AI Architect– Data Engineering / GenAI

AIFA Solutions is seeking a senior, hands-on AI Architect to design, build and govern production-grade Generative AI solutions for enterprise clients. The role owns end-to-end architecture and delivery, including RAG and agentic systems, model strategy, security, AI governance and collaboration with data engineering teams.

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

Requirements:

• 11 to 16 years in software, data and solution architecture, including at least 3 years designing and delivering production GenAI/LLM solutions
• Strong Python (plus Java, TypeScript or similar), REST/GraphQL APIs and event-driven integration. Current in code, not only diagrams
• 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
• Hands-on with Anthropic, OpenAI and Gemini APIs, including tool/function calling, structured outputs, prompt engineering, context management and cost control
• Production experience on GCP (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 modelling, lakehouse/warehouse patterns, ingestion pipelines, metadata, lineage and data quality. Snowflake, BigQuery or Databricks experience
• Microservices, API design, integration patterns, resilience and non-functional requirements
• RBAC, identity (OAuth2/OIDC, Entra ID, IAM), APIM gateways, encryption and compliance (e.g., HIPAA, GDPR, SOC 2)
• Proven track record presenting to and securing approval from ARBs, with clear ADRs and trade-off analysis
• Clear written and verbal communication with executives and engineers. Experience leading without direct authority
• GCP
• 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 (GCP 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
• Azure AI, GenAI Architect, Architect, Llm guardrails, Deep Learning, ChatGPT, OpenAI, Data Engineer, Python, Microsoft Copilot, Machine Learning