Location: Plano, TX
Experience: 12–15 Years
GenAI/Agentic AI Experience: 2–3+ Years
Work Arrangement: Hybrid – 3 Days Onsite per Week
Duration: 12 Months (NO BENEFITS)
We are seeking an experienced Solution Lead with strong expertise in Enterprise Java architecture, Generative AI (GenAI), Agentic AI, and AI-powered software engineering. The ideal candidate will have a proven track record of designing scalable enterprise applications, particularly in commerce environments, and leveraging AI-driven development tools to accelerate software delivery while maintaining high standards of quality.
This is a hands-on, customer-facing technical leadership role requiring expertise in Java enterprise architecture, large language models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and modern AI development frameworks.
The Solution Lead will collaborate with customers, business stakeholders, architects, and engineering teams to drive AI transformation initiatives, lead solution design, deliver proofs of concept (PoCs), and guide teams in adopting AI-assisted software development practices.
Design and implement enterprise-grade GenAI, RAG, and Agentic AI solutions.
Lead engagements from discovery and architecture through PoC, MVP, and production deployment.
Evaluate LLMs, AI frameworks, vector databases, and cloud-based AI services.
Integrate AI capabilities into enterprise applications, commerce platforms, and business workflows.
Implement prompt engineering, embeddings, function calling, and AI agent orchestration.
Apply AI security, responsible AI practices, guardrails, and observability to enterprise AI solutions.
Provide technical leadership in Java enterprise application development using Java 11/17+, Spring Boot, REST APIs, and microservices.
Design scalable distributed systems and event-driven architectures.
Lead architecture discussions, technical design reviews, and code reviews.
Establish engineering standards, reusable design patterns, and best practices.
Support legacy application modernization using cloud-native technologies and AI-assisted development approaches.
Ensure enterprise applications meet performance, scalability, security, and reliability requirements.
Demonstrate hands-on expertise with AI development tools such as Windsurf, Cursor, GitHub Copilot, Claude Code, OpenAI Codex, or equivalent agentic development environments.
Drive AI adoption across the software development lifecycle, including requirements analysis, coding, testing, code reviews, documentation, and modernization.
Identify opportunities to automate repetitive engineering tasks and improve developer productivity.
Establish best practices for AI-assisted coding, validation, testing, and code quality.
Measure and improve engineering efficiency through AI-enabled development practices.
Conduct customer workshops, discovery sessions, and architecture discussions.
Collaborate with business and technical stakeholders to understand requirements and define scalable solutions.
Translate business challenges into practical enterprise architecture and AI implementation strategies.
Support solution proposals, technical estimations, proof-of-concept development, and executive presentations.
Communicate technical concepts effectively to business stakeholders and engineering teams.
Drive solution delivery with a strong focus on quality, ownership, accountability, and business outcomes.
Mentor architects, senior developers, and engineering teams on Enterprise Java, GenAI, Agentic AI, and AI-assisted development.
Lead technical reviews and guide teams in adopting modern engineering practices.
Participate in hackathons, innovation initiatives, and rapid prototyping activities.
Develop reusable accelerators, frameworks, and AI-enabled engineering assets.
Promote collaboration across architecture, development, and delivery teams.
12–15 years of experience in software engineering, application development, and technical architecture.
Strong hands-on expertise in Java 11/17 or later.
Extensive experience with Spring Boot, REST APIs, and microservices architecture.
Strong understanding of distributed systems and event-driven architecture.
Experience with SQL databases and enterprise application integration.
Proven ability to design and deliver scalable, enterprise-grade applications.
Experience with commerce applications or enterprise commerce platforms is highly desirable.
2–3+ years of hands-on experience developing GenAI and AI-powered applications.
Strong understanding of large language models (LLMs) and prompt engineering.
Experience implementing Retrieval-Augmented Generation (RAG) solutions.
Knowledge of embeddings, vector databases, and semantic search.
Hands-on experience building AI agents, agentic workflows, and function-calling integrations.
Ability to integrate LLM-based capabilities into enterprise applications.
Experience with one or more of the following:
LangChain
LangGraph
Microsoft Semantic Kernel
LlamaIndex
CrewAI
AutoGen
Hands-on experience with one or more of the following:
Windsurf
Cursor
GitHub Copilot
Claude Code
OpenAI Codex
Similar AI-powered or agentic development environments
Experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (GCP).
Knowledge of Docker and Kubernetes.
Experience with CI/CD pipelines and DevOps practices.
Understanding of AI security, responsible AI, LLMOps, guardrails, and observability.
Strong customer-facing and consultative skills.
Experience leading technical discussions and collaborating with business and engineering stakeholders.
Excellent problem-solving, communication, and presentation skills.
Ability to mentor engineering teams and drive technical initiatives.
Comfortable working in a hybrid environment with regular onsite customer interaction.
Senior Java Architect with hands-on GenAI experience.
Principal Engineer with expertise in Java and AI-powered application development.
Enterprise Architect with strong coding and solution implementation capabilities.
AI Architect with extensive enterprise application development experience.
Experience delivering AI-enabled solutions for commerce applications.
Experience driving AI adoption, rapid prototyping, hackathons, and engineering productivity initiatives.
Primary Skills: Enterprise Java, Java 11/17+, Spring Boot, Microservices, REST APIs, GenAI, LLMs, RAG, Agentic AI, Prompt Engineering, AI Agents, AI-Powered Development Tools.
AI Frameworks: LangChain, LangGraph, Semantic Kernel, LlamaIndex, CrewAI, AutoGen.
AI Development Tools: Windsurf, Cursor, GitHub Copilot, Claude Code, OpenAI Codex.
Secondary Skills: AWS, Azure, GCP, SQL Databases, Docker, Kubernetes, CI/CD, DevOps, LLMOps, AI Security, Responsible AI, Guardrails, Observability.