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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technical Lead (Applied AI) - **Company:** X BY 2, LLC - **Location:** Detroit, MI, United States (Remote available) - **Experience:** Expert - **Salary:** $143,000.0 - $185,000.0 - **Contract:** Permanent contract - **Skills:** AI Evaluation, Java (Programming Language), JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Microsoft Azure, C Sharp (Programming Language), Code Review, Databases, Computer Engineering, Information Engineering, Software Design Patterns, Python (Programming Language), Machine Learning, Enterprise Messaging Systems, Performance Tuning, OpenAI, Tensorflow, Software Engineering, SQL Databases, Systems Integration, Pinecone, Enterprise Software Applications, Feature Engineering, Pytorch, LangChain, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Reliability of Systems, Technical Debt, Agentic-AI, Event Driven Architecture, Pgvector, Scikit Learn, Kubernetes, Information Technology, CrewAI, Data Analytics, Weaviate, AutoGen, Machine Learning Operations, Claude, Invoking Functions, Software Coding, GPT, Data Pipelines, Natural Language Generation, ChromaDB - **Published:** November 8, 2026 - **Apply:** https://www.juju.com/job/16_e6c2cc256 ## About the Role + 6+ years of experience in software engineering designing and developing enterprise applications, data/analytics solutions, and/or integration solutions + 1+ years of providing technical leadership as Tech Lead, Lead Engineer, or Architect + 1+ years in developing AI agents and/or AI model development and training * Education + Bachelor's Degree in Computer Science, Software Development, Software Engineering, or Computer Engineering + (Optional) Master's Degree or Minor in AI/Machine Learning or Statistics * AI Engineering Skills (Two or More) + Agentic AI: building and integrating autonomous AI agents using LLM APIs and orchestration frameworks (e.g., Anthropic Claude, OpenAI GPT, LangChain, CrewAI, AutoGen, MCP) + Machine Learning & Deep Learning: developing and training models using standard ML/DL frameworks (e.g., TensorFlow, PyTorch, scikit-learn) + Data Engineering & Feature Engineering: building data pipelines, performing feature engineering, and ensuring data quality across large, complex datasets + Context Engineering: Agentic SQL retrieval, MCP integration, agentic tool use, as well as vector databases & RAG techniques implementing retrieval-augmented generation patterns using vector stores (e.g., Pinecone, Weaviate, pgvector, ChromaDB), . + LLM Prompt Engineering & Fine-Tuning: designing and evaluating effective prompts, system instructions, and fine-tuning strategies for production LLM applications + AI Evaluation & Production Readiness: defining evaluation methods, testing model behavior, monitoring performance, and addressing reliability, safety, explainability, and auditability concerns * What We're Looking For + You move fluidly between writing code and leading a room - equally comfortable in a design review as you are in a pull request + You have strong opinions about architecture and aren't afraid to share them, but you know when to listen and adapt + You take ownership seriously - on small teams, your decisions have real impact and you're energized by that, not intimidated + You're genuinely curious about AI, not just checking a box - you've experimented with agentic systems, LLMs, or ML tools on your own terms + You communicate clearly with both engineers and non-technical stakeholders, and can translate complexity without dumbing it down ## Description * Technical Leadership & Architecture + Lead architecture, design, development, testing, and deployment of enterprise software solutions (applications, data, integration, AI agents, AI models) + Participate in strategy and roadmap discussions, architecture definition, and technology evaluations + Mentor engineers through design reviews, code reviews, and technical guidance + Quickly evaluate and adopt new tools, technologies, and platforms to build prototypes and proofs-of-concept + Drive adoption of AI solutions and collaborate with engineering teams, product leaders, and domain experts to deliver results * Software Engineering + Design, develop, and maintain scalable, production-grade enterprise applications using modern languages and frameworks (Python, Java, C#, JavaScript) + Define and enforce coding standards, best practices, and design patterns across the team + Build and maintain CI/CD pipelines, infrastructure-as-code, and cloud-deployed services (AWS, Azure) + Integrate enterprise systems via APIs, event-driven architectures, and messaging platforms + Identify and resolve performance bottlenecks, technical debt, and system reliability issues * Data & Analytics + Work with large, complex datasets and ensure data quality and integrity + Analyze data to generate insights that inform both model development and broader solution strategy + Collaborate with stakeholders to translate business problems into data-driven solutions * AI / Machine Learning Development + Design, develop, and train machine learning and deep learning models for healthcare and insurance use cases + Perform data modeling and feature engineering to support model development + Develop custom model metrics and approaches tailored to specific business problems + Ensure models are scalable, reliable, and integrated into production systems * Agentic AI Development + Design and develop LLM-powered workflows and agentic systems that help users complete complex tasks, retrieve information, reason over enterprise data, or interact with internal systems. + Integrate LLMs with tools, APIs, databases, documents, and enterprise platforms using patterns such as function calling, MCP, RAG, and structured tool use. + Architect orchestration patterns for planning, task decomposition, memory, context management, and human-in-the-loop review where appropriate. + Develop orchestration layers to manage agent planning, memory, task decomposition, and execution loops + Evaluate agentic systems for correctness, reliability, safety, observability, auditability, and harden them for production readiness. + Stay current with emerging AI frameworks and platforms, selecting tools pragmatically based on client needs.