Applied AI Engineer/Model Engineer

Tasks

• Build and deploy production-ready LLM applications, knowledge assistants, document intelligence solutions, and AI workflow agents.
• Develop RAG pipelines using embeddings, vector databases, semantic search, chunking, reranking, and response grounding.
• Integrate AI services with AWS-hosted enterprise platforms, model APIs, data services, and authentication systems.
• Manage the end-to-end LLMOps/MLOps lifecycle, including deployment, monitoring, evaluation, rollback, and continuous improvement.
• Collaborate with cloud engineering teams on Terraform, Infrastructure as Code (IaC), CI/CD pipelines, and release management.
• Optimize models and inference workloads for latency, throughput, reliability, and cost.
• Implement AI observability, security, privacy, Responsible AI, and model risk controls.
• Develop reusable AI components, APIs, prompts, and production documentation.

Requirements

• 7+ years of experience in AI/ML engineering, software engineering, platform engineering, or applied machine learning.
• Strong hands-on experience with LLMs, Transformers, embeddings, RAG, semantic search, and Generative AI.
• Proficiency in Python and frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, or Semantic Kernel.
• Experience deploying production AI services using APIs, containers, Kubernetes, CI/CD, and monitoring tools.
• Practical experience with AWS AI/cloud services or comparable cloud-native AI deployment platforms.
• Knowledge of model optimization tech

Schedule

• Full-Time

Location

• New York / New Jersey-Hybrid
• Tempe, AZ
• Brea, CA
• Tampa, FL
• Los Angeles, CA (potential)
• San Francisco, CA (potential)
• Walnut Creek, CA (potential)