Responsibilities

Key Responsibilities

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Build AI and ML pipelines for training, inference, embeddings, retrieval, reranking and grounded generation.

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Develop production-grade RAG solutions using appropriate chunking, vector stores, retrieval methods, metadata filters and evaluation datasets.

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Design and implement agents using frameworks such as Google ADK and LangChain, including tool use, reasoning flows, memory, caching and multi-agent orchestration.

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Integrate agents with internal APIs, search, reporting workflows, enterprise systems and external data sources using MCP or equivalent secure tool-integration patterns.

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Implement and run evaluation for accuracy, groundedness, hallucination, bias, tool success, task completion, latency, reliability and cost; add regression tests for prompt, model and workflow changes.

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Build and deploy APIs, services and applications on GCP / Vertex AI and, where required, Azure; manage containerization, CI/CD, environment configuration and release readiness.

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Monitor models and agents in production, diagnose failures,



and optimize token usage, compute, latency, scalability and runtime cost.

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Collaborate with the Tech Lead, Product Owner, Data Scientists, Data Engineers and QA teams; document code, APIs, experiments, evaluations and operational runbooks

Qualifications

3-6 years of applied AI / ML engineering experience, with evidence of deploying models, RAG systems, agents or AI services beyond notebooks and prototypes.

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Demonstrated ownership of production code, API integration, automated testing, deployment, monitoring and troubleshooting.

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Team-oriented and agile working style, with the ability to explain technical decisions and AI limitations clearly to media and product teams.

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Curiosity, structured problem solving and a strong bias toward reusable, maintainable and measurable solutions.

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Media, martech, audience data, research data or automated reporting experience is a strong plus.

📌 AI Engineering Specialist (Mumbai)
🏢 PUBLICIS GROUPE
📍 Mumbai