Lead AI Engineer

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 densesparse 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.