AI Architect
Full TIme Role - No Visa Sponsorship
Role Summary
We are looking for a senior, hands-on AI Architect to design, build and govern production-grade Generative AI solutions for enterprise clients. You will combine deep data and application architecture expertise with practical LLM engineering: RAG, vector search, agentic patterns and multi-model strategy across Anthropic, OpenAI and Gemini. You will own solutions end to end, from target-state architecture and ARB approval through secure, governed delivery on cloud.
Key Responsibilities
• Define target-state AI, data and application architectures, reference patterns and reusable accelerators for enterprise GenAI programs.
• Design and build RAG and agentic systems: ingestion, chunking, embeddings, hybrid retrieval, re-ranking, grounding, evaluation and observability.
• Select and integrate LLMs (Anthropic Claude, OpenAI, Google Gemini) with sound model-routing, cost, latency, safety and fallback strategies.
• Stay hands-on: write and review code, build proofs of concept, set engineering standards and unblock delivery teams.
• Take architectures through Architecture Review Boards: prepare artifacts (ADRs, NFRs, logical and data architecture, integration flows), defend trade-offs and secure approvals on time.
• Design security and access: RBAC/ABAC, identity integration, API management (APIM), secrets, network isolation, data classification, PII/PHI handling and audit trails.
• Establish AI governance: prompt and model versioning, evaluation harnesses, guardrails, human-in-the-loop controls, lineage and responsible-AI practices.
• Partner with data engineering on pipelines, semantic layers, metadata and data quality that make enterprise data AI-ready.
• Advise client executives and engineering leaders, mentor architects and engineers, and contribute to pre-sales and solution shaping.
Required Qualifications
• Experience: 15+ years in software, data and solution architecture, including at least 3 years designing and delivering production GenAI/LLM solutions.
• Hands-on engineering: Strong Python (plus Java, TypeScript or similar), REST/GraphQL APIs and event-driven integration. Current in code, not only diagrams.
• RAG and vector search: Demonstrated delivery of RAG systems with vector databases (e.g., Pinecone, pgvector, Azure AI Search, Vertex AI Vector Search, OpenSearch, Weaviate or Milvus). Retrieval quality tuning and evaluation.
• LLM platforms: Hands-on with Anthropic, OpenAI and Gemini APIs, including tool/function calling, structured outputs, prompt engineering, context management and cost control.
• Cloud: Production experience on Google Cloud Platform (strongly preferred), Azure or AWS: compute and containers (Kubernetes), managed AI services (Vertex AI, Azure OpenAI/AI Foundry, Bedrock), storage, serverless and CI/CD.
• Data architecture: Data modelling, lakehouse/warehouse patterns, ingestion pipelines, metadata, lineage and data quality. Snowflake, BigQuery or Databricks experience.
• Application architecture: Microservices, API design, integration patterns, resilience and non-functional requirements.
• Security and governance: RBAC, identity (OAuth2/OIDC, Entra ID, IAM), APIM gateways, encryption and compliance (e.g., HIPAA, GDPR, SOC 2).
• Architecture governance: Proven track record presenting to and securing approval from ARBs, with clear ADRs and trade-off analysis.
• Communication: Clear written and verbal communication with executives and engineers. Experience leading without direct authority.
Preferred Qualifications
• Agentic frameworks and orchestration (LangGraph, LangChain, LlamaIndex, MCP, Claude Agent SDK, Google ADK).
• Knowledge graphs, ontology or GraphRAG (e.g., Neo4j).
• LLMOps: evaluation frameworks, observability (e.g., LangSmith, Arize, Langfuse), guardrails and red-teaming.
• Experience with AI-assisted engineering tools such as Claude Code for code generation and migration.
• Cloud or architecture certifications (Google Cloud Platform Professional Cloud Architect, Azure Solutions Architect, AWS Solutions Architect, TOGAF).
• Consulting or professional-services background, and experience building reusable accelerators.
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science or equivalent experience.
