AI/ML Engineer
Alation is pioneering the next era of data intelligence powered by AI agents and trusted data. They are seeking an AI/ML Engineer to build intelligent systems and collaborate with enterprise customers to design AI applications. The role involves prototyping AI agents, developing AI-powered applications, and writing clean Python code.
Responsibilities
- Prototype AI Agents: Experiment with modern LLM and agent frameworks (e.g., LangChain, Pydantic-AI) — evaluate, benchmark, and refine performance with guidance from AI experts and researchers
- Collaborate with customers: Partner with technical stakeholders to understand real-world use cases and co-design practical solutions
- Build AI-powered applications: Develop agentic systems that combine structured data, language models, and automation to deliver measurable results
- Code with care: Write clean, well-structured Python code for integrations, pipelines, and intelligent systems
- Grow through teamwork: Work directly with product and platform teams to share insights from real deployments and help improve Alation’s AI platform
- Contribute reusable tools: Help create templates and patterns that speed up future implementations
Skills
- 1 - 3 years of related experience in a startup or rapidly growing and evolving business
- B.S. in Computer Science or related field (M.S. in Computer Science or related field preferred)
- Strong Python fundamentals, with experience writing and debugging production-quality code
- Curiosity and an understanding of LLMs or AI system development — from prompting to evaluation
- Comfort working with SQL and relational data; familiarity with tools like Snowflake, BigQuery, or Redshift is a plus
- Interest in working across the full lifecycle — from problem framing and prototyping to testing and deployment
- Clear communication skills and an ability to collaborate with both engineers and non-technical teams
- A growth mindset — you like learning new frameworks, exploring ideas, and solving open-ended problems
- Experience deploying AI or ML systems in production (e.g., observability, monitoring, and iterative improvement)
- Background in forward deployed engineering, solutions engineering, or AI consulting roles
- Familiarity with data infrastructure and MLOps concepts — APIs, orchestration, containerization, and cloud environments
- Understanding of enterprise data governance and access control principles
Company Overview