AI Lead Architect

Overview

Lead hands-on engineering, architecture, and technical execution for end-to-end AI solutions across deep learning, Generative AI, agentic AI, multimodal, and applied machine learning use cases. Drive pragmatic enterprise AI delivery through technical validation, trade-off analysis, production quality, and capability building.

What you'll do

• Lead hands-on engineering for end-to-end AI solutions across Deep Learning, GenAI, Agentic AI, and multimodal use cases.
• Apply “fail fast” logic to identify, evaluate, and disqualify unviable AI use cases based on technical feasibility, effort, cost, and risk early in the cycle.
• Perform trade-off analysis on model class, retrieval design, memory optimization, and orchestration.
• Lead solutioning and support 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, including planning, tool use, memory, grounding, and inter-agent communication using MCP and A2A.
• Conduct solution design reviews across concurrent client engagements and facilitate technical decisions.
• Design and lead multi-agent systems with reasoning, planning, tool use, persistent memory, and grounded retrieval.
• Lead multimodal system design across text, vision, speech, and structured data, including ingestion, representation, and downstream agent reasoning.
• Establish patterns for SLM design and adoption through distillation, fine-tuning, quantization, and routing to meet enterprise constraints.
• Define hybrid retrieval and knowledge architectures spanning vector, graph, and NoSQL stores, including KG-assisted retrieval, entity linking, and structured grounding.
• Establish evaluation frameworks, golden datasets, regression suites, automated and human-in-the-loop evaluations, and observability.
• Define and enforce safety, guardrail, and hallucination-control standards, and lead red-teaming and adversarial testing.
• Set production-readiness standards for reliability, latency, cost, monitoring, drift detection, and incident response.
• Lead GPU and accelerator operations, model serving, and lifecycle automation across cloud hyperscalers, on-premises, and edge environments.
• Mentor engineers through code and architecture reviews and establish AI-in-SDLC frameworks.
• Engage client and stakeholder leadership on architecture, feasibility, and risk, and communicate technical direction to non-technical audiences.
• Support pre-sales and solutioning for GenAI and Agentic AI opportunities, including effort estimation, architectural framing, and capability storytelling.

What you'll need

• Strong hands-on experience with neural networks, Transformers, predictive modeling, embeddings, and vector search.
• Hands-on experience with LLMs/SLMs, RAG/Agentic RAG, agents, prompt engineering, grounding, multimodal architectures, and production GenAI solutions.
• Practical experience with SFT, LoRA/QLoRA, RLHF/RLAIF, distillation, and/or quantization.
• Hands-on experience with multi-agent orchestration, planning, tool use, memory, and agentic workflows.
• Experience with frameworks such as LangGraph, LlamaIndex, or AutoGen.
• Advanced Python and SQL skills.
• Strong API/backend engineering experience using FastAPI, Flask, Django, or equivalent frameworks.
• Proven experience designing, developing, testing, and deploying AI/ML solutions in enterprise production environments.
• Strong hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.
• Experience with databases and data platforms such as MongoDB, NoSQL, vector databases, graph databases, or equivalent.
• Minimum 8 years of total hands-on software development/engineering experience.
• Minimum 3+ years of hands-on experience building and deploying Deep Learning/AI systems in production.
• Demonstrable hands-on GenAI/Agentic AI experience beyond basic API integrations or simple RAG implementations, such as multi-agent systems, custom fine-tuning, advanced RAG, or SLM deployments.
• Willingness to work from the Pune office at least 3 days per week.
• Be equipped with a builder’s hands and a highly pragmatic approach to enterprise AI.
• Prioritize technical validation, pragmatic domain expertise, and rigorous testing over “AI hype.”
• Be open and flexible toward a hybrid work structure with no less than 3 days of work from the office in Pune.

Nice to have

• Experience with commerce cloud ecosystems, including Salesforce and Adobe.

Details

• Work location: Pune.
• Hybrid work structure with at least 3 days per week from the Pune office.
• Employment type: Full time.
• Contract type: Permanent.
• Brand: Merkle.