Senior AI Architect

Responsibilities

• Own end-to-end architecture for enterprise AI systems, spanning data, model, retrieval/orchestration, serving, and integration, designed for scale, performance, security, cost, and reliability.
• Architect across the full spectrum: traditional ML and deep learning (statistical modeling, forecasting, NLP, computer vision, full model lifecycle) and GenAI/agentic systems (LLMs, RAG, multi-agent orchestration, fine-tuning), choosing the right technique for each problem.
• Design and build complete frameworks for AI-driven SDLC - spec-driven development, code generation, migration/modernization, test generation, review, and documentation - with the orchestration, guardrails, and human-in-the-loop controls that make agentic delivery enterprise-safe.
• Define MLOps/LLMOps practice: CI/CD for models and prompts, registries and versioning, drift and quality monitoring, evaluation, and scalable, cost-optimized inference.
• Embed responsible-AI, security, data-governance, and model-risk considerations into architectures from the start.
• Partner with sales and client executives to shape solutions, scope proposals and RFP responses, and present architecture to engineering and executive audiences; provide design direction and mentorship to distributed delivery teams.

Required qualifications

• Experience: 12+ years in software/data engineering or solution architecture, including 5+ in AI/ML and 2+ hands-on with production GenAI/LLM systems; at least three enterprise AI systems delivered to production.
• Traditional AI/ML: statistics and ML fundamentals; classical algorithms and deep learning (transformers, CNNs/RNNs); feature engineering; model training, evaluation, deployment, and monitoring.
• Generative AI/LLM: LLMs (GPT, Claude, Gemini, Llama, Mistral); RAG and retrieval design; embeddings and vector databases (e.g., pgvector, Pinecone); prompt and context engineering; fine-tuning (LoRA/QLoRA/PEFT); evaluation and guardrails.
• Agentic systems: multi-agent orchestration, tool use, memory/state; frameworks such as LangChain/LangGraph, LlamaIndex, or equivalents; and MCP.
• AI-driven SDLC: hands-on applying AI across the lifecycle and building frameworks/accelerators - not just using tools.
• Cloud & platforms: strong on at least one of AWS (Bedrock, SageMaker), Azure (Azure OpenAI, Azure ML), GCP (Vertex AI); cloud-native, distributed, event-driven design.
• Engineering: expert Python; API and microservices design; Docker/Kubernetes; CI/CD; SQL/NoSQL and graph/knowledge graphs; strong design-patterns and system-design grounding.
• Non-functional rigor: scalability, performance, security, observability, and cost at enterprise scale.
• Communication: credible with CIOs and engineers alike; experience mentoring and setting technical standards.

Preferred qualifications

• Regulated or high-complexity enterprise experience (financial services, insurance, retail, manufacturing).
• AI-based legacy modernization and code transformation.
• AI governance / model-risk frameworks in practice (e.g., SR 11-7, NIST AI RMF, ISO/IEC 42001).
• Building frameworks, platforms, or accelerators adopted across multiple teams or clients.
• Consulting or systems-integration background.