Senior/Lead AI ML Engineer

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

• 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

• 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).

• 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.