Key Responsibilities:
Agentic AI System Design & Development
- Design and build multi-agent systems, autonomous agents, and tool-using AI workflows.
- Develop orchestration frameworks for agent collaboration using LLM-based architectures.
- Implement human-in-the-loop and decision-support systems for enterprise applications.
- Optimize agent performance for reliability, scalability, and cost efficiency.
LLM Engineering & Productionization
- Fine-tune and prompt-engineer Large Language Models (open-source and proprietary).
- Build and deploy production-grade LLM applications including copilots, chat systems, and intelligent automation tools.
- Design and implement Retrieval-Augmented Generation (RAG) pipelines.
- Evaluate and optimize LLM performance across accuracy, latency, and cost metrics.
GenAI Frameworks & Ecosystem
- Work with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen.
- Integrate LLMs with enterprise data sources and APIs for real-world use cases.
- Develop reusable GenAI components and scalable architecture patterns.
- Stay current with emerging GenAI tools and research advancements.
Data Science & Machine Learning
- Translate business problems into well-defined ML and AI solutions.
- Perform advanced data wrangling, feature engineering, and exploratory data analysis.
- Apply statistical modeling, deep learning, NLP, and machine learning techniques.
- Drive experimentation, hypothesis testing, and model evaluation.
MLOps / LLMOps & Production Systems
- Design and implement CI/CD pipelines for ML and LLM systems.
- Ensure model monitoring, versioning, governance, and performance tracking.
- Optimize infrastructure for scalable deployment on cloud platforms (AWS, Azure, or GCP).
- Implement responsible AI practices including explainability, fairness, and risk management.
Insight Generation & Stakeholder Communication
- Translate complex analytical and AI outputs into actionable business insights.
- Build data storytelling narratives for executive leadership.
- Influence strategic decisions through AI-driven recommendations.
- Communicate technical concepts clearly to non-technical stakeholders.
Leadership & Mentorship
- Lead and mentor senior data scientists and ML engineers.
- Define best practices for AI development, deployment, and governance.
- Drive capability building in GenAI, Agentic AI, and advanced analytics.
- Foster a culture of innovation, ownership, and continuous learning.
Innovation & Problem Solving
- Apply design thinking to develop user-centric AI solutions.
- Evaluate trade-offs between model complexity, performance, and business value.
- Lead experimentation with cutting-edge AI techniques and architectures.
- Identify opportunities for AI-driven transformation across business domains.
