Lead Architect

What you'll do

• Architect, govern, and grow AI delivery practice across GenAI, Agentic AI, and applied ML engagements
• Lead solutions for multiple AI delivery pods and partner with engineering and DX leadership
• Drive solution architectures, evaluation standards, reusable components, and technical bar for AI delivery team
• Translate business problems into staged, defensible AI solution roadmaps
• Lead solutioning and architecture for end-to-end AI solutions across GenAI, Agentic AI, multimodal, and applied ML use cases
• Own reference architectures and solution design patterns for multimodal agentic systems
• Conduct solution design reviews and facilitate technical decisions
• Design and lead 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
• Establish patterns for SLM design and adoption to meet enterprise constraints
• Define hybrid retrieval and knowledge architectures spanning vector, graph, and NoSQL stores
• Establish evaluation frameworks, golden datasets, regression suites, automated and human-in-the-loop evals, and observability
• Define and enforce safety, guardrail, and hallucination-control standards
• Lead red-teaming and adversarial testing for high-stakes deployments
• Set production readiness standards including reliability, latency, cost, monitoring, drift detection, and incident response
• Drive enterprise deployment best practices across cloud hyper-scalers, on-prem, and edge
• Shape practice capability roadmap and mentor AI engineers and leads
• Run technical reviews, pairing sessions, and internal knowledge exchange
• Set technical hiring bar and lead architecture and senior engineering interviews
• 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
• Support pre-sales and solutioning for new GenAI and Agentic AI opportunities

Requirements

• 10–12 years of hands-on experience building and deploying ML, DL, and AI systems in production
• Progression into solution architecture and technical leadership
• 10+ years working with global businesses on large accounts
• 3+ years hands-on work in GenAI and/or Agentic AI beyond prompt engineering and basic RAG
• Experience with multi-agent systems, custom fine-tuning, multimodal pipelines, or SLM-based deployments
• Proven track record architecting and shipping AI systems in enterprise-grade environments including regulated or high-stakes domains
• 3+ years experience leading ML-AI technical pods or teams, mentoring senior engineers, and setting hiring and review standards
• Advanced Python and SQL programming skills
• Strong API and backend engineering experience with FastAPI, Flask, or Django
• Experience with LLMs, SLMs, RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs, fine-tuning, distillation, and quantization
• Experience with multi-agent orchestration, planning, tool use, persistent memory, MCP and A2A patterns
• Eval framework design, golden datasets, automated and human evals, red-teaming, guardrails, hallucination control, observability
• Strong foundation in classical ML and DL including CNNs, RNNs/LSTMs, Transformers, embeddings, vector search, CV, NLP, and time-series
• Cloud experience with AWS, Azure, or GCP including model serving, GPU/accelerator ops, CI/CD, monitoring, on-prem and edge deployment
• Data engineering experience with Kafka, Spark/Flink, Hadoop, MongoDB and other NoSQL/graph/vector stores
• Math foundations in linear algebra, probability, statistics, optimization
• Good to have experience with commerce cloud ecosystems like Salesforce and Adobe
• Excellent communication skills
• Agile and current with GenAI and agentic AI developments
• Open and flexible toward hybrid work with minimum 3 days in office

Tech stack