Job Summary
Seeking a Senior AI Engineer to design, build, and operate production-grade AI solutions, including AI agents, retrieval-augmented generation (RAG), and LLM-powered automation. The role will take AI use cases from discovery through production while ensuring solutions are reliable, secure, scalable, and cost-effective. The ideal candidate will have strong software, data, and AI engineering expertise with hands-on experience delivering LLM and Generative AI solutions in production environments.

Key Responsibilities
• Design and build AI agents and multi-step agentic workflows that automate business processes.
• Integrate LLMs with internal systems and data through APIs and MCP (Model Context Protocol).
• Build RAG, vector search, and embedding pipelines across documents and knowledge bases.
• Develop document processing pipelines to extract, validate, and structure data.
• Implement output validation, AI evaluation frameworks, and human-in-the-loop review processes.
• Build AI observability capabilities covering quality, latency, failure rates, and cost, with appropriate alerting.
• Collaborate with data engineering teams to develop AI-ready data pipelines and data models.
• Enforce data security, privacy, governance, and appropriate PII handling across AI solutions.
• Own CI/CD, Infrastructure as Code, and deployment processes for AI services.
• Work with business stakeholders to scope AI use cases and define measurable success criteria.
• Create and maintain architecture decision records and technical documentation.
• Conduct code reviews and mentor engineers on AI engineering practices and solutions.
• Monitor and optimize production AI solutions for reliability, performance, and cost efficiency.

Required Qualifications
• 8+ years of experience in software, data, or machine learning engineering, including 2+ years delivering LLM or Generative AI solutions to production.
• Strong proficiency in Python and SQL.
• Hands-on experience with LLM APIs such as OpenAI, Anthropic, or Azure OpenAI.
• Experience with prompt engineering and structured outputs.
• Proven experience building RAG solutions, vector databases, and embedding pipelines.
• Experience building AI agents and agentic workflows.
• Strong data engineering background with cloud data platforms such as Snowflake, Databricks, or similar technologies.
• Experience with data orchestration tools such as Airflow and data transformation frameworks such as dbt.
• Production experience with AWS, Azure, or GCP.
• Experience with Docker, CI/CD, and Terraform.
• Experience with GitHub Actions and Git-based development workflows.
• Experience with Datadog or similar observability and monitoring platforms.
• Strong understanding of data security, privacy, and governance.
• Strong communication skills with both technical and non-technical audiences.

Preferred Qualifications
• Experience building MCP servers or gateways.
• Experience with intelligent document processing or OCR technologies.
• MLOps experience, including feature stores, model deployment, and GPU inference.
• Experience with AI evaluation, observability, and cost optimization.
• Experience in financial services or another regulated industry.
• Master's degree in Computer Science, Artificial Intelligence, or a related field.
• Cloud or data platform certifications such as AWS ML, Snowflake, or Azure AI.
• Regular use of AI-assisted development tools such as Claude Code or GitHub Copilot.