Role: AI Engagement Lead – Wealth & Asset Management
Structure: Full-time
Location: Newark, NJ (Hybrid)
Rates: $180,000 - $210,000 base plus competitive bonus, benefits and 401K package
This is a high-impact leadership role at the intersection of engineering, data science, and business strategy. As an AI Technology Lead, you will own the end-to-end delivery of production-grade GenAI applications and data science solutions. You will provide hands-on technical guidance to a cross-functional team of developers and scientists while maintaining the project management discipline required to move complex AI programs from concept to deployment.
Production-Grade GenAI Applications: End-to-end delivery of scalable AI solutions, including the implementation of RAG pipelines, LLM integrations, and robust vector search architectures.
Technical Milestones & Roadmaps: Translation of ambiguous business problems into clear technical scopes, Agile delivery plans (Scrum/Kanban), and production deployment schedules.
Validated Solution Architectures: Designing secure, compliant, and performant architectures on Microsoft Azure, ensuring seamless integration between frontend, backend, and data pipelines.
Technical Proofs of Concept (PoCs): Rapid development of prototypes and prototypes to validate technical feasibility and guide decision-making for long-term production builds.
Extensive Technical Leadership: At least 10+ years of experience in software engineering or AI, with a minimum of 3 years specifically leading AI/ML or GenAI-focused projects.
Hands-on GenAI Expertise: Deep technical knowledge of LLMs (OpenAI, Gemini, etc.), prompt engineering, embeddings, RAG architectures, and responsible AI guardrails.
Azure Cloud Proficiency: Strong experience designing and delivering solutions using the Microsoft Azure ecosystem, specifically Azure OpenAI Service, AKS, Data Factory, and App Services.
Full-Stack & MLOps Oversight: Ability to guide development across the full stack (React, Python/Node.js) and partner with data scientists on MLOps/LLMOps workflows and monitoring.
Agile Program Management: Proven expertise in driving Agile execution, managing complex dependencies, and communicating technical trade-offs to both senior leadership and technical teams.
