Vice President

Key Responsibilities

• Develop and deliver GenAI/LLM solutions for problems such as content extraction, semantic search, question answering, summarization, reasoning, and recommendation.
• Design, deploy, and manage prompt-based and RAG-based systems, including orchestration patterns and agentic workflows (tool use, structured outputs, multi-step reasoning).
• Build comprehensive evaluation and testing frameworks (offline + online) to measure accuracy, faithfulness, robustness, latency, and cost; implement red-teaming and safety checks where applicable.
• Leverage Amazon Bedrock to prototype and productionize LLM applications, including model selection, prompt templates, routing, and deployment patterns.
• Work hands-on with Cortex (e.g., Cortex Analyst) to enable governed analytics experiences and GenAI-assisted workflows.
• Hands-on experience working in environments such as AWS Bedrock, Amazon SageMaker or Databricks 
• Collaborate with engineering teams to deliver scalable services (APIs, batch jobs, pipelines), ensuring strong software engineering discipline and operational readiness.
• Build and maintain data pipelines for structured and unstructured data, enabling retrieval, indexing, and preprocessing for LLM applications.
• Conduct applied research by studying scientific articles and state-of-the-art techniques (prompting, fine-tuning, evaluation, agent design) and translating them into practical improvements.
• Communicate clearly with technical and non-technical stakeholders, translating business needs into measurable problem statements, solution designs, and success metrics.
• Mentor and lead junior data scientists, influence standards, and drive adoption of responsible AI practices.

Required Qualifications, Skills & Capabilities

• Advanced degree (Masters preferred) in Data Science, Computer Science, Machine Learning, Statistics, or related quantitative field (or equivalent practical experience).
• 5 -7  years of relevant applied experience building ML/NLP solutions, including production deployment in a fast-paced environment.
• Proven NLP + LLM experience, including prompt engineering, RAG, and evaluation methodologies.
• Hands-on experience with Amazon Bedrock (or equivalent managed LLM platform) for building and deploying GenAI solutions.
• Experience with Cortex (e.g., Cortex Analyst and related workflows) in an enterprise setting.
• Strong Python skills; familiarity with ML/DL frameworks such as PyTorch or TensorFlow, and standard ML tooling (pandas, NumPy, scikit-learn).
• Experience building APIs and integrating LLM/NLP solutions into applications and services.
• Data pipeline experience for structured/unstructured data processing; strong understanding of embeddings, vector search, indexing, and retrieval patterns.
• Solid software engineering practices: Git/version control, code quality, testing, and CI/CD fundamentals.
• Excellent communication and stakeholder management skills; ability to present tradeoffs, risks, and results concisely.
• Strong analytical skills and working knowledge of financial services / markets / asset management concepts.

Preferred Qualifications

• Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and related methods.
• MLOps experience: experiment tracking, model registry, monitoring, drift/performance tracking, incident management, rollback.
• Experience with cloud deployment patterns (AWS preferred) and production runtime environments (containers/orchestration).