AI Lead - Dallas, TX

Photon Career Site

About The Position

The Agentic AI Lead will be a critical bridge between the product team and the development team. This role requires a unique blend of deep technical expertise in AI, cloud deployments, and exceptional communication skills to deliver successful outcomes for complex, agentic AI solutions. The AI Lead will be responsible for product conversations, leading proof-of-concept initiatives, architecture definition and also managing diverse technical teams.

Requirements

10+ years of experience in strong software engineering , primarily in Python, with experience writing clean, maintainable, and efficient code.
6+ years hands-on experience in Python, with exposure to frameworks like Django, Flask, or FastAPI.
Experience in designing secure, scalable, multi-tenant AI platform and in depth experience using LLM’s like Open AI, Gemini, Anthropic , Llama
Experience using technologies like Autogen, Crew AI, Agent development Kit
Hands on experience in Langgraph, Langchain, Langsmith , Lang flow
Experience in building Retrieval-Augmented Generation (RAG)-based agents
Experience in the advanced mechanisms and libraries around python, pytorch, langchain, llama index, deepeval, scikit learn.
Deep understanding of LLM mechanisms (prompt engineering, fine-tuning, RAG, function/tool calling).
Experience with observability tools (logging, tracing, monitoring) for real-time model performance diagnostics.
Strong experience in designing and building microservices architectures, REST APIs, and event-driven systems.
Proficiency in database design and development – relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis, etc.).
Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native development.
Familiarity with containerization technologies (Docker, Kubernetes).
Experience with CI/CD pipelines and tools like Jenkins, GitLab CI, etc.
Strong understanding of software engineering principles, design patterns, and best practices.
Ability to lead architectural discussions and present solutions to stakeholders and clients.

Responsibilities

Solution Design: Lead the design and development of robust, scalable, and secure backend architectures using Python and related frameworks
Deliverables: Define technical standards, patterns, and best practices for development and deployment
Collaboration: Collaborate with product managers, frontend teams, DevOps, and QA to define technical requirements and ensure end-to-end system integration
Stakeholder Interaction: Lead and execute technical POCs for agentic AI solutions, showcasing the value and potential of the platform. This involves collaborating closely with customer stakeholders to define success criteria, designing and implementing bespoke agent configurations, and demonstrating quantifiable business impact
Agent Orchestration: Architect agent orchestration solutions aligned with customer requirements, ensuring timely implementation and delivery.
Platform Development: Develop resilient, scalable, and high-performance platforms and tools to support product evolution and seamless production deployment.
Mandatory Evaluation Mastery: Own the definition, implementation, and analysis of agent and LLM evaluation benchmarks (reference tests, heuristics, model-graded evaluation). This includes metrics for helpfulness, accuracy, hallucination, and safety.
Performance Optimization: Optimize the platform and infrastructure for performance, scalability, and cost-efficiency.
Best Practices: Establish and enforce best practices for code quality, model versioning, experimentation tracking, and FinOps, TechOps, MLOps, AIOps
Mentorship & Guidance: Provide technical leadership and mentorship to junior AI engineers and researchers.
Innovation & Research: Stay abreast of the latest advancements in AI research and actively explore new techniques and tools to enhance our capabilities.
Documentation: Create comprehensive technical documentation for architectures, models, and processes.