Senior AI Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior AI Engineer based in United States. As a Senior AI Engineer, you will build and evolve advanced AI capabilities that help customers turn research and information into actionable insights.

You will work across generative AI, large language models, enterprise RAG, agentic workflows, and machine learning systems.

Your work will span the full AI lifecycle, from experimentation and prototyping through production deployment, evaluation, and monitoring.

You will build secure, scalable, and dependable AI services that directly influence customer experiences and product quality.

The role combines hands-on engineering with strong technical ownership and opportunities to shape AI engineering standards and practices.

You will collaborate closely with Product, Engineering, Data Science, Data Engineering, and Analytics teams in a fully remote environment.

This position is open to candidates based in the US Eastern Time zone.

Accountabilities

• Design, build, and deploy generative AI capabilities across products, with a key focus on research and conversational AI experiences.
• Develop AI applications using large language models, retrieval-augmented generation, vector search, tool use, and agentic systems.
• Build reliable services and APIs that enable product teams to integrate AI capabilities into customer-facing experiences.
• Transform prototypes and experiments into production-ready systems with measurable performance and quality standards.
• Explore new AI-driven approaches for helping customers collect, understand, and act on information.
• Design and operate machine learning services and workflows using Python, Docker, Kubernetes, and AWS.
• Build reliable batch and real-time processing pipelines using technologies such as Kafka and Airflow.
• Design and implement vector database solutions supporting retrieval, recommendations, personalization, and semantic search.
• Use MLflow to manage experiments, model versions, registries, and deployments.
• Improve the reliability, scalability, performance, and cost efficiency of AI systems operating in production.
• Build automated evaluation pipelines for generative AI applications and conversational or analytical AI capabilities.
• Develop benchmarks covering accuracy, relevance, reliability, fairness, latency, and cost.
• Evaluate retrieval approaches, including chunking, embeddings, context selection, and reranking.
• Monitor production AI systems, identify quality and performance gaps, and implement improvements.
• Create safeguards to reduce unexpected AI behavior and help protect customer data.
• Establish reusable engineering patterns and technical standards for building, evaluating, deploying, and monitoring AI systems.
• Help teams make informed decisions regarding models, frameworks, infrastructure, performance, scalability, and cost.
• Apply strong engineering practices across testing, security, observability, version control, and deployment.
• Share technical knowledge and contribute to the growth and development of other engineers.
• Stay current with relevant AI research, tools, and engineering practices and apply valuable developments where appropriate.
• Partner with Product, Engineering, Data Science, Data Engineering, and Analytics teams to align AI initiatives with customer and business needs.
• Work with Data Scientists to turn experiments and models into reliable production services.
• Clearly communicate technical concepts, risks, and tradeoffs to both technical and nontechnical stakeholders.
• Contribute to technical planning and help shape the broader direction of AI engineering.

Requirements

• At least 4 years of experience building and deploying machine learning or AI systems in production.
• Strong Python programming and software engineering skills.
• Experience building production services with Python frameworks such as FastAPI.
• Hands-on experience developing generative AI applications using large language models, RAG, tool use, or agentic systems.
• Experience with frameworks such as PyTorch, LangChain, LangGraph, or comparable technologies.
• Strong understanding of enterprise RAG systems, including chunking, embeddings, retrieval, reranking, evaluation, and monitoring.
• Experience creating automated evaluation frameworks and pipelines for generative AI app…