AI/ML Engineer – Agentic Systems ( 3 to 8 yrs )

AI/ML Engineer – Agentic SystemsExperience: 3–8 YearsLocation: RemoteMode of Engagement: Full-timeNo. of Positions: 4Educational Qualifications: B.E./B.Tech/M.E./M.Tech in Computer Science, AI/ML, Data Science, or related fieldIndustry: IT – AI/ML ServicesNotice Period: Immediate What We Are Looking For3–8 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. ResponsibilitiesDesign, 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. QualificationsBachelor’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.

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