Senior AI/ML engineer

Job description

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.

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.