AI Engineer

Cerberus Capital Management is a global alternative investment firm seeking an AI Engineer to join its growing AI specialists team. The role focuses on building and operating full-stack internal applications, AI-enabled tools, workflow automations, and agentic systems that support investment operations, portfolio companies, analytics, and business processes.

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

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

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 engineers and improving team engineering practices

Advanced prioritisation and delivery judgement in ambiguous environments

Experience with AI governance, assurance, evaluation, security, permissions or agent sandboxes

Strong understanding of software development best practices, especially in the age of AI-assisted engineering

Preferred

Familiarity with tools such as Microsoft Fabric, Databricks or Rayfin is helpful but not required

Exposure to Temporal or similar orchestrators is a plus, especially for multi-step, long-running or failure-prone workflows

Familiarity with tools such as LangChain, LangGraph, LangSmith or Langfuse is helpful but optional

Experience with vector databases, embeddings or retrieval-augmented generation is useful but not required

Experience with tools such as Sentry or Grafana is a plus

Microsoft Fabric

Databricks

Rayfin

Workflow Orchestration

LangChain

LangGraph

LangSmith

Langfuse

Vector Databases

Embeddings

Retrieval-Augmented Generation

Sentry

Grafana

Benefits

Eligible for an annual discretionary bonus

Company

Alternative investment advisor in credit, real estate, and private equity

New York, New York, USA

http://www.cerberus.com/

Founded in 1992

501-1000 employees

Qualifications

Degree in a STEM field or equivalent experience, with a track record of building reliable applications, backend services, internal tools, APIs or production systems

Skilled in writing production-grade Python, including clean APIs, type hints, testing, packaging and maintainable service design

Experience building backend services and APIs using FastAPI or similar frameworks, with an ability to reason about reliability, maintainability, observability and system design

Experience building modern frontends using React and TypeScript, with an ability to create practical, usable interfaces for business users

Strong understanding of relational databases, data modelling and SQL, with the ability to write efficient, maintainable queries and work with structured business data

Experience building applications, workflows or analytics products on top of enterprise data platforms, relational databases, APIs or business systems

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

Experience building or implementing AI agents in code for use cases such as analytics, data management, application building or business process automation

Experience interacting with LLM provider APIs such as OpenAI, Anthropic or similar services, including tool use, structured outputs, evaluation and monitoring considerations

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

Strong habits around Git, testing, code reviews, CI/CD, documentation, monitoring and operational ownership

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

Ability to take ambiguity, shape it into a concrete technical plan, build quickly, learn from users and make pragmatic trade-offs

Strong communication, stakeholder management and business interaction skills, with the ability to work effectively with business users, product managers and technical colleagues

Familiarity with tools such as Microsoft Fabric, Databricks or Rayfin is helpful but not required

Exposure to Temporal or similar orchestrators is a plus, especially for multi-step, long-running or failure-prone workflows

Familiarity with tools such as LangChain, LangGraph, LangSmith or Langfuse is helpful but optional

Experience with vector databases, embeddings or retrieval-augmented generation is useful but not required

Experience with tools such as Sentry or Grafana is a plus

Required skills

Python, FastAPI, React, TypeScript, Relational Databases, SQL, Enterprise Data Platforms, System Architecture, AI Agent Development, LLM APIs, Workflow Orchestration, Git, Stakeholder Management

Preferred skills

Experience with tools such as Sentry or Grafana is a plus