SumerSports

Application Engineer

• Design and build AI-powered user features: Implement prompt-based and retrieval-augmented systems (RAG) that answer complex sports questions and generate insights.
• Build agents and workflows that combine deterministic logic with LLM reasoning.
• Prototype and iterate fast: Use prompting, tool orchestration, and retrieval design to rapidly build and refine AI behaviors.
• Collaborate with Eval Engineers to build golden sets and automate quality checks before release.
• Work closely with the LLMOps Platform team: Use shared eval frameworks, prompt registries, and model gateways.
• Provide feedback loops to improve platform reliability, latency, and safety.
• Integrate with deep learning and data systems: Combine structured stats, tracking data, and video-derived features into AI-powered applications.
• Build APIs and UI layers that expose insights to coaches, teams, and partners.
• Push the limits of applied AI: Explore how LLMs, retrieval, and deterministic logic can create novel sports analytics tools.
• Use AI internally to accelerate development (code generation, testing, debugging).

AI Application Engineer

Platform Engineer

• Build and operate the LLM Platform: Develop model routing, prompt registry, and orchestration services for multi-model workflows.
• Integrate external LLM APIs (OpenAI, Anthropic, Mistral) and internal finetuned models.
• Enable fast, safe experimentation: Implement automated evaluation pipelines (offline + online) with golden sets, rubrics, and regression detection.
• Support CI/CD for prompt and model changes, with rollback and approval gates.
• Collaborate cross-functionally: Partner with product pods to instrument RAG pipelines and prompt versioning.
• Work with deep learning and data teams to integrate structured and unstructured retrieval into LLM workflows.
• Optimize performance and cost: Profile latency, token usage, and caching strategies.
• Build observability and monitoring for LLM calls, embeddings, and agent behaviors.
• Ensure reliability and safety: Implement guardrails (toxicity, PII filters, jailbreak detection). Maintain policy enforcement and audit logging for AI usage.

LLMOps Platform Engineer

Data Scientist

• Build and implement analytical football models, providing valuable insights to enhance our football analytics offerings
• Conduct comprehensive data analysis to extract actionable insights
• Ensure seamless deployment of machine learning and statistical models using Python, Databricks, and Spark
• Handle large datasets efficiently using Spark and Databricks, ensuring data integrity and performance optimization
• Work closely with other data scientists, analysts, and stakeholders to understand their needs and deliver high-quality analytical solutions
• Identify and implement new tools, techniques, and best practices to enhance our data analytics capabilities

Senior Football Data Scientist

Data Engineer

• Build and operate robust data pipelines for ingestion, cleaning, and transformation using Databricks, Airflow, or Dagster
• Develop efficient ETL/ELT workflows in Python and SQL to support both batch and streaming workloads
• Collaborate with ML and AI teams to deliver high-quality datasets for training, evaluation, and production features
• Model and maintain structured data assets (Delta, Parquet, Iceberg) for reliability, versioning, and lineage tracking
• Implement orchestration and monitoring — schedule jobs, track dependencies, and automate recovery from failures
• Ensure data quality and compliance through validation frameworks, schema enforcement, and audit logging
• Contribute to data platform evolution — evaluate tools, standardize best practices, and improve developer experience
• Support performance and cost optimization across compute, storage, and orchestration systems

• Design and operate ML infrastructure: Manage data, training, serving, and inference systems for high-throughput model workflows
• Build scalable pipelines: Implement reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking
• Optimize compute and cost: Tune GPU and CPU workloads, manage clusters, and drive efficiency via rightsizing, spot scheduling, and caching
• Serve models in production: Operate APIs for low-latency inference with autoscaling, blue-green or canary rollouts, and rollback safety
• Ensure reliability and observability: Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality
• Secure and automate: Manage IAM, secrets, and container security; automate deployment pipelines via CI/CD and infrastructure as code
• Collaborate cross-functionally: Partner with research scientists and AI engineers to deliver models from experiment to production with minimal friction
• Document and enable: Build templates, runbooks, and internal tooling that make ML workflows repeatable, safe, and fast

AI Application Engineer

LLMOps Platform Engineer

Senior Football Data Scientist

Data Engineer

MLOps, ML Platform Engineer