DevOps, Infra & QA Engineer

**About Scispot:**
Scispot is building the world’s first AI scientist for labs. This intelligent agent connects directly to instruments—like HPLC systems, sequencers, and plate readers. It pulls raw data, then cleans, normalizes, and calibrates it. Next, it tags each result with experiment metadata and runs QA checks. Finally, it loads the refined data into our platform’s unified data model.

Over time, the AI scientist learns from each run. It spots anomalies, suggests corrections, and highlights emerging trends. Labs save hours of manual work. Scientists focus on breakthroughs — not busywork.

Key Responsibilities:

This is a hands on technical role. Candidate should be proficient in Java, Python and React. This candidate will own product release lifecycle.

• Develop full stack automation for AI pipelines, ETL
• Develop and maintain our end to end EKS stack
• Develop unit test automation
• Develop white box testing automation
• Develop evals for AI pipelining
• Translate customer needs into clear, actionable product specs.
• Act as the main liaison between Engineering, Design, and Customer Success to sign off every release
• Be comfortable with prompt engineering to create clear acceptance criteria for every release.
• Set up and manage automated QA/QC pipelines (test automation, CI/CD).
• Define and document processes to boost product delivery and quality.
• Track execution across teams and keep timelines on point.
• Define and run evaluations for our AI scientist agent to perform ELT tasks.
• Design metrics and tests for AI agent accuracy, speed, and reliability.

Required Skills:

• Experience with AWS, EKS, Networking, Messaging (e.g. Rabbit MQ), Fast APIs, Lambda
• Knowledge of graph databases and Kubernetes
• Strong grasp of the software development lifecycle and QA best practices.
• Hands-on with CI/CD tools (GitHub Actions, CircleCI, GitLab CI).
• Experience with test automation frameworks (Cypress, Playwright, Selenium).
• Ability to write simple scripts (Bash, Python) and configure YAML pipelines.
• Proven skill in evaluating AI agents and their performance.
• Excellent cross-functional communication and organization skills.

Preferred Experience:

• Background in Product Ops, Technical Program Management, or DevOps-focused roles.
• Prior work in a SaaS or high-growth startup.
• Familiarity with monitoring and alerting tools (Sentry, Datadog).

Join us in shaping the future of lab automation. Become part of our mission to turn every lab into an AI powerhouse.

Introductory call followed by a case study