Job Description
AI/ML Engineer – Agentic Systems
Experience: 4–8 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
4–8 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.
Experience: 4–8 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
4–8 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.
Apply for this Position
Ready to join ? Click the button below to submit your application.
Submit Application