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

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:ImmediateWhat We Are Looking For 3–8 years of hands-on experience in AI/ML with strong practical exposure toTransformer-based models . Proven experience infine-tuning, optimizing, and deploying LLMs(BERT, T5, GPT-style, LLaMA, Mistral, etc.). StrongPythonskills for model training, inference pipelines, APIs, and system integration. Real-world experience working withagentic / multi-agent AI systems(planner–executor, supervisor–worker patterns, tool-using agents). Ability to independently ownmodel development → experimentation → production deployment . Experience handlinglatency, cost, scalability, and monitoringin production AI systems.Responsibilities Design, fine-tune, and deployTransformer-based modelsfor NLP, reasoning, information extraction, and generation tasks. Build and managemulti-agent AI systemsfor task decomposition, orchestration, and decision-making. Implementefficient inference pipelineswith batching, caching, quantization, and latency optimization. Develop production-gradeREST APIsusing 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 intransformer architectures, LLMs, and agent-based AI systems .Qualifications Bachelor’s or Master’s degree in Computer Science, AI/ML, or a related discipline. Minimum3 years of hands-on experiencewith Transformer architectures. Strong working experience withHugging Face TransformersandPyTorch(preferred) or TensorFlow/JAX. Solid understanding ofattention mechanisms, encoder–decoder models, embeddings, and fine-tuning strategies . Familiarity withmulti-agent frameworks(LangChain, LangGraph, AutoGen, CrewAI, or similar). Experience withREST APIs, Docker, Git , and cloud deployment onAWS or GCP . Strong communication skills with the ability to explain complex AI concepts to non-technical stakeholders.

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