Technical Lead - Dallas, TX

The Agentic AI Architect / Technical Lead will serve as the primary technical authority for designing and delivering large-scale Agentic AI solutions. This role acts as a critical bridge between product strategy, customer requirements, and engineering execution, requiring a strong blend of AI expertise, system architecture, and leadership skills.

The Architect will be responsible for leading architecture definition, driving complex proof-of-concept initiatives, enabling multi-agent system design, and managing cross-functional engineering teams. The role demands deep experience in LLM ecosystems, cloud-native platforms, and scalable system design to deliver robust, production-grade AI platforms with measurable business outcomes.

Solution Architecture: Lead the design and development of scalable, secure, and high-performance architectures for Agentic AI platforms using Python and modern frameworks

Technical Standards & Deliverables: Define architecture patterns, engineering standards, and best practices for development, deployment, and system scalability

Collaboration: Work closely with product managers, engineering teams, DevOps, and QA to align technical solutions with business requirements and ensure seamless system integration

Stakeholder Interaction: Lead and execute technical POCs for Agentic AI solutions, working with customer stakeholders to define success criteria, build tailored agent configurations, and demonstrate business impact

Agent Orchestration: Architect and implement multi-agent workflows using frameworks such as LangGraph, AutoGen, or CrewAI, ensuring alignment with real-world use cases

Platform Development: Design and build resilient, scalable, multi-tenant AI platforms that support continuous innovation and production deployment

Evaluation & Benchmarking: Own the design and implementation of LLM and agent evaluation frameworks, including metrics for accuracy, hallucination, safety, and performance

Performance Optimization: Optimize system architecture and infrastructure for scalability, latency, and cost-efficiency across AI workloads

Best Practices: Establish and enforce standards across MLOps, AIOps, CI/CD, model versioning, experimentation tracking, and system observability

Leadership & Mentorship: Provide technical leadership, guide architectural decisions, and mentor engineering teams to ensure high-quality delivery

Innovation & Research: Stay updated with advancements in AI, LLMs, and agentic frameworks, continuously improving system capabilities

Documentation: Create and maintain comprehensive architectural and technical documentation

• 12+ years of experience in software engineering with strong expertise in Python and backend system development
• Extensive experience in designing scalable, secure, multi-tenant AI/ML platforms
• Deep expertise in LLMs (OpenAI, Gemini, Anthropic, Llama) and agentic AI systems
• Hands-on experience with agent frameworks such as AutoGen, CrewAI, LangGraph, and LangChain ecosystem (LangChain, LangSmith, LangFlow)
• Strong experience building RAG-based systems and working with vector databases
• Proficiency in Python ecosystem including PyTorch, Scikit-learn, LlamaIndex, and evaluation tools like DeepEval
• Deep understanding of LLM concepts (prompt engineering, fine-tuning, function/tool calling, RAG)
• Strong experience in microservices architecture, REST APIs, and event-driven systems
• Expertise in relational (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Redis)
• Experience with cloud platforms (AWS, Azure, GCP) and cloud-native architectures
• Familiarity with Docker, Kubernetes, and modern DevOps practices
• Experience with CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions)
• Strong exposure to observability tools for logging, monitoring, and tracing AI systems
• Strong understanding of system design, scalability, and engineering best practices
• Proven ability to lead architectural discussions, mentor teams, and engage with stakeholders and clients