Lead AI Engineer

Dentsu Global Services; Maharashtra; Hybrid; INR 4,000,000 - 6,000,000; Full time

Job summary

Dentsu Global Services in Pune is seeking a Lead AI Engineer to build and deploy GenAI applications, including multimodal and agentic systems. You will work closely with CX, engineering, and product teams to translate business needs into scalable AI solutions.

The role requires 5–6 years of software development experience with deep learning, and hands‑on experience in production ML systems, model deployment, and cloud platforms.

Qualifications

• 5–6 years of hands‑on software development including building and deploying ML models into production.
• Experience with deep learning, GenAI, or transformer architectures.

Responsibilities

• Build multimodal and agentic AI applications using LLMs and frameworks (LangChain, LangGraph, LlamaIndex, AutoGen).
• Design AI workflows with reasoning, planning, tool-use, memory, grounding, and external system integrations.
• Develop KG‑assisted AI systems with entity extraction/linking and KG‑augmented retrieval.
• Collaborate with CX, engineering, and product teams to translate business needs into AI solutions.

Skills

Python

SQL

API development

Tools

Flask

FastAPI

Django

Docker

Apache Spark

Hadoop

MongoDB

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

• 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, or 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 – Applies LoRA/QLoRA, distillation, debugging, optimization.
• Transformers & Deep Learning – Builds and tunes end‑to‑end ML pipelines.
• 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 – Understands and applies 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 to facilitate knowledge exchange across projects.

Location: DGS India – Pune – Kharadi EON Free Zone

Brand: Merkle

Time Type: Full time

Contract Type: Permanent