Lead Architect

What you'll do

• Architect and govern AI delivery practice across GenAI, Agentic AI, and applied ML engagements
• Lead solution architecture and technical direction for multiple AI delivery pods
• Translate business problems into staged AI solution roadmaps
• Lead solutioning and architecture for end-to-end AI solutions including multimodal and agentic systems
• Own reference architectures and solution design patterns for multimodal agentic systems
• Conduct solution design reviews and facilitate technical decisions
• Design and lead multi-agent systems with reasoning, planning, tool use, persistent memory, and grounded retrieval
• 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, safety guardrails, hallucination control, and production readiness standards
• Drive enterprise deployment best practices across cloud, on-prem, and edge environments
• Shape the practice capability roadmap and mentor AI engineers
• Set technical hiring bar and lead architecture and senior engineering interviews
• 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 experience working with global businesses on large accounts
• 3+ years hands-on experience in GenAI and/or Agentic AI beyond prompt engineering
• Experience with multi-agent systems, custom fine-tuning, multimodal pipelines, or SLM-based deployments
• Proven track record of architecting and shipping AI systems in enterprise-grade, regulated or high-stakes environments
• 3+ years experience leading ML-AI technical pods or teams, mentoring senior engineers, setting hiring and review standards
• Advanced programming skills in Python and SQL
• Strong API and backend engineering experience (FastAPI/Flask/Django)
• Experience with generative AI techniques including LLMs, SLMs, prompt engineering, fine-tuning, distillation, quantization
• Experience with agentic AI including multi-agent orchestration, planning, tool use, persistent memory
• Experience designing evaluation frameworks, guardrails, hallucination control, red-teaming
• Strong foundation in classical ML and deep learning (CV, NLP, time-series)
• Experience with cloud platforms (AWS, Azure, GCP) and enterprise deployment including GPU/accelerator ops
• Experience with data engineering tools like Kafka, Spark, Flink, Hadoop, MongoDB, NoSQL/graph/vector stores
• Good to have experience with commerce cloud ecosystems like Salesforce and Adobe
• Excellent communication skills
• Willingness to work hybrid with minimum 3 days in office

Tech stack