AI Lead Architect

dentsu; Maharashtra; Vor Ort; INR 1.200.000 - 2.500.000; Vollzeit

Zusammenfassung

Merkle in Pune is seeking a senior AI engineer to lead hands-on development of end-to-end AI solutions, including Deep Learning, GenAI, and multi-agent systems. The role demands experience delivering production-grade GenAI platforms and guiding architecture choices across cloud and on-prem deployments.

You will own reference architectures, drive memory-grounded retrieval designs, and collaborate with clients on feasibility, risk, and deployment readiness in a fast-paced enterprise setting.

Qualifikationen

• 8+ years of total hands-on software development/engineering experience.
• 3+ years hands-on experience building and deploying AI systems in production.
• Experience with GenAI, agentic AI, multi-agent systems.
• Willingness to work from the Pune office at least 3 days per week.

Aufgaben

• 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 AI project management, evaluating and disqualifying unviable use cases early.
• Perform trade-off analysis on model classes, retrieval design, memory, and orchestration.
• Lead solutioning and architecture for GenAI, Agentic AI, multimodal use cases with trade-offs on model class and deployment.
• Own reference architectures and design patterns for multimodal agentic systems including memory, grounding, and inter-agent communication.
• Conduct solution design reviews across client engagements and facilitate key technical decisions.
• Design and build multi-agent systems with reasoning, planning, tool use, memory, and grounded retrieval.
• Lead multimodal system design across text, vision, speech, and structured data.

Kenntnisse

Deep Learning

GenAI

Agentic AI

Multi-agent systems

Python

SQL

FastAPI

LlamaIndex

LangGraph

AutoGen

AWS

NoSQL

MongoDB

Vector databases

Graph databases

Tools

LangGraph

LlamaIndex

AutoGen

Jobbeschreibung

Job Description

Key Responsibilities

• Solution Engineering & Technical Execution:
• Lead the hands-on engineering for end-to-end AI solutions across Deep Learning, GenAI, Agentic AI, and multimodal use cases.
• 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.
• Perform explicit trade-off analysis on model class (frontier vs. SLM vs. fine-tuned), retrieval design, memory optimization, and orchestration.
• 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.
• 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).
• Conduct solution design reviews across concurrent client engagements; facilitate subjective technical decisions and enable delivery excellence.
• Design and lead the build of multi-agent systems with reasoning, planning, tool use, persistent memory, and grounded retrieval.
• Lead multimodal system design and solutions across text, vision, speech, and structured data, including ingestion, representation, and downstream agent reasoning.
• 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.
• Define hybrid retrieval and knowledge architectures spanning vector, graph (KG), and NoSQL stores; lead KG-assisted retrieval, entity linking, and structured grounding.
• 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.
• Define and enforce safety, guardrail, and hallucination-control standards across the practice; lead red-teaming and adversarial testing for high-stakes deployments.
• Set the bar for production readiness - reliability, latency, cost, monitoring, drift detection, and incident response - for AI systems in regulated, enterprise-grade environments.
• Lead GPU/accelerator ops, model serving, and lifecycle automation for deployment across cloud hyper-scalers, on-prem, and edge.
• 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.
• Establish and enforce AI in SDLC frameworks on delivery projects.
• Engage with client and stakeholder leadership on architecture, feasibility, and risk; communicate technical direction clearly to non-technical audiences.
• Support pre-sales and solutioning for new GenAI and Agentic AI opportunities, including effort estimation, architectural framing, and capability storytelling.

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