Key Responsibilities

Translate business problems from clients into staged, defensible AI solution roadmaps working with business leaders through pre-sales and project delivery cycles.
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.
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).
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 — distillation, fine-tuning, quantization, and routing — to meet enterprise constraints on cost, latency, data residency, and on-prem/edge deployment.
Define hybrid retrieval and knowledge architectures spanning vector, graph (KG), and NoSQL stores; lead KG-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, guardrail, and hallucination-control standards across the practice; lead red-teaming and adversarial testing for high-stakes deployments.
Set the bar for production readiness — reliability, latency, cost, monitoring, drift detection, and incident response — for AI systems in regulated, enterprise-grade environments.
Drive enterprise deployment best practices across cloud hyper-scalers, on-prem, and edge, including GPU/accelerator ops, model serving, and lifecycle automation.

Shape the practice’s capability roadmap: which techniques to invest in, which to retire, and how the team stays at the leading edge of GenAI and agentic AI.
Mentor AI Engineers and Lead AI Engineers; run technical reviews, pairing sessions, and internal knowledge exchange on agentic, multimodal, and SLM topics.
Set the technical hiring bar; lead architecture and senior engineering interviews and calibrate the team’s evaluation standards.
Establish and promote AI in SDLC frameworks on delivery projects

Partner with engineering, data science, product, and DX leadership on delivery and acceleration initiatives
Engage with client and stakeholder leadership on architecture, feasibility, and risk; communicate technical direction clearly to non-technical audiences.
Support pre-sales and solutioning for new GenAI and Agentic AI opportunities, including effort estimation, architectural framing, and capability storytelling.

Required Technical Skills –

Programming & Engineering: Python (advanced), SQL; strong API and backend engineering in FastAPI/Flask/Django; production-grade software practices.
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.
Eval & Safety: Eval framework design, golden datasets, automated and human evals, red-teaming, guardrails, hallucination control, observability for AI systems.
Machine Learning & Deep Learning: Predictive modeling, deep learning (CNNs, RNNs/LSTMs, Transformers), embeddings, vector search, classical ML; CV, NLP, and time-series exposure.
Cloud, MLOps & Deployment: AWS, Azure, or GCP at depth; model serving, GPU/accelerator ops, CI/CD, monitoring, on-prem and edge deployment patterns.
Data Engineering: Kafka, Spark/Flink, Hadoop, MongoDB and other NoSQL/graph/vector stores; large-scale streaming and batch pipelines.
Math Foundations: Linear algebra, probability, statistics, optimization.
Experience with commerce cloud ecosystems (good to have) – Salesforce and Adobe

10–12 years of hands-on experience building and deploying ML, DL, and AI systems in production, with progression into solution architecture and technical leadership
10+ years of demonstrable experience working with global businesses, delivering on large accounts
3+ years of demonstrable hands-on work in GenAI and/or Agentic AI — beyond prompt engineering and basic RAG — including multi-agent systems, custom fine-tuning, multimodal pipelines, or SLM-based deployments.
Proven track record of architecting and shipping AI systems in enterprise-grade environments, including regulated or high-stakes domains.
3+ Experience leading ML-AI technical pods or teams (formal or dotted-line), mentoring senior engineers, and setting hiring and review standards