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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Insight Global - **Location:** Houston, TX, United States - **Contract:** Permanent contract - **Skills:** Multitier Architecture, A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Cloud Computing, Profiling, Encodings, Computer Programming, Databases, Continuous Integration, Data as a Services, Information Engineering, Github, Python (Programming Language), Logical Volume Manager, Machine Learning, MongoDB, NoSQL, NumPy, Tensorflow, Azure Machine Learning, SQL Databases, Enterprise Data Management, Data Processing, Google Cloud, Pytorch, Large Language Models, Snowflake, Prompt Engineering, Deep Learning, Generative AI, Indexer, Gitlab, Git, Matplotlib, Pyspark, Scikit Learn, Cassandra, HuggingFace, Xgboost, Bitbucket, GPT, Software Version Control, Databricks - **Published:** June 20, 2026 - **Apply:** https://www.juju.com/job/00000000g9nutz ## About the Role Microsoft Foundry & Foundry Agents, or LangGraph Expertise * Azure AI Foundry: Hands-on experience in designing, deploying, and managing GenAI solutions using Foundry orchestration and governance. * LangGraph: Proven ability to build agentic workflows, multi-step reasoning, and tool orchestration using LangGraph for enterprise-grade applications. Generative AI Expertise * Large Language/Vision Models (LLM/LVM): Hands-on with multiple providers/models (e.g., Gemini, GPT, Claude, Llama) and their APIs. * Retrieval Augmented Generation (RAG): Ability to design and implement robust RAG systems for real-time, context-aware applications. * Prompt engineering & agentic workflows: Advanced prompt design (system/task/reflection patterns) and building multi-step AI agents. * Vector databases/search & embeddings: Practical experience with vector indexing, similarity search, and embedding selection/management. Mathematics & Foundations * Statistics, Multivariate Calculus, Linear Algebra, Optimization (you can explain choices and trade-offs in model behavior based on these principles). Programming * Advanced Python (clean architecture, typing, packaging, testing; performance profiling and async where appropriate). Python Ecosystem * Data Handling/Visualization: pandas, NumPy, Seaborn/Matplotlib, PySpark * Machine Learning: scikit-learn, XGBoost, LightGBM * Deep Learning: TensorFlow or PyTorch * Generative AI: LangChain, LlamaIndex, Haystack, Hugging Face transformers Software Craftsmanship & Platforms * Version Control: Git (GitHub/GitLab/Bitbucket) * Databases: SQL and NoSQL (e.g., MongoDB, Cassandra) * Cloud: 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) * Enterprise data engineering platforms: Databricks, Snowflake ## Description * Design & 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. * Build robust RAG systems: Stand up end-to-end RAG pipelines (ingestion * chunking * embedding * retrieval * synthesis) leveraging Foundry orchestration and LangGraph agents, with observability, feedback loops, and AB testing for groundedness and hallucination control. * Develop 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. * Fine-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. * ML/LLM engineering: Build high-quality Python code, reusable libraries, and APIs; implement CI/CD, testing, experiment tracking, and model/version governance. * Data & platforms: Partner with data engineering to operationalize pipelines on enterprise platforms (e.g., Databricks/Snowflake) and integrate with cloud AI/ML services. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure](https://www.wearedevelopers.com/videos/1532-agentic-ai-from-theory-to-practice-developing-multi-agent-ai-systems-on-azure) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Building AI Applications with LangChain and Node.js](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Got AI ideas but no money? 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