Lead Generative AI Engineer

Dentsu Global Services; Mumbai; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Dentsu Global Services is seeking a Lead Generative AI Engineer in Mumbai to craft advanced GenAI applications, integrating LLMs, multimodal models, and agentic capabilities. You will work with CX, engineering, and product teams to deliver scalable AI solutions for global brands.

The role emphasizes production-grade ML deployment, knowledge graphs, and safety guardrails, with mentoring of junior engineers and collaboration across MLOps.

Qualifications

• Proficient in Python and SQL with API development and data engineering experience.
• Expertise in Generative AI including LLMs, RAG, and multimodal architectures.
• Hands-on cloud deployment experience on AWS/Azure/GCP for AI systems.

Responsibilities

• Build conversational and non-conversational multimodal AI applications using LLMs and frameworks.
• Design AI workflows with reasoning, tool-use, memory, grounding, and external integrations.
• Develop KG-assisted AI systems with entity extraction and retrieval enhancements.

Skills

Python

SQL

APIs

Data pipelines

LLMs

RAG

Multimodal

LangChain

AWS

Azure

GCP

MongoDB

PyTorch

TensorFlow

Tools

LangChain

LangGraph

LlamaIndex

AutoGen

PyTorch

TensorFlow

AWS

Azure

GCP

MongoDB

Apache Spark

Hadoop

Job description

Looking for your next opportunity in Lead Generative AI EngineerWe're hiring experienced professionals with strong expertise in Generative AI Engineer to join our growing team.

AI Lead Engineer

Role Overview

We are seeking a Lead Generative AI Engineer with strong foundations in deep learning, transformer architecture, and practical experience building GenAI applications beyond basic RAG systems. The ideal candidate has hands-on experience/technical familiarity with LLM fine-tuning, multimodal models, retrieval systems, agentic frameworks, retrieval architectures, and production-grade ML deployment.

This role will partner with engineering, data science, and CX teams to build intelligent agents, multimodal experiences, personalization systems, and knowledge-grounded AI solutions that power the future of customer engagement for global brands.

Key Responsibilities

• Build conversational and non-conversational, multimodal, and agentic AI applications using LLMs and frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or similar.
• Design AI workflows incorporating reasoning, planning, tool-use, memory, grounding, and external system integrations.
• Develop Knowledge Graph (KG)-assisted AI systems, including entity extraction, linking, and KG-augmented retrieval.
• Ensure safety, consistency, and hallucination-control through structured evaluation and guardrails.
• Transform models into scalable APIs and microservices using Python, FastAPI/Flask, Docker.
• Deploy and monitor ML/AI systems in AWS/Azure/GCP, optimizing for cost, latency, and reliability.
• Collaborate with MLOps teams on CI/CD pipelines, model versioning, monitoring, and automated evaluation.
• Work with big data technologies including Apache Spark, Hadoop, and NoSQL databases such as MongoDB.
• Build and optimize transformer-based and multimodal models using deep learning frameworks (e.g., PyTorch, TensorFlow).
• Implement fine-tuning, alignment (RLHF/RLAIF), LoRA/QLoRA, pruning, and model evaluation pipelines.
• Develop information retrieval systems, including hybrid dense-sparse retrieval, ranking, knowledge graphs, and relevance optimization.
• Build predictive models and ML pipelines from scratch, including data preparation, feature engineering, and model selection.
• Work cross-functionally with CX, engineering, and product stakeholders to translate business needs into AI solutions.
• Document models, experiments, evaluation frameworks, and deployment processes.
• Mentor junior engineers and contribute to internal best practices, reusable components, and R&D initiatives.

Required Technical Skills

• Programming: Python (advanced), SQL; robust experience with API development and data engineering,
• Generative AI: LLMs, RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs.
• Cloud Platforms: AWS, Azure, or GCP with hands-on experience deploying and scaling AI systems.
• Data Technologies: Apache Spark, Hadoop, MongoDB; strong understanding of data pipelines and large-scale processing.
• Math Foundations: Linear algebra, probability, statistics.

Experience Requirements

• Minimum 5-6 years of hands-on software development experience including building and deploying machine learning models into production.
• 2+ years of experience working with deep learning, GenAI, or transformer-based architectures.
• Demonstrated experience building GenAI applications beyond simple RAG (e.g., agents, multimodal, custom LLM fine-tuning).
• Experience integrating AI systems in enterprise-grade environments.