AI Solutions Lead/AI (Associate) Architect

Job title: AI Solutions Lead/AI (Associate) Architect

About the Role We are seeking an AI Solutions Lead to architect, govern, and grow our AI delivery practice across GenAI, Agentic AI, and applied ML engagements. This is a hands-on, hybrid role that includes architecting AI solutions and shaping the growth of the AI practice. The role requires fluency with GenAI and agentic AI on top of a solid foundation in classical ML and DL, and the ability to set the technical direction for a growing team.

What You'll Do

• Translate business problems into staged, defensible AI solution roadmaps, working with business leaders through pre-sales and project delivery cycles.
• Lead solutioning and support architecture for end-to-end AI solutions across GenAI, Agentic AI, multimodal, and applied ML use cases, with explicit trade-offs on model class, retrieval design, memory, and orchestration.
• Own the practice's reference architectures and solution design patterns for multimodal agentic systems, including planning, tool use, memory, and inter-agent communication.
• Conduct solution design reviews across concurrent client engagements; facilitate subjective technical decisions and enable delivery excellence.
• Design and lead the build of multi-agent systems with reasoning, planning, tool use, persistent memory, and grounded retrieval.
• Guide multimodal system design across text, vision, speech, and structured data, including ingestion, representation, and downstream agent reasoning.
• Establish patterns for SLM design and adoption to meet enterprise constraints on cost, latency, data residency, and on-prem/edge deployment.
• Define hybrid retrieval and knowledge architectures spanning vector, graph, and NoSQL stores; lead knowledge graph-assisted retrieval, entity linking, and structured grounding.
• 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.
• Define and enforce safety, guardrails, and hallucination-control standards across the practice; lead red-teaming and adversarial testing for high-stakes deployments.
• Drive enterprise deployment best practices across cloud hyper-scalers, on-prem, and edge, including GPU/accelerator ops, model serving, and lifecycle automation.
• Practice Building & Technical Mentorship: shape the capability roadmap, hire and calibrate the team's evaluation standards, mentor AI Engineers and Lead AI Engineers, and promote AI in SDLC frameworks on delivery projects.
• Cross-functional Leadership & Delivery: partner with engineering, data science, product, and DX leadership on delivery and acceleration; engage client leadership and support pre-sales and solutioning.

What We're Looking For

• Technical fluency with GenAI and agentic AI on top of a strong foundation in classical ML and deep learning; ready to set technical direction for a growing team.
• Experience leading AI delivery architectures across GenAI, Agentic AI, multimodal, and enterprise deployments.
• Ability to translate business problems into AI roadmaps and to design, review, and govern reference architectures.
• Deep knowledge of SLM design, retrieval, memory, grounding, and inter-agent communication; experience with relevant patterns.
• Hands-on programming in Python (advanced), SQL, and API/backend engineering; production-grade software practices.
• Experience with LLMs/SLMs, multimodal architectures, tool use, and grounding; knowledge graphs, vector stores, and NoSQL.
• Experience with fine-tuning, distillation, quantization.
• Experience with evaluation frameworks, safety/guardrails, red-teaming, observability, and production-readiness for enterprise deployments.
• Strong cross-functional collaboration, client-facing communication, and ability to lead technical hiring and mentoring.

Nice to Have

• Preferably experience in multimodal agentic systems, SLM design, ML/DL, and enterprise deployments.
• Familiarity with on-prem/edge deployment patterns and regulated environments; knowledge graphs exposure is a plus.
• Experience with relevant AI tooling.