Senior Software Engineer Data & AI

Key Responsibilities

• Design & Build AI-Powered Features: Architect, implement, and ship data-driven product features, always starting with the customer problem first. From prototyping through production, you’ll apply the right level of technology (whether simple heuristics or advanced AI) to deliver impact.
• Build for Production: Build maintainable and scalable systems, including writing clean, testable code, integrating with new and existing data pipelines, and deploying solutions and services into cloud environments.
• End to End Solution Delivery: Operate as an end-to-end developer in a fast paced startup. This involves identifying opportunities, prototyping solutions, securing stakeholder support, and productionalizing features that deliver measurable business value.
• Applied AI Innovation: Research and apply modern methods (AI agents, LLMs, RAG, embeddings, supervised/unsupervised learning) to power new product capabilities, prioritizing impact and speed over perfection.
• Enhance User Experience Through AI: Translate business problems into AI solutions that improve user workflows, accelerate insights, and drive measurable value.
• Collaborate Cross-Functionally: Work with business stakeholders and other engineers to turn customer problems into technical requirements and impactful features.
• Promote Best Practices: Establish and evangelize standards for AI/ML engineering and data quality across the team.

Qualifications

• 5+ years of professional experience in software engineering or data engineering, with at least 3+ years building AI/ML-driven products.
• Strong programming skills in Python and proficiency in SQL.
• Experience with modern AI frameworks (eg HuggingFace, LangChain, etc.) and designing data pipelines (Airflow, Dagster, or equivalent).
• Experience applying LLMs and generative AI in production, including prompt engineering, RAG, and LLM deployment.
• Solid understanding of cloud-based infrastructure (AWS, GCP, or Azure).
• Strong grasp of fundamental data science and machine learning techniques, with the ability to balance research and engineering tradeoffs.
• Demonstrated ability to translate business problems into data solutions that drive business value.
• Proven track record of shipping user-facing features with measurable impact.
• Excellent written and verbal communication skills; ability to collaborate across data, engineering, and product teams.
• Startup experience preferred.