Senior/Lead AI ML Engineer

Hiringhood; Vadodara; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Hiringhood is seeking a senior AI/ML leader to architect and deliver enterprise-scale AI solutions. You will guide generative AI projects, supervise engineers, and shape the AI roadmap to align with product strategy. Experience with LangChain, LLMs, and scalable infrastructure is essential.

The role requires hands-on expertise in NLP, CV, and multimodal AI, with strong Python and cloud deployment skills across AWS/Azure/GCP. You will mentor teams and drive innovation in a fast-paced environment.

Qualifications

• Extensive hands-on AI/ML development and deployment experience.
• Strong background in supervised/unsupervised learning, NLP, CV.
• Experience with LLM training, fine-tuning, prompt engineering.

Responsibilities

• Lead enterprise AI/ML architecture and implementation.
• Define AI roadmap aligned with product strategy and business goals.
• Mentor AI/ML engineers and data scientists.
• Develop GenAI apps and RAG pipelines for knowledge systems.
• Drive model optimization, deployment, and monitoring practices.
• Collaborate with product/engineering/data teams on AI features.

Skills

AI strategy

Technical leadership

LLM systems

Python programming

PyTorch/TensorFlow

MLOps

Cloud platforms

NLP/Computer Vision

LangChain/LangGraph

Model deployment

Education

Master’s or PhD in CS/ML

Tools

FAISS/Pinecone/ChromaDB/Weaviate

Docker/Kubernetes

FastAPI/Flask

Job description

Company Brief :

One of our key employers is a leading technology solutions and software development company specializing in enterprise applications, digital transformation, cloud computing, AI/ML, data engineering, and custom software development. The organization delivers innovative, scalable, and high-quality solutions to clients across various industries, helping businesses accelerate their digital initiatives. It fosters a collaborative, innovation-driven work culture with excellent opportunities for professional growth, continuous learning, and exposure to modern technologies.

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

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

Performance Optimization & Quality

• 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

• 610 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).
• Experience 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.