AI Engineer

As an AI engineer on the AI team at Cerberus, you’ll work on high-impact projects that combine the pace of a startup with the reach of a global investment platform. Our team partners directly with internal investment desks as well as portfolio companies across industries to build and deploy machine learning systems that accelerate decision‑making and unlock business value.

You’ll design, implement and deploy production‑grade AI and ML systems, ranging from NLP pipelines that extract insights from complex documents to integrating models with third‑party services to streamline workflows.

We’re looking for AI engineers who care about impact: people who want to see their models not just trained, but deployed, adopted, and driving measurable results.

• Design and deliver AI systems:
  Build and deploy machine learning models and data‑driven products that directly impact investment decisions and portfolio company performance.
• Drive measurable impact:
  Partner with internal desks and portfolio teams to integrate ML products into their existing workflows to drive high adoption and value.
• Move fast and iterate:
  Work in an agile environment where experimentation, pragmatic engineering, and rapid iteration are key to creating business value.
• Leverage modern tools and methods:
  Use contemporary ML frameworks, cloud platforms, and MLOps best practices to build scalable, reusable solutions.
• Communicate insights clearly:
  Distill complex technical findings into concise, actionable narratives for technical and business audiences alike.
• Keep learning and pushing boundaries:
  Expand your engineering toolkit across the full ML development lifecycle—from prototyping to deployment—and explore new architectures, tools, and approaches to solving complex, real‑world problems.

• Generative AI for due diligence:
  Lead the rollout of our in‑house GenAI platform across investment desks to automate and accelerate due diligence. You’ll configure and extend the system for desk‑specific processes, run proof‑of‑value pilots, measure business impact, and collaborate closely with users to drive adoption and effectiveness.
• Automated Deal Sourcing Workflows:
  Prototype experimental systems to automate early‑stage deal sourcing. You’ll build integrations to extract signals from public and proprietary data sources, integrate with third‑party APIs to enrich lead information, and integrate with in‑house GenAI platform to create a structured data asset. This includes designing modular components for adaptability across investment strategies, running pilot deployments, and collaborating with users to refine workflows and measure sourcing efficiency.

• Strong technical foundation: Degree in a STEM field (or equivalent experience) with hands‑on expertise in applied statistics, machine learning, forecasting, NLP, computer vision, or optimization.
• Python expertise: Skilled in writing production‑grade code in Python (e.g., using type hints and understanding the limitations of the language) and in building data pipelines and ML models using modern libraries across multiple domains.
• Data science stack:
  Num Py, pandas / polars, scikit‑learn, XGBoost, LightGBM
• Deep learning:
  PyTorch, JAX
• Statistical programming:
  Num Pyro, PyMC
• Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for analytics and modeling workflows.
• ML Ops & deployment: Familiarity with deploying models into production using APIs or microservices, and applying ML Ops practices such as experiment tracking (e.g., MLflow, Weights & Biases), model versioning, and performance monitoring. Experience collaborating with engineering teams to ensure scalable and maintainable deployment.
• Backend & service development: Experience building production‑grade Python web services (e.g., FastAPI, Flask), developing APIs, and integrating ML components into broader systems.
• Software engineering practices: Comfortable with testing, code reviews, CI/CD pipelines, and version control (Git, Azure Dev Ops) beyond the very basics, ensuring reliable and maintainable codebases.
• Infrastructure & cloud: Familiarity…