Senior Applied AI Engineer Multi-Strat Fund - Selby Jennings - eFinancialCareers : Job Details

A leading multi-strategy investment firm is seeking a Senior AI Engineer to join a high-impact Applied AI team focused on building next-generation tools that enhance decision-making across the investment lifecycle. Operating at the intersection of finance and machine intelligence, the team is responsible for developing production-grade AI systems that empower portfolio managers, analysts, and researchers with intelligent, data-driven capabilities.

About the Role

This is a senior-level engineering position within a cross-functional team that blends AI research, software engineering, and large-scale data integration. You'll play a key role in designing and deploying robust AI infrastructure, with a particular focus on knowledge representation, intelligent retrieval systems, and the seamless integration of AI models into production environments.

You'll collaborate closely with ML researchers, frontend engineers, and stakeholders across the business to deliver scalable, high-performance applications that bridge structured and unstructured data.

Key Responsibilities

• Design and build intelligent data retrieval systems that power AI-driven investment tools.
• Collaborate with ML researchers to prototype, develop, and deploy new AI/ML products.
• Work with frontend engineers to integrate backend systems into user-facing applications.
• Lead architectural decisions and contribute to the evolution of AI infrastructure.
• Participate in the full software development lifecycle, from design through deployment and support.
• Mentor junior engineers and contribute to a culture of technical excellence.
• Support critical infrastructure through on-call rotations and incident response.

Ideal Candidate Profile

• 8+ years of professional software engineering experience, with at least 3 years focused on backend AI systems.
• Strong Python skills and deep familiarity with ML libraries and frameworks.
• Experience designing and deploying knowledge graphs and working with graph databases (e.g. Neo4j, Amazon Neptune).
• Proficient in both SQL and NoSQL databases, with a strong understanding of data modelling and integration.
• Experience building APIs (REST, gRPC) and working with containerised environments (Docker, Kubernetes).
• Solid grasp of distributed systems, networking, and infrastructure-as-code practices.
• Familiarity with vector databases, retrieval-augmented generation (RAG), and LLM integration is a plus.
• Exposure to cloud platforms (AWS preferred) and messaging systems (Kafka, RabbitMQ) is advantageous.
• Strong communication skills and the ability to work across technical and non-technical teams.
• Knowledge of financial markets or investment workflows is a bonus, but not required.

If you're excited by the opportunity to shape how AI is applied in a high-performance investment environment, we'd love to hear from you.