AI / ML Engineer

About the job

AI / ML Engineer – Agentic Systems Experience : 3–8 Years Location : Remote Mode of Engagement : Full-time No. of Positions : 4 Educational Qualifications : B.E. / B.Tech / M.E. / M.Tech in Computer Science, AI / ML, Data Science, or related field Industry : IT – AI / ML Services Notice Period : Immediate What We Are Looking For • 3–8 years of hands-on experience in AI / ML with strong practical exposure to Transformer-based models. • Proven experience in fine-tuning, optimizing, and deploying LLMs (BERT, T5, GPT-style, LLaMA, Mistral, etc.). • Strong Python skills for model training, inference pipelines, APIs, and system integration. • Real-world experience working with agentic / multi-agent AI systems (planner–executor, supervisor–worker patterns, tool-using agents). • Ability to independently own model development → experimentation → production deployment. • Experience handling latency, cost, scalability, and monitoring in production AI systems. Responsibilities • Design, fine-tune, and deploy Transformer-based models for NLP, reasoning, information extraction, and generation tasks. • Build and manage multi-agent AI systems for task decomposition, orchestration, and decision-making. • Implement efficient inference pipelines with batching, caching, quantization, and latency optimization. • Develop production-grade REST APIs using FastAPI / Flask and containerize services using Docker. • Collaborate with internal teams and clients to convert business requirements into scalable AI solutions. • Monitor model performance, accuracy, drift, and cost; continuously improve system reliability. • Stay up to date with advancements in transformer architectures, LLMs, and agent-based AI systems. Qualifications • Bachelor’s or Master’s degree in Computer Science, AI / ML, or a related discipline. • Minimum 3 years of hands-on experience with Transformer architectures. • Strong working experience with Hugging Face Transformers and PyTorch (preferred) or TensorFlow / JAX. • Solid understanding of attention mechanisms, encoder–decoder models, embeddings, and fine-tuning strategies. • Familiarity with multi-agent frameworks (LangChain, LangGraph, AutoGen, CrewAI, or similar). • Experience with REST APIs, Docker, Git, and cloud deployment on AWS or GCP. • Strong communication skills with the ability to explain complex AI concepts to non-technical stakeholders.

Requirements

• AI/ML
• Transformer-based models
• Python
• LLMs
• FastAPI
• Flask
• Docker

Qualifications

• B.E.
• B.Tech
• M.E.
• M.Tech
• Computer Science
• AI / ML
• Data Science

Preferred Technologies

• AI/ML
• Transformer-based models
• Python
• LLMs
• FastAPI
• Flask
• Docker