Lead AI Engineer

ABOUT THE ROLE

We are seeking an accomplished, hands-on Vice President, AI Engineer to design, build, and deploy enterprise-grade Generative AI, agentic AI, and machine learning solutions that drive meaningful business value across the firm.

The ideal candidate combines deep software engineering expertise with practical experience in modern AI architectures, large language models (LLMs), and cloud-native technologies. This individual will play a critical role in advancing our enterprise AI platform, developing reusable AI capabilities, and embedding intelligent automation into high-value investment and business workflows.

Success in this role requires strong proficiency in Python, sound architectural judgment, and the ability to translate complex business requirements into secure, scalable, and production-ready AI solutions. We value engineers who are intellectually curious, highly collaborative, pragmatic in their approach, and passionate about hands-on development and continuous innovation.

WHAT SUCCESS LOOKS LIKE 

The successful candidate will be a hands-on engineer who can independently take complex AI initiatives from concept through production, delivering reliable solutions that are adopted by the business and generate measurable value.

Beyond technical proficiency, we are looking for an individual who demonstrates strong ownership, sound engineering judgment, intellectual curiosity, and a collaborative mindset. This person will help shape the evolution of our enterprise AI capabilities while maintaining a strong focus on practical execution, operational excellence, and long-term scalability.

RESPONSIBILITIES

AI Engineering & Platform Development

• Design, develop, and deploy enterprise-grade Generative AI and agentic AI solutions, emphasizing modularity, reusability, scalability, and measurable business impact.
• Build and enhance core AI platform capabilities, including agent orchestration, tool integration, model abstraction, context management, and reusable AI services.
• Develop sophisticated AI agents capable of reasoning, planning, tool execution, and automating complex, multi-step business workflows.
• Evaluate and integrate commercial and open-source LLMs, leveraging frameworks and protocols such as Agno, Model Context Protocol (MCP), and related technologies.

Enterprise Knowledge & Data Integration

• Architect and implement Retrieval-Augmented Generation (RAG) solutions, including document ingestion, parsing, chunking, embeddings, vector indexing, semantic search, and retrieval optimization.
• Develop scalable document intelligence and information extraction capabilities for structured and unstructured enterprise content.
• Integrate AI solutions with enterprise data platforms, databases, APIs, knowledge graphs, and internal business applications.
• Build robust data pipelines and retrieval capabilities that enable AI agents to access and reason over enterprise knowledge securely and efficiently.

Cloud Architecture & Production Engineering

• Architect and implement scalable, secure, resilient, and cost-efficient AI solutions utilizing AWS, Azure, GCP, and cloud-native technologies.
• Develop production-quality Python services, APIs, and microservices, adhering to modern software engineering standards and best practices.
• Implement automated testing, CI/CD pipelines, deployment frameworks, and operational controls for AI applications and agents.
• Establish application observability, including tracing, logging, performance monitoring, usage analytics, model evaluation, and cost optimization.
• Continuously improve AI solution quality, latency, reliability, and efficiency through systematic evaluation, experimentation, and performance tuning.

Business Partnership & Technical Leadership

• Partner closely with investment professionals, business stakeholders, product managers, and technology teams to identify and deliver high-impact AI solutions.
• Translate business challenges into pragmatic technical designs and production-ready implementations, balancing delivery speed with long-term architectural sustainability.
• Contribute to architectural decisions, technical design reviews, code reviews, and engineering standards across the AI ecosystem.
• Promote responsible AI development, ensuring appropriate security, data privacy, access controls, governance, and human oversight.
• Mentor junior engineers, share technical knowledge, and foster a culture of engineering excellence, experimentation, and continuous learning.

QUALIFICATIONS 

Required

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical discipline, or equivalent practical experience.
• 8+ years of professional software engineering experience, including meaningful hands-on experience developing and deploying AI/ML or Generative AI solutions in production.
• Advanced proficiency in Python, with demonstrated experience building scalable backend services, APIs, and enterprise applications.
• Hands-on experience developing LLM-powered applications, AI agents, tool integrations, and/or RAG-based solutions.
• Strong understanding of modern AI engineering concepts, including prompt engineering, embeddings, vector databases, semantic retrieval, agent orchestration, and model evaluation.
• Practical experience with cloud platforms such as AWS, Azure, or GCP, including containerization, cloud-native services, and distributed application architectures.
• Solid understanding of software engineering fundamentals, including object-oriented design, data structures, system architecture, automated testing, and CI/CD.
• Proficiency with relational databases, SQL, REST APIs, and integration patterns; familiarity with NoSQL and vector databases.
• Experience designing, deploying, monitoring, and troubleshooting production systems with a strong emphasis on performance, security, reliability, and maintainability.
• Excellent analytical, problem-solving, and communication skills, with the ability to independently drive complex engineering initiatives and collaborate effectively across teams.

Preferred

• Experience building multi-agent systems, autonomous workflows, or enterprise AI orchestration platforms.
• Familiarity with modern AI frameworks and protocols, such as Agno, MCP, and agent-to-agent integration patterns.
• Experience implementing enterprise search, knowledge graphs, hybrid retrieval, reranking, and advanced RAG architectures.
• Knowledge of AI evaluation frameworks, observability, LLMOps/MLOps, model routing, and inference cost optimization.
• Experience with document intelligence, large-scale unstructured data processing, and multimodal AI capabilities.
• Experience working with financial or alternative investment datasets, financial documents, market data, and investment research workflows.
• Understanding of enterprise AI governance, data security, access control, and responsible AI practices.
• Experience mentoring engineers, conducting technical design reviews, and contributing to engineering architecture and best practices.

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