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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and building machine learning engineering solutions for pricing engines, with a focus on real-time, low-latency model deployment and optimization. Proficient in managing end-to-end ML workflows, including feature engineering, model serving, and collaboration with cross-functional teams.
Highest-signal resume keywords
Machine Learning Model ProductionPython ProgrammingRay (Serve, Train, Tune, Data)MLOps WorkflowsLinear Programming
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Machine Learning EngineeringFeature EngineeringModel ServingInference OptimizationDistributed TrainingLinear ProgrammingOffline Reinforcement LearningData Reading (Iceberg)APIs DevelopmentTesting and Clean Code
Soft Skills
MentoringTechnical Reference
Tools & Technologies
RayMLflowFeastRedisDockerClaude CodeGurobiHiGHSVLLMLiteLLM
Industry Keywords
MLOpsLow Latency ModelsReal-Time InferenceMulti-Tenant Architectures
Tech Stack
Tools & technologiesDockerPythonRayRedis
About the role
Key responsibilities & impact- Design and build the ML engineering for the pricing engine
- Develop inference serving, training pipelines, and feature engineering for real-time, low-latency models on Ray
- Design chained model pipelines on Ray Serve, including composition, low latency, and update strategies
- Build distributed training pipelines with Ray Train/Data, HPO with Ray Tune, and tenant-specific trained models with resilient checkpointing
- Define and materialize features in the Feast/Redis feature store, ensuring consistency between training and production
- Implement and optimize linear programming and offline RL components of the pricing pipeline
- Monitor modeling drift, validate versions, and deliver explainability with SHAP in partnership with MLOps
- Serve as a technical reference, mentor team members, and define viable, scalable solutions
- Manage handoffs with data scientists, receive data from data engineers, and deliver to the MLOps/Platform team for deployment and operations
Requirements
What you’ll need- Proven experience putting ML models into production
- Python and strong Software Engineering fundamentals (APIs, testing, clean code)
- Serving and inference optimization for low latency
- Familiarity with containers (Docker) and MLOps workflows (registry, deployment)
- Comfortable with AI-assisted development (Claude Code)
- Ray (Serve, Train, Tune, Data, RLlib)
- MLflow, Feast, and Redis
- Linear programming (Gurobi, HiGHS)
- Offline RL
- Iceberg data reading
- Nice to have: vLLM, LiteLLM, and multi-tenant architectures
Benefits
Comp & perks- Ongoing professional development
- Remote work
- Six-month project, with the possibility of extension or permanent employment