Key Responsibilities 

• Develop Algorithms that enable AI agents to perform tasks without step-by-step instructions.  

• Design and implement agentic AI systems capable of autonomous decision-making, learning from experience, and adapting to dynamic goals and contexts 

• Design and develop multi-agent frameworks using tools such as LangGraph, Crew AI, or Semantic Kernel to orchestrate intelligent workflows. 

• Design and develop machine learning techniques that allow agents to learn from experience and adapt over time.  

• Translate business requirements into agentic AI solutions. 

• Collaborate with data scientists, software engineers, business stakeholders, and product teams to integrate AI solutions into production systems. 

• Conduct context and prompt engineering using zero-shot, few-shot, and chain-of-thought techniques to enhance model performance and relevance. 

• Optimize agents based on different models for performance, scalability, and accuracy. 

• Ensure ethical AI practices and compliance with data privacy regulations. 

• Document processes, models, and code for transparency and reproducibility. 

• Conduct research and stay up to date with the latest advancements in AI/ML technologies. 

Required Skills & Experience  

• Proficiency in programming languages such as Python, Java, or C++.  

• Strong foundation in AI/ML algorithms, data structures, and software engineering principles. 

• Understanding of cloud platforms (e.g., AWS, Azure, GCP) for deploying AI solutions.  

• Familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker for CI/CD).  

• Solid knowledge of data structures, algorithms, and software engineering principles.  

• Experience with agent orchestration, LLMOps, and model lifecycle management, vector search, information retrieval, graph algorithms and knowledge graph. 

• Experience with version control systems (e.g., Git).  

• Familiarity with agent orchestration platforms and enterprise integration. 

• Strong problem-solving skills and ability to work independently and collaboratively. 

Preferred Skills & Experience 

• Experience with natural language processing (NLP), computer vision, or reinforcement learning. 

• Knowledge of generative AI and foundation models (e.g., Gemini, Claude). 

• Experience with real-time inference systems and edge AI. 

• Background in mathematics, statistics, or computational neuroscience. 

• Understanding of LLM (Large Language Model)  & LRM (Large Reasoning Model) 

• Understanding of AI ethics, bias mitigation, and explainable AI.