Lead AI/ML Engineer

Hiringhood; Vadodara; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Hiringhood is seeking an experienced Lead AI/ML Engineer to drive the design, development, and deployment of advanced AI solutions in Vadodara, India. The role demands 6–10 years of AI/ML experience with strong expertise in Generative AI, LLMs, CV, and AI platform architecture.

You will define AI strategy, architect scalable systems, and lead initiatives to build production-ready solutions using LLMs, RAG, AI Agents, and multimodal AI. Strong leadership and mentoring skills are essential.

Qualifications

• 6–10 years of experience in AI/ML development and deployment.
• Strong experience with Generative AI and LLMs.
• Hands-on experience with LLM fine-tuning, prompt engineering, and optimization.
• Experience building RAG, chatbots, AI assistants, or document intelligence solutions.
• Strong NLP and Computer Vision experience.
• Experience with Transformers, OpenCV, YOLO, and R-CNN.
• Experience with LangChain, LangGraph, and/or LlamaIndex.
• Proficiency in PyTorch, TensorFlow, or Keras.
• Experience with AWS, Azure, or GCP.
• Experience with MLOps, model serving, and AI lifecycle management.
• Hands-on experience with vector databases such as FAISS, Pinecone, ChromaDB, or Weaviate.
• Experience optimizing LLM inference for speed, cost, and memory.
• Proven leadership and mentoring experience.

Responsibilities

• Lead architecture, design, and implementation of enterprise-scale AI/ML solutions.
• Define and drive AI/ML technical roadmaps.
• Provide technical leadership and mentorship to AI/ML engineers and data scientists.
• Establish best practices for AI development, deployment, monitoring, and optimization.
• Develop GenAI applications using GPT, LLaMA, Gemini, or other foundation models.
• Design and implement RAG pipelines.
• Lead LLM fine-tuning, prompt engineering, and model optimization.
• Design AI agent architectures using LangChain, LangGraph, and LlamaIndex.
• Develop NLP, Computer Vision, and multimodal AI solutions.
• Work with PyTorch, TensorFlow, or Keras.
• Evaluate foundation and pre-trained models for business use cases.
• Drive data preprocessing, feature engineering, and dataset curation.
• Design scalable AI infrastructure and MLOps pipelines.
• Deploy AI solutions on AWS, Azure, GCP, or hybrid environments.
• Build APIs, microservices, and AI integration pipelines.
• Optimize model inference for performance, scalability, and cost.
• Conduct model evaluation and benchmarking.
• Optimize AI systems for latency, scalability, and cost.
• Implement testing, monitoring, and observability for production AI systems.
• Work closely with Product, Engineering, and Data teams.
• Translate business requirements into scalable AI solutions.
• Mentor engineers and promote innovation and knowledge sharing.

Skills

AI Architecture

Leadership

Generative AI

LLMs

RAG pipelines

MLOps

Model optimization

Education

BE/BTech, ME/MTech, MCA, MSc-IT, BCA or equivalent

Tools

LangChain

LangGraph

LlamaIndex

PyTorch

TensorFlow

Keras

OpenCV

YOLO

R-CNN

AWS

Azure

GCP

FAISS

Pinecone

ChromaDB

Weaviate

NVIDIA Triton

Job description

Our client is a leading technology organization focused on developing innovative AI/ML solutions, Generative AI applications, and next-generation digital products.

Job Summary

We are looking for an experienced Lead AI/ML Engineer to drive the design, development, and deployment of advanced AI solutions.

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.

You will define AI strategy, architect scalable AI systems, and lead AI/ML engineering initiatives to build production-ready solutions using LLMs, RAG, AI Agents, and multimodal AI.

Key Responsibilities

AI Strategy & Technical Leadership

• Lead architecture, design, and implementation of enterprise-scale AI/ML solutions.
• Define and drive AI/ML technical roadmaps.
• Provide technical leadership and mentorship to AI/ML engineers and data scientists.
• Establish best practices for AI development, deployment, monitoring, and optimization.

Generative AI & LLM

• Develop GenAI applications using GPT, LLaMA, Gemini, or other foundation models.
• Design and implement RAG pipelines.
• Lead LLM fine-tuning, prompt engineering, and model optimization.
• Design AI agent architectures using LangChain, LangGraph, and LlamaIndex.
• Develop NLP, Computer Vision, and multimodal AI solutions.
• Work with PyTorch, TensorFlow, or Keras.
• Evaluate foundation and pre-trained models for business use cases.
• Drive data preprocessing, feature engineering, and dataset curation.

AI Platform & Infrastructure

• Design scalable AI infrastructure and MLOps pipelines.
• Deploy AI solutions on AWS, Azure, GCP, or hybrid environments.
• Build APIs, microservices, and AI integration pipelines.
• Optimize model inference for performance, scalability, and cost.

Performance & Quality

• Conduct model evaluation and benchmarking.
• Optimize AI systems for latency, scalability, and cost.
• Implement testing, monitoring, and observability for production AI systems.
• Work closely with Product, Engineering, and Data teams.
• Translate business requirements into scalable AI solutions.
• Mentor engineers and promote innovation and knowledge sharing.

Required Skills

• 6–10 years of experience in AI/ML development and deployment.
• Strong experience with Generative AI and LLMs.
• Hands-on experience with LLM fine-tuning, prompt engineering, and optimization.
• Experience building RAG, chatbots, AI assistants, or document intelligence solutions.
• Strong NLP and Computer Vision experience.
• Experience with Transformers, OpenCV, YOLO, and R-CNN.
• Experience with LangChain, LangGraph, and/or LlamaIndex.
• Proficiency in PyTorch, TensorFlow, or Keras.
• Experience with AWS, Azure, or GCP.
• Experience with MLOps, model serving, and AI lifecycle management.
• Hands-on experience with vector databases such as FAISS, Pinecone, ChromaDB, or Weaviate.
• Experience optimizing LLM inference for speed, cost, and memory.
• Proven leadership and mentoring experience.

Good to Have

• Multimodal AI — text, image, video, or speech.
• Docker and Kubernetes.
• FastAPI, Flask, or NVIDIA Triton.
• Distributed training and large-scale model training pipelines.

Qualifications

BE/BTech, ME/MTech, MCA, MSc-IT, BCA or equivalent qualification in Computer Science, IT, or related fields.