AI Lead Architect

Job Description:

Key Responsibilities

• • Solution Engineering & Technical Execution:

• o Lead the hands-on engineering for end-to-end AI solutions across Deep Learning,

• GenAI, Agentic AI, and multimodal use cases.

• o Apply rigorous "fail fast" logic to all AI project management. Quickly identify,

• evaluate, and disqualify unviable AI use cases based on technical feasibility, effort,

• cost, and risk early in the cycle.

• o Perform explicit trade-off analysis on model class (frontier vs. SLM vs. fine-tuned),

• retrieval design, memory optimization, and orchestration.

• o 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.

• o 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).

• o Conduct solution design reviews across concurrent client engagements; facilitate

• subjective technical decisions and enable delivery excellence.

• Multimodal Agentic Systems & SLM Design:

• o Design and lead the build of multi-agent systems with reasoning, planning, tool

• use, persistent memory, and grounded retrieval.

• o Lead multimodal system design and solutions across text, vision, speech, and

• structured data, including ingestion, representation, and downstream agent

• reasoning.

• o 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

• o Define hybrid retrieval and knowledge architectures spanning vector, graph (KG),

• and NoSQL stores; lead KG-assisted retrieval, entity linking, and structured

• grounding.

• • Eval, Guardrails & Production Quality:

• o 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.

• o Define and enforce safety, guardrail, and hallucination-control standards across

• the practice; lead red-teaming and adversarial testing for high-stakes

• deployments.

• o Set the bar for production readiness—reliability, latency, cost, monitoring, drift

• detection, and incident response—for AI systems in regulated, enterprise-grade

• environments.

• o Lead GPU/accelerator ops, model serving, and lifecycle automation for

• deployment across cloud hyper-scalers, on-prem, and edge.

• • Technical Leadership & Capability Pillars:

• o Act as a technical sentinel for the AI practice, mentoring engineers through

• rigorous code and architecture reviews to ensure permanent capability building

• rather than temporary crisis management.

• o Establish and enforce AI in SDLC frameworks on delivery projects.

• • Cross-functional Leadership & Delivery

• o Engage with client and stakeholder leadership on architecture, feasibility, and risk;

• communicate technical direction clearly to non-technical audiences.

• o Support pre-sales and solutioning for new GenAI and Agentic AI opportunities,

• including effort estimation, architectural framing, and capability storytelling.

Must Have

Technical Skills

• Deep Learning & Machine Learning: Strong hands-on experience with neural networks, Transformers, predictive modeling, embeddings, and vector search.
• Generative AI: Hands-on experience with LLMs/SLMs, RAG/Agentic RAG, agents, prompt engineering, grounding, multimodal architectures, and production GenAI solutions.
• Fine-tuning: Practical experience with techniques such as SFT, LoRA/QLoRA, RLHF/RLAIF, distillation, and/or quantization.
• Agentic AI: Hands-on experience with multi-agent orchestration, planning, tool use, memory, and agentic workflows. Experience with frameworks such as LangGraph, LlamaIndex, or AutoGen.
• Programming & Engineering: Advanced Python, SQL, strong API/backend engineering experience using FastAPI, Flask, Django, or equivalent frameworks.
• Production Engineering: Proven experience designing, developing, testing, and deploying AI/ML solutions in enterprise production environments.
• Cloud: Strong hands-on experience with at least one major cloud platform — AWS, Azure, or GCP.
• Data/Storage: Experience with databases and data platforms such as MongoDB, NoSQL, vector databases, graph databases, or equivalent.
• Experience: Minimum 8 years of total hands-on software development/engineering experience.
• AI Experience: Minimum 3+ years of hands-on experience building and deploying Deep Learning/AI systems in production.
• GenAI/Agentic AI: Demonstrable hands-on experience beyond basic API integrations or simple RAG implementations, such as multi-agent systems, custom fine-tuning, advanced RAG, or SLM deployments.
• Work Location: Willingness to work from the Pune office at least 3 days per week.

Good to have:

Experience with commerce cloud ecosystems (Salesforce and

Adobe)

Location

Pune

Brand:

Merkle

Time Type:

Full time

Contract Type:

Permanent