AI Engineer

Job summary

At Ent*** 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.

Benefits

Qualifications

• 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.
• NEEDED
• Minimum

Responsibilities

• In addition to the following, other
• 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.

Skills

Certifications

Degrees

Work schedule

Industry

Company size