Technical Lead (Applied AI)

X by 2•Metro Detroit, MI

2d•$143,000 - $185,000•Hybrid

About The Position

X by 2 is a technology consulting firm specializing in healthcare and insurance transformation. For over 25 years, we have partnered with leading organizations across North America — from strategy and architecture to system modernization, data analytics, and AI — delivering high-impact solutions from start to finish. We're agile, collaborative, and deeply committed to doing consulting the way it was meant to be done. We are looking for a Technical Lead who will lead the design and development of AI solutions within enterprise environments for our clients. You'll combine hands-on engineering with leadership, guiding architecture decisions, mentoring teams, and building solutions.

Requirements

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
Bachelor's Degree in Computer Science, Software Development, Software Engineering, or Computer Engineering
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
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

Nice To Haves

Master's Degree or Minor in AI/Machine Learning or Statistics

Responsibilities

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
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
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
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
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.