Job DescriptionEntertainment 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 RESPONSIBILITIESIn 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 NEEDEDMinimum 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.
