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
