Lead AI Engineer

dentsuaegis; Pune District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Dentsu Aegis seeks an AI Lead Engineer with deep learning, transformer architectures, and GenAI expertise to build scalable AI systems and knowledge-grounded solutions. You will partner with engineering, data science and CX teams to ship intelligent agents and multimodal experiences across global brands.

The role emphasizes production-grade ML deployment, CI/CD pipelines, and ML Ops practices in a hybrid work setting.

Qualifications

• Proficient in Python and SQL with API development experience.
• Experience building production-grade GenAI applications and large language models.
• Hands-on experience with multimodal models, agentic frameworks, and retrieval architectures.
• Familiarity with deployment to cloud platforms and MLOps practices.
• Strong knowledge of knowledge graphs, vector search, and information retrieval.

Responsibilities

• Build conversational and multimodal GenAI applications using LLMs and frameworks.
• Design AI workflows with reasoning, planning, tool-use, memory and grounding.
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• CoT_job_summary_short
• COMPANY NAME: Dentsu AegisKEY POINTS: GenAI, multimodal AI, agentic frameworks
• job_summary_short
• <p>Dentsu Aegis is seeking an AI Lead Engineer to design and deploy production‑grade GenAI systems, including multimodal models and agentic workflows. The role emphasizes building scalable APIs, cloud deployments, and robust evaluation pipelines.</p><p>You will collaborate with CX, data science, and engineering teams to deliver knowledge-grounded AI solutions, ensure safety, and mentor junior engineers in a hybrid work model.</p>
• contract_type
• fulltime
• location_type
• remote
• remote_scope
• international
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• Lead AI Engineer in Pune, India: market range INR 40–70 LPA based on experience and scope.
• estimated_low
• 4000000
• estimated_medium
• 5500000
• estimated_high
• 7000000
• currency
• INR
• frequency
• yearly
• bonus
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• tax
• gross
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Skills

Python

SQL

API development

Flask

FASTAPI

Django

PyTorch

TensorFlow

LLMs

RAG

Multimodal

Agents

Prompt engineering

Grounding

Knowledge graphs

AWS

Azure

GCP

Apache Spark

Hadoop

MongoDB

Linear algebra

Tools

LangChain

LangGraph

LlamaIndex

AutoGen

Docker

Kubernetes

PyTorch

TensorFlow

Spark

Hadoop

MongoDB

Job description

Job Description:

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

Generative AI, Multimodal Systems & Agentic Frameworks

• 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.

Deployment, APIs & Cloud Engineering

• 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.

Model Development & Applied AI Engineering

• 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.

Collaboration, Documentation & Mentorship

• 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,
• Backend Frameworks: Flask, FASTAPI, Django
• Machine Learning: Predictive modelling, deep learning, optimization, embeddings, vector search, model evaluation.
• 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.

Skill Category

• Lead AI Engineer
• Transformers & Deep Learning
• Applies LoRA/QLoRA, distillation, debugging, optimization.
• Generative AI (LLMs & Multimodal)
• Builds tool‑using pipelines, multilingual/multimodal flows.
• Information Retrieval & Relevance
• Implements hybrid retrieval + ranking, KG‑enhanced semantic retrieval
• Predictive Modeling
• Builds and tunes end‑to‑end ML pipelines.
• Knowledge Graphs
• Builds KG pipelines (entity linking, embeddings).
• Conversational AI
• Multi‑turn, multilingual dialogue systems with evaluation metrics.
• Agentic Frameworks
• Multi‑step agent workflows with planning & memory.
• Model Deployment
• Scales services with CI/CD, monitoring, GPU/accelerator ops.
• Cloud & MLOps
• End‑to‑end model lifecycle automation.
• Big Data & Pipelines
• Uses Spark/Hadoop/MongoDB effectively.
• Deep Learning
• Understand and applied deep learning architectures - RNNs, LSTMs, Transformers

Attitude & Mindset

• Growth‑oriented, collaborative, and experimentation‑driven.
• Strong problem‑solving skills with a bias toward action.
• Ability to communicate complex concepts clearly to non‑technical stakeholders.
• Open and flexible towards a hybrid work structure with no less than 2‑days work from office - This is to ensure that the team working in the AI domain regularly connects and does knowledge exchange across pr