AI Engineer

Entertainment Partners•Tempe, AZ

9d•Hybrid

About The Position

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.

Requirements

Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field.
6–10+ years of professional software engineering experience, with a minimum of 3+ years focused on ML/AI engineering in production environments.
Expert-level proficiency in Python; deep familiarity with the Python ML/AI ecosystem.
Hands-on production experience with PyTorch — model definition (nn.Module), custom training loops, autograd, GPU acceleration (CUDA), and model serialization (TorchScript, ONNX).
Experience with Hugging Face Transformers, Datasets, and PEFT libraries; ability to fine-tune and adapt foundation models.
Demonstrated experience building RAG pipelines, including chunking strategies, embedding models, vector store selection, and retrieval evaluation.
Production experience integrating LLM APIs (OpenAI, Anthropic, open-source via vLLM/Ollama) and building reliable prompt engineering systems.
Experience with LangChain or LangGraph for multi-step agent and tool-calling workflows.
Strong understanding of ML fundamentals: supervised/unsupervised learning, loss functions, regularization, evaluation metrics, and statistical validation.
Experience with experiment tracking tools (MLflow, Weights & Biases, Comet) and reproducible ML workflows.
Working knowledge of containerization (Docker) and cloud ML services (AWS SageMaker, Azure ML, or OCI Data Science).
Experience with SQL and NoSQL databases; ability to design data pipelines for ML training and inference.

Nice To Haves

Experience with additional deep learning frameworks (TensorFlow, JAX) or framework interoperability (ONNX).
Familiarity with computer vision (torchvision, OpenCV) or speech/audio processing (torchaudio) domains.
Experience with model compression techniques: quantization (INT8, FP16, BF16), pruning, distillation.
Experience serving ML models at scale using Triton Inference Server, TorchServe, Ray Serve, or similar.
Contributions to open-source ML projects or published research (papers, patents, or technical blog posts).
Experience with responsible AI frameworks, bias evaluation, and AI governance practices.
Familiarity with MCP (Model Context Protocol) server development for exposing tools to AI agents.
Prior domain experience in payroll, fintech, media, or enterprise SaaS environments.
Experience with Kubernetes-based ML workload orchestration (Kubeflow, KFServing, or similar).

Responsibilities

Design, develop, train, fine-tune, and evaluate machine learning models using PyTorch and associated ecosystem libraries (torchvision, 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 LangChain, LangGraph, 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 (GitHub Actions, Azure DevOps).
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 practices in Architecture Review Board discussions.