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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineers - **Company:** Truveta Inc. - **Location:** Seattle, United States - **Experience:** Expert - **Salary:** $175,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Microsoft Excel, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Engineering, Software Quality, Python (Programming Language), Performance Tuning, Search Technologies, Systems Integration, Google Cloud, Large Language Models, Multi-Agent Systems, Information Technology, Machine Learning Operations - **Published:** September 3, 2026 - **Apply:** https://www.dice.com/job-detail/2f1ec5aa-9223-4d2a-becb-48bcf11d5c74 ## About the Role * 5+ years of experience building and deploying scalable, production-ready ML systems in a collaborative engineering environment. * Proficiency with agentic AI frameworks (e.g., LangGraph, AutoGen, CrewAI) and understanding of interoperability protocols such as MCP and A2A. * Hands-on experience fine-tuning and optimizing large language models (LLMs) or multimodal models, using techniques such as LoRA, PEFT, and TRL. * Experience working with vector databases and embeddings, integrating FAISS, Pinecone, Chroma, or Azure AI Search into retrieval-augmented generation (RAG) or semantic-search pipelines. * Strong software engineering fundamentals, with proficiency in Python and experience designing scalable systems in modern cloud environments (Azure, AWS, or Google Cloud Platform). * B.S. or M.S. in Computer Science, Artificial Intelligence, or a related technical field. ## Description * Design and reason with agentic AI frameworks - experienced in building multi-agent workflows using frameworks such as LangGraph, AutoGen, or CrewAI, integrating reasoning, planning, and memory to create intelligent, goal-driven systems. * Bring a deep understanding of LLM fundamentals - knowledgeable in transformer architectures, attention mechanisms, and tokenization principles. You understand how embeddings, context windows, and model scaling laws influence quality, cost, and performance. * Work fluently with embeddings and vector stores - experienced in building or integrating retrieval-augmented generation (RAG) pipelines, managing vector databases (e.g., FAISS, Pinecone, Chroma, Azure AI Search, or similar), and leveraging semantic search or context injection to enhance reasoning. * Excel at model fine-tuning - with hands-on expertise fine-tuning large language models (LLMs) or multimodal models using supervised, reinforcement, or instruction-tuning techniques (e.g., LoRA, PEFT, TRL). Skilled at optimizing model efficiency, interpretability, and inference performance. * Think and build like engineers - grounded in strong software design principles, modular architecture, and code quality. You can translate experimental ideas into robust, maintainable systems that integrate seamlessly within large-scale AI platforms. * Collaborate across boundaries - partnering with platform, application, and product engineers to transform concepts into scalable, reliable AI solutions. You communicate clearly, share knowledge openly, and thrive in cross-functional teams. * Demonstrate senior-level ownership - capable of setting technical direction, mentoring peers, and making pragmatic design trade-offs that balance innovation, performance, and reliability. * Understand evaluation deeply - fluent in ML validation and measurement (Precision, Recall, Specificity, NPV, etc.), and experienced designing reward functions, evaluators, or grader models for reinforcement fine-tuning and continuous improvement. * Adapt and learn continuously - staying ahead of evolving AI architectures, agentic frameworks, and emerging paradigms in reasoning and retrieval. You have curiosity, humility, and a bias for iteration. * Act with purpose - applying thoughtful engineering and ethical AI principles to create systems that advance healthcare intelligence responsibly and at scale. ## Related Videos - [In-depth .NET Azure Functions: Isolated mode, performance and durable AI agents](https://www.wearedevelopers.com/videos/100207-in-depth-net-azure-functions-isolated-mode-performance-and-durable-ai-agents) - [Developer Tools for Microsoft Azure](https://www.wearedevelopers.com/videos/450-developer-tools-for-microsoft-azure) - [The Cloud is Calling: Answer with In-Demand Skills](https://www.wearedevelopers.com/videos/945-the-cloud-is-calling-answer-with-in-demand-skills) - [Best practices: Building Enterprise Applications that leverage GenAI](https://www.wearedevelopers.com/videos/1513-best-practices-building-enterprise-applications-that-leverage-genai) - [Inside the AI Revolution: How Microsoft is Empowering the World to Achieve More](https://www.wearedevelopers.com/videos/869-inside-the-ai-revolution-how-microsoft-is-empowering-the-world-to-achieve-more) - [Develop enterprise-ready applications for Microsoft Teams with Azure resources on modern web technologies](https://www.wearedevelopers.com/videos/187-develop-enterprise-ready-applications-for-microsoft-teams-with-azure-resources-on-modern-web-technologies) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)