AI Lead Architect

Merkle Italia; Pune District; Hybrid; INR 350,000 - 600,000; Full time

Job summary

Merkle is seeking an AI Lead Architect to spearhead hands-on engineering for end-to-end AI solutions including deep learning, GenAI, and multimodal agentic use cases in Pune. You will evaluate, design, and implement scalable architectures with rigorous fail-fast laws and risk assessment across frontiers, SLMs, and fine-tuned models.

You will lead solutioning, architecture reviews, and production readiness, while mentoring engineers and engaging with cross-functional teams to drive

Qualifications

• Hands-on experience with neural networks, transformers, embeddings, and vector search.
• Proven experience building GenAI and agentic AI solutions in production.
• Experience with model fine-tuning, RAG, and deployment of AI models.
• Experience with multi-agent orchestration, planning, tool use, and memory.
• Strong Python development and API/backend engineering skills.
• Experience with cloud platforms (AWS/Azure/GCP) in production
• Experience with databases/no-SQL/vector/graph stores.

Responsibilities

• Lead hands-on engineering for end-to-end AI solutions across DL, GenAI, and multimodal use cases.
• Apply fail-fast evaluation to AI use cases, assessing feasibility, cost, and risk early.
• Perform trade-off analyses on model classes, retrieval design, memory, and orchestration.
• Own reference architectures and design patterns for multimodal agentic systems.
• Lead design reviews and enable delivery excellence across client engagements.
• Establish evaluation frameworks, guardrails, and production readiness standards.
• Guide GPU/accelerator ops, model serving, and lifecycle automation across environments.
• Mentor engineers through rigorous code and architecture reviews.

Skills

Deep Learning

Transformers

Generative AI

Agentic AI

Multi-agent systems

Python

APIs & Backend

Production AI

LangGraph

LlamaIndex

AutoGen

Education

Bachelor's in CS/Math/EE

Tools

FastAPI

SQL

MongoDB/NoSQL

Cloud (AWS/Azure/GCP)

Job description

## AI Lead ArchitectApply: Pune: Full time: Posted Today: R1123393**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 analysison model class (frontier vs. SLM vs. fine-tuned), retrieval design, memory, andorchestration.o Own the practice's reference architectures and solution design patterns formultimodal agentic systems, including planning, tool use, memory, grounding, andinter-agent communication (MCP, A2A).o Conduct solution design reviews across concurrent client engagements; facilitatesubjective technical decisions and enable delivery excellence.• **Multimodal Agentic Systems & SLM Design:**o Design and lead the build of multi-agent systems with reasoning, planning, tooluse, persistent memory, and grounded retrieval.o Lead multimodal system design and solutions across text, vision, speech, andstructured data, including ingestion, representation, and downstream agentreasoning.o Establish patterns for SLM design and adoption — distillation, fine-tuning,quantization, and routing — to meet enterprise constraints on cost, latency, dataresidency, and on-prem/edge deploymento Define hybrid retrieval and knowledge architectures spanning vector, graph (KG),and NoSQL stores; lead KG-assisted retrieval, entity linking, and structuredgrounding.**• Eval, Guardrails & Production Quality:**o Establish evaluation as a first-class discipline: design eval frameworks, goldendatasets, regression suites, automated and human-in-the-loop evals, andobservability for agentic and generative systems.o Define and enforce safety, guardrail, and hallucination-control standards acrossthe practice; lead red-teaming and adversarial testing for high-stakesdeployments.o Set the bar for production readiness—reliability, latency, cost, monitoring, driftdetection, and incident response—for AI systems in regulated, enterprise-gradeenvironments.o Lead GPU/accelerator ops, model serving, and lifecycle automation fordeployment across cloud hyper-scalers, on-prem, and edge.**• Technical Leadership & Capability Pillars:**o Act as a technical sentinel for the AI practice, mentoring engineers throughrigorous code and architecture reviews to ensure permanent capability buildingrather 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 andAdobe).Attitude & Mindset• Equipped with a builder's hands and a highly pragmatic approach to enterprise AI.• Prioritizes technical validation, pragmatic domain expertise, and rigorous testing over\"AI hype.\"• Open and flexible toward a hybrid work structure with no less than 3 days work fromthe office in Pune, ensuring regular connection and cross-project knowledge.**Location:**Pune**Brand:**Merkle**Time Type:**Full time**Contract Type:**Permanent