AI Lead Architect

Job Description: Key Responsibilities • Solution Engineering & Technical Execution: o Lead the hands-on engineering for end-to-end AI solutions across Deep Learning, GenAI, Agentic AI, and multimodal use cases. o Apply rigorous "fail fast" logic to all AI project management. Quickly identify, evaluate, and disqualify unviable AI use cases based on technical feasibility, effort, cost, and risk early in the cycle. o Perform explicit trade-off analysis on model class (frontier vs. SLM vs. fine-tuned), retrieval design, memory optimization, and orchestration.  o Lead solutioning, support architecture for end-to-end AI solutions across GenAI, Agentic AI, multimodal, and applied ML use cases, with explicit trade-off analysis on model class (frontier vs. SLM vs. fine-tuned), retrieval design, memory, and orchestration. o Own the practice's reference architectures and solution design patterns for multimodal agentic systems, including planning, tool use, memory, grounding, and inter-agent communication (MCP, A2A). o Conduct solution design reviews across concurrent client engagements; facilitate subjective technical decisions and enable delivery excellence. • Multimodal Agentic Systems & SLM Design: o Design and lead the build of multi-agent systems with reasoning, planning, tool use, persistent memory, and grounded retrieval. o Lead multimodal system design and solutions across text, vision, speech, and structured data, including ingestion, representation, and downstream agent reasoning.  o Establish patterns for SLM design and adoption — distillation, fine-tuning, quantization, and routing — to meet enterprise constraints on cost, latency, data residency, and on-prem/edge deployment o Define hybrid retrieval and knowledge architectures spanning vector, graph (KG), and NoSQL stores; lead KG-assisted retrieval, entity linking, and structured grounding. • Eval, Guardrails & Production Quality: o Establish evaluation as a first-class discipline: design eval frameworks, golden datasets, regression suites, automated and human-in-the-loop evals, and observability for agentic and generative systems.  o Define and enforce safety, guardrail, and hallucination-control standards across the practice; lead red-teaming and adversarial testing for high-stakes deployments. o Set the bar for production readiness—reliability, latency, cost, monitoring, drift detection, and incident response—for AI systems in regulated, enterprise-grade environments. o Lead GPU/accelerator ops, model serving, and lifecycle automation for deployment across cloud hyper-scalers, on-prem, and edge. • Technical Leadership & Capability Pillars: o Act as a technical sentinel for the AI practice, mentoring engineers through rigorous code and architecture reviews to ensure permanent capability building rather than temporary crisis management. o Establish and enforce AI in SDLC frameworks on delivery projects. • Cross-functional Leadership & Delivery