AI Lead Architect

Job Description:

Role Overview

• We are seeking an AI Tech Lead to engineer, scale, govern, and grow our AI delivery practice across GenAI, Agentic AI, and applied ML, within enterprise engagements. This is a strictly hands-on, individual contributor and technical leadership role. You will act as a core technical sentinel, helping grow frontier AI capabilities and engineering pillars. You will be expected to come with expertise that help you actively contribute towards directing technical capability growth for the AI practice. This role requires a builder’s mindset. You must possess deep technical fluency in Deep Learning architectures and agentic AI, built upon a rock-solid foundation of software engineering. You will lead technical execution, driving solution architectures, evaluation standards, and the technical bar for the AI engineering practice. The expectation is technical depth and breadth showcased across the portfolio of AI work done so far, preferably in multimodal agentic systems, SLM design, ML and DL solutions, and enterprise deployments, and enterprise platform deployments.

Key Responsibilities

Solution Engineering & Technical Execution:

• 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 analysison model class (frontier vs. SLM vs. fine-tuned), retrieval design, memory, andorchestration.o Own the practice's reference architectures and solution design patterns formultimodal agentic systems, including planning, tool use, memory, grounding, andinter-agent communication (MCP, A2A).o Conduct solution design reviews across concurrent client engagements; facilitatesubjective technical decisions and enable delivery excellence.

Multimodal Agentic Systems & SLM Design:

• Design and lead the build of multi-agent systems with reasoning, planning, tooluse, persistent memory, and grounded retrieval.o Lead multimodal system design and solutions across text, vision, speech, and structured data, including ingestion, representation, and downstream agentreasoning. o Establish patterns for SLM design and adoption — distillation, fine-tuning, quantization, and routing — to meet enterprise constraints on cost, latency, dataresidency, and on-prem/edge deploymento Define hybrid retrieval and knowledge architectures spanning vector, graph (KG),and NoSQL stores; lead KG-assisted retrieval, entity linking, and structuredgrounding

Eval, Guardrails & Production Quality:

• Establish evaluation as a first-class discipline: design eval frameworks, goldendatasets, regression suites, automated and human-in-the-loop evals, andobservability for agentic and generative systems. o Define and enforce safety, guardrail, and hallucination-control standards acrossthe practice; lead red-teaming and adversarial testing for high-stakesdeployments.o Set the bar for production readiness—reliability, latency, cost, monitoring, driftdetection, and incident response—for AI systems in regulated, enterprise-gradeenvironments.o Lead GPU/accelerator ops, model serving, and lifecycle automation fordeployment across cloud hyper-scalers, on-prem, and edge

Technical Leadership & Capability Pillars:

• Act as a technical sentinel for the AI practice, mentoring engineers throughrigorous code and architecture reviews to ensure permanent capability buildingrather than temporary crisis management.o Establish and enforce AI in SDLC frameworks on delivery projects

Cross-functional Leadership & Delivery

• Engage with client and stakeholder leadership on architecture, feasibility, and risk;communicate technical direction clearly to non-technical audiences.o Support pre-sales and solutioning for new GenAI and Agentic AI opportunities,including effort estimation, architectural framing, and capability storytelling.

Must Have :

• Deep practical grounding in neural networks,Transformers, predictive modeling, embeddings, vector search; CV, NLP, and timeseries exposure.
• Generative AI: LLMs and SLMs, RAG/Agentic RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs, fine-tuning (SFT,LoRA/QLoRA, RLHF/RLAIF), distillation, and quantization.
• Agentic AI: Multi-agent orchestration, planning, tool use, persistent memory, MCP,and A2A patterns; frameworks such as LangGraph, LlamaIndex, AutoGen.
• Programming & Engineering: Python (advanced), SQL; strong API and backend engineering in FastAPI/Flask/Django; production-grade software practices
• Cloud & Data Engineering: Kafka, Spark/Flink, Hadoop, MongoDB, and otherNoSQL/graph/vector stores. Deep experience with AWS, Azure, or GCP
• Math Foundations: Linear algebra, probability, statistics, optimization. Experience Requirements

• Minimum 8+ years of total hands-on software development and engineering experience, with a proven track record of reliably deploying solutions on enterprise platforms.
• 3+ years of deep, hands-on experience building and deploying Deep Learning and AI systems in production (within the total 8 years of experience)
• Demonstrable hands-on work in GenAI and/or Agentic AI—beyond basic APIwrappers and simple RAG—including multi-agent systems, custom fine-tuning, orSLM-based deployment.

Good to have:

• Experience with commerce cloud ecosystems (Salesforce andAdobe).

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