Key Responsibilities

Agentic AI System Design & Architecture

• Design and develop multi-agent AI systems using LangGraph for autonomous decision-making and workflow automation
• Build context-aware, memory-augmented AI agents capable of reasoning, planning, and executing tasks
• Define scalable architectures for LLM-based systems integrated with enterprise applications

Model Development & Optimization

• Develop and optimize AI agent workflows for performance, modularity, and scalability
• Implement Retrieval-Augmented Generation (RAG) using vector databases such as Pinecone, Weaviate, and FAISS
• Apply reinforcement learning techniques (RLHF/RLAIF) to improve agent behavior and decision-making

Statistical & Machine Learning Expertise

• Apply hypothesis testing techniques such as T-Test and Z-Test for data validation and insights
• Build and deploy regression models (linear and logistic) for predictive analytics
• Implement classification models including Decision Trees and SVM
• Perform forecasting using methods like ARIMA, ARIMAX, and Exponential Smoothing
• Work with probabilistic graph models and statistical computing techniques

AI Research & Innovation

• Conduct research in Agentic AI, LLM orchestration, and multi-agent systems
• Experiment with self-improving AI systems and adaptive learning frameworks
• Stay updated with advancements in generative AI, reinforcement learning, and autonomous agents

Data Engineering & MLOps Integration

• Use tools like PySpark, Python, SAS/SPSS, R, and R Studio for data processing and analysis
• Work with ML frameworks including TensorFlow, PyTorch, Scikit-learn, Keras, MXNet, and CNTK
• Implement model validation and monitoring using tools such as Great Expectations and Evidently AI
• Deploy models using platforms like Kubeflow and BentoML

Leadership & Stakeholder Impact

• Lead and mentor data science and AI engineering teams
• Translate AI capabilities into business-driven solutions and scalable enterprise systems
• Drive proof-of-concept (PoC) initiatives and scale successful models into production
• Ensure responsible AI practices and model governance