AI Engineer

About the job

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.

We’re a small, high-impact team with a broad remit and diverse technical backgrounds. We don’t expect any single candidate to check every box below - if your experience overlaps strongly with what we do and you’re excited to apply your skills in a fast-moving, real-world environment, we’d love to hear from you.

• 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 (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.,…