Job Description
We are seeking a hands‑on Data Scientist/AI Engineer/ML Engineer to design, build, evaluate, and deploy customer‑facing LLM applications—with a primary focus on retrieval‑augmented generation (RAG), agentic workflows, and production‑grade Azure deployments.
This role will be responsible for delivering a web‑enabled, customer‑facing chatbot that combines proprietary knowledge with live web search, integrates securely with enterprise systems, and meets high standards for accuracy, reliability, observability, and safety.
This is not a research‑only role. You will write production code, build evaluation harnesses, and own models and services from prototype through live deployment.
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
-4+ years of experience in Data Science, Machine Learning Engineering, or AI Engineering, with recent hands‑on work in Generative AI / LLMs.
-Strong proficiency in Python for production‑grade ML and AI services.
-Demonstrated experience building RAG‑based LLM applications beyond simple demos or notebooks.
-Hands‑on experience with vector databases or vector search systems (e.g., Azure AI Search, Pinecone, FAISS, etc.).
-Practical experience with prompt engineering, prompt chaining, and agent/tool orchestration.
-Experience designing LLM evaluation frameworks and quality metrics—not just manual testing.
Azure & Cloud Experience
-Production experience with Azure OpenAI and Azure‑based AI services.
-Experience deploying AI/ML services using Azure‑native infrastructure (Functions, App Services, Containers, CI/CD).
-Familiarity with observability and telemetry for AI systems (logging, metrics, tracing).
LLM Application Engineering
-Experience integrating external tools, APIs, or web search into LLM workflows.
-Understanding of LLM limitations, failure modes, and mitigation strategies.
-Ability to design systems that balance accuracy, latency, cost, and safety.
Nice to Have Skills & Experience
-Experience with LangChain, Semantic Kernel, LlamaIndex, or similar orchestration frameworks.
-Experience with hybrid search (keyword + vector) and reranking strategies.
-Familiarity with responsible AI, content filtering, and prompt safety patterns.
-Experience building customer‑facing chatbots or conversational AI systems at scale.
-Background in NLP, information retrieval, or applied ML research.
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.