AI Engineer

At Entertainment Partners and Central Casting, we are committed to creating an environment where every employee is seen, where ideas, thoughts and perspectives are shared openly, and where fearless innovation is encouraged. Weaving diversity, equity, and inclusion into who we are will drive our competitiveness by encouraging creativity and enhanced decision making.

We help to power Oscar-winning films, Emmy-winning shows, and Clio-winning commercials. Feel the satisfaction of doing work that directly impacts the most exciting industry in the world. EP is poised to redefine and evolve the back-office processes of the entertainment community with security at the core of what we do.

Are you looking for the next opportunity to revolutionize an industry? If so....

Entertainment Partners (EP) is seeking a Senior Software Engineer specializing in AI and Machine Learning to join our AI Services organization. This role sits at the intersection of applied ML engineering, LLM product development, and production-grade system design. The AI Senior Software Engineer is responsible for building, training, evaluating, and deploying AI/ML models and agentic systems that power EP's intelligent product suite --- including Rosey Intelligence, Project Florence, and EP Answers.

The ideal candidate brings deep hands-on expertise in PyTorch, transformer architectures, and the full ML lifecycle, combined with the software engineering discipline required to ship reliable AI products at scale in a production entertainment technology environment.

In addition to the following, other duties may be assigned to meet business needs.

• Design, develop, train, fine-tune, and evaluate machine learning models using PyTorch and associated ecosystem libraries (torch vision, torchaudio, torch.nn, torch.optim).
• Build and maintain ML training pipelines, experiment tracking workflows, and model evaluation frameworks.
• Implement transformer-based models and large language model (LLM) integrations for production use cases including NLP, information extraction, classification, and generation.
• Apply parameter-efficient fine-tuning techniques (LoRA, QLoRA, PEFT) to adapt foundation models for EP-specific domains (payroll, residuals, production management).
• Design and implement RAG (Retrieval-Augmented Generation) architectures using vector databases (pgvector, Pinecone, Weaviate) and semantic search pipelines.
• Optimize model inference for latency and throughput; implement quantization, batching, and caching strategies for production serving.
• Develop and maintain AI evaluation frameworks --- including automated evals as unit tests --- to ensure model behavior is reliable, safe, and production-grade.

• Design and implement LLM-powered agentic workflows using Lang Chain, Lang Graph, and EP's internal MCP (Model Context Protocol) server architecture.
• Build multi-step reasoning pipelines, tool-calling agents, and autonomous task execution systems that integrate with EP's enterprise data and product APIs.
• Implement prompt engineering strategies, few-shot templates, chain-of-thought scaffolding, and structured output validation.
• Apply and maintain EP's AI quality engineering (QE) standards including failure taxonomy, runtime guardrails, and evidence-driven release gates.
• Contribute to EP's Enterprise Context Engine --- the governed, zero-data-retention AI context layer exposed via MCP to Tabnine Agent and Claude Code.

• Build and maintain MLOps infrastructure for model training, experiment tracking (MLflow, Weights & Biases), versioning, and deployment.
• Containerize and deploy ML services using Docker and Kubernetes; integrate with CI/CD pipelines (Git Hub Actions, Azure Dev Ops).
• Monitor model performance in production; implement drift detection, feedback loops, and automated retraining triggers.
• Ensure AI systems meet EP's security, privacy, and compliance requirements including data minimization and access control for sensitive payroll data.
• Collaborate with the data engineering team to design and maintain feature stores, data pipelines, and training data infrastructure.

• Partner with the Chief Architect AI & Data and CAIO to define AI architecture patterns and best practices for the EP engineering organization.
• Collaborate with product managers, UX designers, and full stack engineers to translate AI capabilities into well-designed product features.
• Conduct code reviews for AI/ML code with a focus on reproducibility, correctness, and production readiness.
• Mentor engineers across the organization in AI engineering fundamentals, LLM integration patterns, and responsible AI practices.
• Stay current with the rapidly evolving AI/ML landscape; evaluate new models, frameworks, and techniques for potential application at EP.
• Contribute to EP's PE AI Maturity Scorecard (S1--S3) by advancing the organization's AI capability maturity.
• Represent EP's AI engineering…