AI Engineer

Role Summary
Develop, implement, and scale machine learning and generative AI solutions that deliver measurable business outcomes. This role blends strong software engineering expertise with hands-on experience building applications powered by modern AI frameworks and large language models. You will take models and AI systems from prototype through production release, with a focus on reliability, performance, and operational excellence.

Responsibilities

• Build and deploy machine learning and deep learning models using production-ready engineering practices
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, including embeddings, chunking strategies, and reranking
• Integrate LLM APIs into application workflows and orchestration layers
• Develop and maintain AI-powered APIs using FastAPI to support scalable system access
• Operationalize ML pipelines, including monitoring and continuous performance evaluation in production
• Use vector databases and semantic search techniques to improve retrieval quality and downstream results
• Evaluate model and LLM performance using appropriate frameworks and testing approaches
• Optimize AI-driven workloads for cost and latency while maintaining quality targets
• Containerize services and support deployments using Docker and related best practices
• Collaborate with cross-functional stakeholders to translate technical decisions into business impact

Qualifications

• Strong proficiency in Python and SQL; experience with Java or JavaScript is a plus
• Hands-on experience developing and deploying machine learning models, including deep learning solutions
• Strong understanding of model selection, evaluation methods, and performance tuning
• Experience building and deploying APIs with FastAPI
• Familiarity with containerization and deployment using Docker
• Experience integrating LLM APIs and using at least one orchestration framework such as LangChain or LangGraph
• Exposure to enterprise AI platforms such as Azure ML, Snowflake Cortex, and/or MLflow
• Knowledge of vector databases and semantic search techniques
• Practical experience implementing RAG architectures, including embeddings, chunking, and reranking
• Ability to communicate technical concepts clearly in a business context

Publishing Pay Range: $60.00 - $67.00 Hourly
This position is based in office and requires employee to work on-site.