Technical Lead - Dallas at Photon

Job description

Job Summary: 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. Key Responsibilities: 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 Required Skills & Qualifications: • 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 Job Description Template

Requirements

Must-haves

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)

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aillmpythonci/cdmlrestawsazuregcpdockerkubernetes

Platform: oracle-cloud
Effort: medium