Lead AI/ML Engineer

JD for Lead AI/ML Engineer

Job Title: Lead AI/ML Engineer

Job Purpose:

We are seeking a highly experienced Lead AI/ML Engineer to drive the design,

development, and deployment of advanced AI solutions across the organization. The

ideal candidate will have 6–10 years of experience in AI/ML, with strong expertise in

Generative AI, Large Language Models (LLMs), Computer Vision, and AI platform

architecture.

As a Lead AI/ML Engineer, you will define AI strategy, architect scalable AI systems,

and lead a team of engineers and data scientists to build production-ready AI

solutions. You will play a critical role in translating business problems into innovative

AI-driven products, leveraging technologies such as LLMs, Retrieval-Augmented

Generation (RAG), AI agents, and multimodal AI systems.

You will collaborate closely with Product, Engineering, and Data teams to build

intelligent, scalable, and high-performance AI platforms that power next-generation

applications.

Key Responsibilities:

AI Strategy & Technical Leadership

• Lead the architecture, design, and implementation of enterprise-scale AI/ML

solutions.

• Define and drive the AI/ML roadmap, ensuring alignment with business objectives

and product strategy.

• Provide technical leadership and mentorship to AI/ML engineers and data scientists.

• Establish best practices for AI model development, experimentation, deployment,

and monitoring.

Generative AI & LLM Systems

• Design and develop Generative AI applications using LLMs such as GPT, LLaMA,

Gemini, or custom models.

• Architect and implement Retrieval-Augmented Generation (RAG) pipelines for

enterprise knowledge systems.

• Lead initiatives for LLM fine-tuning, prompt engineering, and model optimization.

• Design AI agent architectures using frameworks like LangChain, LangGraph, and

LlamaIndex.

AI/ML Model Development:

• Develop and deploy NLP, Computer Vision, and multimodal AI models for real-world

business applications.

• Implement advanced deep learning architectures using PyTorch, TensorFlow, or

Keras.

• Identify and evaluate pre-trained and foundation models suitable for specific use

cases.

• Drive data preprocessing, feature engineering, and dataset curation for model

training.

AI Platform & Infrastructure

• Design scalable AI infrastructure and MLOps pipelines for model training,

deployment, and monitoring.

• Deploy AI solutions across cloud platforms (AWS, Azure, GCP) or hybrid/on-premise

environments.

• Build APIs, microservices, and pipelines to integrate AI capabilities into enterprise

applications.

• Lead efforts in model optimization, inference acceleration, and resource efficiency.

Performance Optimization & Quality

• Conduct model evaluation, benchmarking, and continuous performance optimization.

• Optimize AI systems for latency, scalability, and cost efficiency.

• Implement testing, monitoring, and observability frameworks for AI systems in

production.

Collaboration & Innovation

• Work closely with Product, Engineering, and Data teams to define AI-powered

product features.

• Stay at the forefront of AI research and emerging technologies, evaluating their

business impact.

• Promote a culture of experimentation, innovation, and knowledge sharing within the

AI team.

Required Skills & Experience

AI & Machine Learning

• 6–10 years of experience in AI/ML development and deployment.

• Strong expertise in supervised and unsupervised learning techniques, including

regression, classification, clustering, SVMs, and neural networks.

Generative AI & LLMs

• Hands-on experience with LLM training, fine-tuning, prompt engineering, and

optimization.

• Experience building GenAI applications such as chatbots, AI assistants, and

document intelligence systems.

NLP & Computer Vision

• Strong experience in Natural Language Processing and Computer Vision.

• Hands-on expertise with Transformers, OpenCV, YOLO, and R-CNN architecture.

AI Agents & Frameworks

• Experience with multi-agent frameworks such as LangChain, LangGraph, and

LlamaIndex.

Deep Learning Frameworks

• Proficiency in PyTorch, TensorFlow, or Keras.

Programming

• Strong programming skills in Python with experience in API development and

microservices.

Cloud & AI Infrastructure

• Experience deploying AI models on AWS, Azure, or Google Cloud Platform.

• Familiarity with MLOps pipelines, model serving, and AI lifecycle management.

Vector Databases

• Hands-on experience with vector databases such as FAISS, Pinecone, ChromaDB, or

Weaviate.

Performance Optimization

• Experience optimizing LLM inference for speed, cost, and memory efficiency.

Leadership & Collaboration

• Proven ability to lead AI projects and mentor engineering teams.

• Strong communication skills with the ability to translate business requirements into

AI solutions.

Good to Have

• Experience with multimodal AI (text, image, video, speech).

• Familiarity with Docker, Kubernetes, and containerized AI deployment.

• Experience with model serving frameworks such as FastAPI, Flask, or NVIDIA Triton.

• Exposure to distributed training and large-scale model training pipelines.

Qualifications:

ME (IT, Computer), BE (IT, Computer), MCA, MSC-IT, BCA

Required Competencies

• Must possess excellent communication skills – oral and written

• Must possess knowledge of latest technology trends

• Must be a keen learner – should be able to drive “Self Learning”

• Must practice principle of “First Time Right”

• Must have an Eye for Details

• Must have high Customer Orientation

• Must be adaptable to working in multiple / matrix work environment

• Must possess good systems thinking

• Must possess good negotiation, analytical and interpersonal skills. Good leadership &

team player qualities.

• High on personal integrity with ability to establish relationships and work in teams and

should be able to influence stakeholders. Should poses independence, robust ethics and

resilience.