AI Engineer

Entertainment Partners is hiring a Senior Software Engineer to build, train, evaluate, and deploy production AI and agentic systems for its intelligent product suite.

Responsibilities

• Design, develop, train, fine-tune, and evaluate machine learning models using PyTorch and related 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 LLM integrations for production use cases including NLP, information extraction, classification, and generation.
• Use parameter-efficient fine-tuning approaches (LoRA, QLoRA, PEFT) to adapt foundation models for EP domains including payroll, residuals, and production management.
• Design and implement RAG (Retrieval-Augmented Generation) using vector databases (pgvector, Pinecone, Weaviate) and semantic search pipelines.
• Optimize inference for latency and throughput with quantization, batching, and caching for production serving.
• Develop and maintain AI evaluation frameworks with automated evals as unit tests to support reliable, safe, production-grade behavior.
• 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 integrating with EP’s enterprise data and product APIs.
• Apply prompt engineering strategies including few-shot templates, chain-of-thought scaffolding, and structured output validation.
• Maintain EP AI quality engineering (QE) standards such as failure taxonomy, runtime guardrails, and evidence-driven release gates.
• Contribute to EP’s Enterprise Context Engine, a governed zero-data-retention AI context layer exposed via MCP to Tabnine Agent and Claude Code.
• Build and maintain MLOps infrastructure for training, experiment tracking (MLflow, Weights & Biases), versioning, and deployment.
• Containerize and deploy ML services with Docker and Kubernetes; integrate with CI/CD using GitHub Actions and Azure DevOps.
• Monitor model performance in production using drift detection, feedback loops, and automated retraining triggers.
• Ensure AI systems meet security, privacy, and compliance needs including data minimization and access control for sensitive payroll data.
• Collaborate with data engineering on 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.
• Work with product managers, UX designers, and full stack engineers to translate AI capabilities into product features.
• Conduct AI/ML code reviews focused on reproducibility, correctness, and production readiness.
• Mentor engineers on AI engineering fundamentals, LLM integration patterns, and responsible AI practices.
• Stay current with the AI/ML landscape, evaluate new models/frameworks, and assess applicability.
• Advance EP’s AI capability maturity by contributing to the PE AI Maturity Scorecard (S1–S3).
• Represent EP’s AI engineering practices in Architecture Review Board discussions.

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, including 3+ years focused on ML/AI engineering in production environments.
• Expert-level Python proficiency and deep familiarity with the Python ML/AI ecosystem.
• Hands-on production experience with PyTorch: nn.Module, custom training loops, autograd, GPU acceleration (CUDA), and model serialization (TorchScript, ONNX).
• Experience with Hugging Face libraries: Transformers, Datasets, and PEFT for fine-tuning/adaptation.
• Demonstrated ability building RAG pipelines including chunking, 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 agents and tool-calling workflows.
• Strong ML fundamentals: supervised/unsupervised learning, loss functions, regularization, evaluation metrics, and statistical validation.
• Experience with experiment tracking and reproducible workflows using MLflow, Weights & Biases, or Comet.
• Working knowledge of Docker and cloud ML services (AWS SageMaker, Azure ML, or OCI Data Science).
• Experience with SQL and NoSQL databases to design pipelines for ML training and inference.

Technologies

• Python, PyTorch, torchvision, torchaudio, torch.nn, torch.optim
• LoRA, QLoRA, PEFT
• RAG, pgvector, Pinecone, Weaviate
• quantization, MLflow, Weights & Biases, Docker, Kubernetes
• GitHub Actions, Azure DevOps
• LangChain, LangGraph, MCP (Model Context Protocol)
• Tabnine Agent, Claude Code
• TorchScript, ONNX
• Hugging Face Transformers, Hugging Face Datasets, CUDA
• OpenAI, Anthropic, vLLM, Ollama
• Comet, AWS SageMaker, Azure ML, OCI Data Science
• SQL, NoSQL, vector databases
• Triton Inference Server, TorchServe, Ray Serve
• TensorFlow, JAX, OpenCV, Kubeflow, KFServing
• Enterprise Context Engine, MLOps, CI/CD

Benefits

• Health, Dental, and Vision options
• 401(k) retirement savings plan and company match
• Paid holidays, vacation time, and sick time
• Participation in company equity plans
• Employee Assistance Program and mental health and wellness programs
• Training and development
• Annual bonus and merit reviews

Location & Compensation

• Tempe, AZ (hybrid)
• $140,000 - $180,000 per year

Preferred Qualifications

• 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).
• Hybrid work environment: Burbank, CA headquarters with flexible remote schedule.
• On-call availability as needed for production AI system incidents and model deployment events.
• Access to GPU-accelerated compute environments (cloud-based) for model training workloads.
• Ability to sit for extended periods of time at a computer workstation.
• Dexterity of hands and fingers to operate a computer keyboard and mouse.
• Occasional participation in early-morning or evening sessions to coordinate with distributed teams or international partners.