AI/ML Engineer

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.