Lead Architect

Job Description:

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 and hybrid role that includes architecting AI solutions and shaping the growth of the AI practice. This role is suited for someone who has earned technical fluency with GenAI and agentic AI on top of a strong foundation in classical ML and deep learning — and who is now ready to set the technical direction for a growing team.

The role is a hybrid lead position: leading solutions for multiple AI delivery pods, partnering with engineering and DX leadership, and owning the craft of the practice — driving solution architectures, evaluation standards, reusable components, and the technical bar for the AI delivery team. The expectation is 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, while consistently raising the standards at which the team operates.

• 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.

• 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.

• You must have an architect's mindset and equipped with a builder's hands.
• You must be agile and current — actively in the thick of GenAI and agentic developments, learning and shipping at the pace the field demands, with strong fundamentals and understanding of the domain
• Excellent communicator — able to explain agentic, multimodal, and SLM trade-offs clearly to engineering peers, business stakeholders, and clients.
• Open and flexible toward a hybrid work structure with no less than 3 days work from office — to ensure regular connection and cross-project knowledge exchange across the AI practice.

Skill Category

AI Solutions Lead

Solution Architecture

Owns reference architectures across multiple concurrent AI engagements; sets patterns for agentic, multimodal, and SLM-based systems.

Transformers & Deep Learning

Deep practical grounding in transformers, fine-tuning (SFT, LoRA/QLoRA, RLHF/RLAIF), distillation, quantization, and SLM design for cost/latency-bound deployments.

Generative AI (LLMs & Multimodal)

Designs hybrid multimodal RAG, KAG, and grounded-generation systems; selects the right model class (frontier vs. SLM vs. fine-tuned) per use case.

Agentic Frameworks

Architects multi-agent orchestration, planning, memory, tool use, and inter-agent communication patterns including MCP and A2A.

Eval, Safety & Guardrails

Establishes evaluation frameworks, hallucination control, red-teaming, regression suites, and observability as a first-class engineering discipline.

Information Retrieval & Knowledge

Designs hybrid dense–sparse retrieval, ranking, KG-augmented retrieval, and unified knowledge layers across vector, graph, and NoSQL stores.

Predictive & Classical ML

Strong foundation in classical ML and DL (CV, NLP, time-series, GNNs); able to choose non-GenAI approaches when they fit better.

Conversational AI

Architects multi-turn, multilingual, multimodal dialogue systems with grounded responses and structured evaluation.

Model Deployment

Drives enterprise deployment patterns — cloud, on-prem, and edge — including GPU/accelerator ops, scaling, CI/CD, and lifecycle automation.

Cloud & MLOps

End-to-end model and agent lifecycle on AWS/Azure/GCP; cost, latency, and reliability optimization at production scale.

Data Engineering & Pipelines

Designs streaming and batch pipelines (Kafka, Spark, Flink) and the data foundations that production AI systems depend on.

Practice Leadership

Sets the technical bar for hiring, mentoring, code/architecture reviews, and reusable IP; grows the AI practice as a craft.

Location:

Brand:

Time Type:

Contract Type: