Job Description
In this role, the individual will work hands‑on in Dynatrace or Datadog observability environments, with experience in a Dynatrace‑first environment preferred. As monitoring and observability teams merge, they will onboard and develop use cases across both the internal LLM monitoring platform and the broader observability system, ensuring all utilization and GenAI metrics are centralized in one place.
Day to day, the work includes building and supporting LLM‑driven Python workflows, integrating LLM monitoring tools such as LangSmith or LangFuse, and deploying services using PySpark, Spark, FastAPI, cloud platforms, and Kubernetes. The role is an individual contributor position focused on hands‑on development, strong Python coding, and supporting observability and GenAI use cases alongside other scientists and engineers.
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Required Skills & Experience
• The team is looking for individual contributors, not architects; they need hands-on scientists and developers.
• Candidates should have 6–8 years of experience; profiles with more than 10 years are typically not a fit.
• Candidates must have experience in Python programming and be able to complete a live coding test, including technical Python questions.
• Ideal candidates are hybrid scientists with a background in data engineering and the last 2–3 years focused on working with LLMs as data scientists.
• Required skills and keywords include: PySpark, Spark, strong Python, FastAPI, cloud deployment, and Kubernetes experience.
• Candidates should have platform experience and familiarity with LLM monitoring tools such as LangSmith, LangFuse, or similar.
• Dynatrace or Datadog experience for observability
-Python level 3 - write code - expert level 3 python questions - using LLMs in dtd python workflows
Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.