AI Engineer

dentsuaegis; Pune District; On-site; INR 2,800,000 - 4,200,000; Full time

Job summary

dentsuaegis invites a Lead AI Engineer in Pune to drive GenAI strategy for customer engagement. You will architect multimodal AI solutions, design scalable ML pipelines, and mentor a growing team across engineering and data science.

You will deploy production‑grade models on AWS/Azure/GCP, integrate with knowledge graphs, retrieval systems, and agentic frameworks, and ensure safety with guardrails and evals, delivering tangible business outcomes for global brands.

Qualifications

• Minimum 5-6 years of hands-on software development including production ML deployment.
• 2+ years of experience with deep learning, GenAI, or transformer architectures.
• Experience building GenAI applications beyond basic RAG (agents, multimodal, fine-tuning).
• Experience integrating AI systems in enterprise-grade environments.

Responsibilities

• Build conversational and multimodal AI apps using LLMs and frameworks such as 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.
• Ensure safety, consistency, and guardrails through structured evaluation.
• Transform models into scalable APIs and microservices using Python, FastAPI/Flask, Docker.
• Deploy and monitor ML/AI systems in AWS/Azure/GCP, optimizing cost, latency, and reliability.
• Collaborate with MLOps on CI/CD, model versioning, monitoring, and evaluation.

Skills

Python (advanced)

SQL

API development

Data engineering

Flask

FASTAPI

Django

PyTorch

TensorFlow

LangChain / LangGraph

LLMs / GenAI

Knowledge graphs

Cloud platforms (AWS/Azure/GCP)

Spark / Hadoop / MongoDB

Transformer architectures

Tools

Docker

Spark

Hadoop

MongoDB

NoSQL databases

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