AI Engineer at EP | Central Casting

Tempe, Arizona, United States -

Full Time

Start Date

Immediate

Expiry Date

12 Oct, 26

Salary

180000.0

Posted On

14 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

PyTorch, Large Language Models, Python, RAG, LangChain, LangGraph, MLOps, Transformer Architectures, Fine-tuning, Vector Databases, Docker, Kubernetes

Industry

Entertainment Providers

Description

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. KEY RESPONSIBILITIES In addition to the following, other duties may be assigned to meet business needs. AI / ML Engineering * 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. LLM Integration & Agentic Systems * 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. * MLOps & Production Engineering * 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. Collaboration & Technical Leadership * 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. JOB REQUIREMENTS / QUALIFICATIONS NEEDED Minimum qualifications: * 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. 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. * Sitting 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. Other benefits and perks included are: * 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, mental health and wellness programs * Training and development * Annual bonus and merit reviews The salary range for this position in $140,000 to $180,000 and will be commensurate with experience related to the position. Entertainment Partners seeks to employ the most qualified individuals from the available workforce and to provide equal employment opportunity for all persons. Our policy prohibits unlawful discrimination based on race, color, religion, religious creed, sex, gender identity/expression, age, pregnancy, citizenship status, marital status, national origin or ancestry, physical or mental disability (whether perceived or actual), medical condition (cancer-related or genetic characteristics-related), sexual orientation, veteran status, medical/family care leave status or any other consideration made unlawful by applicable federal, state, or local laws. Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Equal opportunity extends to all aspects of the employment relationship, including recruiting, hiring, transfers, promotions, training, terminations, working conditions, compensation, benefits, and other terms and conditions of employment.

Responsibilities

Design, train, and deploy AI/ML models and agentic systems to power the company's intelligent product suite. This includes implementing RAG architectures, optimizing model inference, and maintaining MLOps infrastructure.