AI Engineer (New York City Metropolitan Area)

AI Engineer [Senior Associate / AVP]

Founded in 1992, Cerberus is a global leader in alternative investing with approximately $71 billion in assets across complementary credit, private equity, and real estate strategies. We invest across the capital structure where our integrated investment platforms and proprietary operating capabilities create an edge to improve performance and drive long-term value. Our tenured teams have experience working collaboratively across asset classes, sectors, and geographies to seek strong risk-adjusted returns for our investors. For more information about our people and platforms, visit us at

About Us

We are a new, but growing team of AI specialists - software engineers focused on AI development, and technology strategists - working to transform how an alternative investment firm with $65B in assets under management leverages technology and data. Our remit is broad, spanning investment operations, portfolio companies, and internal systems, giving the team the opportunity to shape the way the firm approaches analytics, automation, and decision-making.

We operate with the creativity and agility of a small team, tackling diverse, high-impact challenges across the firm. While we are embedded within a global investment platform, we maintain a collaborative, innovative culture where our AI talent can experiment, learn, and have real influence on business outcomes.

About the job

As a software engineer on the AI team at Cerberus, you’ll build internal applications that help our investment teams and portfolio companies use AI, data and automation in real workflows. This role is about taking high-value business problems from rough idea to working product, including full-stack apps, workflow automations, and AI-enabled tools.

You’ll design, implement and operate production-grade applications across the stack: Python services, APIs, React and TypeScript frontends, relational databases, SQL, orchestration layers, integrations with enterprise data sources, and LLM-powered agentic workflows.

We’re looking for engineers who care about impact: people who can prototype quickly, make pragmatic technical trade-offs, work closely with business and product stakeholders, and turn emerging AI capabilities into reliable products that users actually adopt.

Responsibilities

• Build internal AI-enabled applications: Design and deliver full-stack applications that solve real business problems across investment workflows, portfolio operations, analytics, data management and process automation.
• Build on top of enterprise data and workflows: Develop applications and workflows that sit on top of structured business data, internal systems and third-party tools, whether that is Microsoft Fabric, Rayfin, relational databases, APIs or other environments.
• Develop across the stack: Build backend services and APIs in Python using FastAPI or similar frameworks, and create user-facing workflows and interfaces using React and TypeScript.
• Implement AI agents in code: Build and integrate AI agents for use cases such as analytics, data management, application building and business process automation, using LLM provider APIs such as OpenAI or Anthropic.
• Turn prototypes into products: Take rough ideas, user pain points and proof-of-concepts through rapid iteration, user testing and, where appropriate, production deployment.
• Orchestrate reliable workflows: Use orchestration patterns and tools such as Temporal or similar systems to make multi-step workflows reliable, observable and maintainable.
• Engineer for adoption and maintainability: Partner with investment teams, portfolio companies, product managers and business stakeholders to make applications useful, usable, secure and supportable.
• Make pragmatic trade-offs: Balance speed, quality, security, cost and maintainability in a fast-moving environment where the goal is to create measurable business value.

Sample Projects You’ll Work On

• Data-driven business applications: Build applications that sit on top of structured data assets and workflow tools, enabling users to explore, update, enrich or act on information through intuitive workflows rather than static dashboards alone.
• AI agents for analytics and data management: Develop agents that can query data, perform analysis, generate structured outputs, validate information, and support repeatable business processes with appropriate controls and human review.
• Workflow automation and internal tools: Build full-stack applications that connect internal systems, proprietary data sources, third-party APIs and AI services into structured workflows for investment, operations or portfolio company teams.
• Prototype-to-production applications: Work with business and product stakeholders to turn a rough idea into a POC, test it with users, refine the workflow, and harden the solution where there is clear business value.

Your Experience

We’re a small, high-impact and entrepreneurial team with a broad remit. We’re looking for an Associate or Senior Associate-level software engineer with strong full-stack engineering fundamentals who is excited to build internal applications, AI-enabled products and data-driven tools. We don’t expect every candidate to check every box, but strong candidates will combine hands-on software engineering ability with sound product judgement and strong communication skills.

• Strong software engineering foundation: Degree in a STEM field or equivalent experience, with a track record of building reliable applications, backend services, internal tools, APIs or production systems.
• Python expertise: Skilled in writing production-grade Python, including clean APIs, type hints, testing, packaging and maintainable service design.
• Backend development: Experience building backend services and APIs using FastAPI or similar frameworks, with an ability to reason about reliability, maintainability, observability and system design.
• Frontend development: Experience building modern frontends using React and TypeScript, with an ability to create practical, usable interfaces for business users.
• Databases and SQL: Strong understanding of relational databases, data modelling and SQL, with the ability to write efficient, maintainable queries and work with structured business data.
• Data applications: Experience building applications, workflows or analytics products on top of enterprise data platforms, relational databases, APIs or business systems. Familiarity with tools such as Microsoft Fabric, Databricks or Rayfin is helpful but not required.
• System architecture: Experience working with distributed systems, monolithic systems, or both, and the judgement to choose the right architecture for the problem rather than over-engineering by default.
• Workflow orchestration: Exposure to Temporal or similar orchestrators is a plus, especially for multi-step, long-running or failure-prone workflows.
• AI/ML tooling and agent development: Experience building or implementing AI agents in code for use cases such as analytics, data management, application building or business process automation.
• LLM provider APIs: Experience interacting with LLM provider APIs such as OpenAI, Anthropic or similar services, including tool use, structured outputs, evaluation and monitoring considerations.
• Agent frameworks and observability: Familiarity with tools such as LangChain, LangGraph, LangSmith or Langfuse is helpful but optional. We care more about sound engineering judgement than any single framework.
• Vector databases and retrieval: Experience with vector databases, embeddings or retrieval-augmented generation is useful but not required.
• AI coding tools: Familiarity with AI coding tools such as Claude, Cursor, Codex or similar tools, and the judgement to use them effectively while maintaining high engineering standards.
• Engineering practices: Strong habits around Git, testing, code reviews, CI/CD, documentation, monitoring and operational ownership. Experience with tools such as Sentry or Grafana is a plus.
• Product and delivery judgement: The technical skills to build across the full stack, combined with the judgement to know which problems are worth solving, when to prototype, when to harden, and when to stop.
• Pragmatic execution: Ability to take ambiguity, shape it into a concrete technical plan, build quickly, learn from users and make pragmatic trade-offs.
• Communication and stakeholder management: Strong communication, stakeholder management and business interaction skills, with the ability to work effectively with business users, product managers and technical colleagues.

For More Advanced or Senior Candidates

More senior candidates may bring a combination of the above skills together with experience designing AI systems or leading small AI-native engineering efforts. Additional strengths may include:

• Designing AI platforms, application architectures or agentic systems.
• Leading small engineering teams or technical workstreams.
• Aligning engineering decisions with product priorities and business value.
• Mentoring other enginee]]>