AI Engineer

Job Description u2022u2003Design & deliver GenAI solutions: Architect and implement LLM/LVM applications (text and, where applicable, vision) with strong emphasis on Microsoft Azure AI Foundry capabilities and LangGraph-based agentic workflows. This includes advanced prompt strategies, guardrails, evaluation metrics, cost/latency optimization, and production rollout. u2022u2003Build robust RAG systems: Stand up end-to-end RAG pipelines (ingestion u2192 chunking u2192 embedding u2192 retrieval u2192 synthesis) leveraging Foundry orchestration and LangGraph agents, with observability, feedback loops, and AB testing for groundedness and hallucination control. u2022u2003Develop agentic workflows: Implement multi-step, tool-using agents using LangGraph for real-time, context-aware operations; orchestrate planning, memory, and tool calling with safe execution policies. u2022u2003Fine-tune foundation models: Select, adapt, and fine-tune open and hosted models for domain-specific tasks using efficient techniques (LoRA/QLoRA, PEFT, parameter-efficient adapters), and manage evaluation datasets. u2022u2003ML/LLM engineering: Build high-quality Python code, reusable libraries, and APIs; implement CI/CD, testing, experiment tracking, and model/version governance. u2022u2003Data & platforms: Partner with data engineering to operationalize pipelines on enterprise platforms (e.g., Databricks/Snowflake) and integrate with cloud AI/ML services. 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 learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: Skills and Requirements Microsoft Foundry & Foundry Agents, or LangGraph Expertise u2022u2003Azure AI Foundry: Hands-on experience in designing, deploying, and managing GenAI solutions using Foundry orchestration and governance. u2022u2003LangGraph: Proven ability to build agentic workflows, multi-step reasoning, and tool orchestration using LangGraph for enterprise-grade applications. Generative AI Expertise u2022u2003Large Language/Vision Models (LLM/LVM): Hands-on with multiple providers/models (e.g., Gemini, GPT, Claude, Llama) and their APIs. u2022u2003Retrieval Augmented Generation (RAG): Ability to design and implement robust RAG systems for real-time, context-aware applications. u2022u2003Prompt engineering & agentic workflows: Advanced prompt design (system/task/reflection patterns) and building multi-step AI agents. u2022u2003Vector databases/search & embeddings: Practical experience with vector indexing, similarity search, and embedding selection/management. Mathematics & Foundations u2022u2003Statistics, Multivariate Calculus, Linear Algebra, Optimization (you can explain choices and trade-offs in model behavior based on these principles). Programming u2022u2003Advanced Python (clean architecture, typing, packaging, testing; performance profiling and async where appropriate). Python Ecosystem u2022u2003Data Handling/Visualization: pandas, NumPy, Seaborn/Matplotlib, PySpark u2022u2003Machine Learning: scikit-learn, XGBoost, LightGBM u2022u2003Deep Learning: TensorFlow or PyTorch u2022u2003Generative AI: LangChain, LlamaIndex, Haystack, Hugging Face transformers Software Craftsmanship & Platforms u2022u2003Version Control: Git (GitHub/GitLab/Bitbucket) u2022u2003Databases: SQL and NoSQL (e.g., MongoDB, Cassandra) u2022u2003Cloud: Hands-on with one or more of AWS, Azure, GCP, specifically AI/ML & data services (e.g., AWS SageMaker, Azure Machine Learning, Google Vertex AI) u2022u2003Enterprise data engineering platforms: Databricks, Snowflake

Created: 2026-03-20