Responsibilities: 

Solve Business Problems with AI 

• Design and build advanced ML models that integrate multi-dimensional data into insights and signals that drive critical business decisions. 

• Design and build enterprise knowledge systems that integrate structured and unstructured data across multiple business platforms, enabling AI agents to retrieve, reason over, and operationalize trusted organizational knowledge. 

• Partner with business stakeholders to identify, frame, and prioritize high‑value problems that can be addressed using Agentic AI, LLMs, and ML. 

• Define and implement business-centric evaluation frameworks that measure coverage, relevance, trustworthiness, explainability, and user adoption in addition to technical model performance. 

• Focus on business outcomes, not just model performance. 

Design & Build Agentic AI Solutions 

• Architect and develop agentic AI systems that can reason, plan, and take actions across tools, workflows, and data sources. 

• Design multi‑agent and tool‑augmented LLM solutions to automate complex, multi‑step processes. 

• Ensure solutions are reliable, explainable, and governed for enterprise use. 

Scalable & Responsible AI 

• Collaborate with engineering teams to deploy AI solutions with scalability, security, and performance in mind. 

• Implement evaluation, monitoring, and guardrails for LLM and agentic systems, including bias, drift, and failure modes. 

• Align solutions with enterprise risk management, compliance, and responsible AI standards. 

Thought Leadership & Collaboration 

• Act as a trusted AI advisor, helping teams understand where Agentic AI and LLMs add value—and where they do not. 

• Contribute to AI best practices, reusable patterns, and strategic direction. 

• Mentor peers and teammates on applied AI and business‑driven problem solving. 

Qualifications: 

• Agentic AI: Experience designing AI agents that reason, plan, and act across systems. 

• Large Language Models (LLMs): Hands‑on experience building enterprise LLM applications (e.g., RAG, tool use, orchestration, evaluation). 

• Natural Language Processing (NLP): Strong experience working with unstructured text and language‑driven workflows. 

• ML: Hands on experience with Gradient Boosting methods, familiar with preeminent hyper-parameter tuning and interpretability options. 

• MS or PhD in Computer Science, Machine Learning, Data Science, or a related quantitative field. 

• 3+ years delivering AI/ML solutions in production environments. 

• 5+ years of hands‑on Python experience; experience with distributed data processing is a plus. 

• 0Strong ability to solve business problems using AI, not just build models. 

• Excellent communication skills, with the ability to explain complex concepts to both technical and non‑technical audiences. 

• Experience working in cross‑functional, enterprise environments.