps.
- Recognized thought leadership through enterprise reference architectures, internal capability building, client advisory, platform accelerators, publications, whitepapers, conference sessions or industry forums.
