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.