GenAI & ML AI Architect for Enterprise Scale

Photon; Irving (TX); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Photon seeks a hands-on AI Architect to lead GenAI and ML architecture for large-scale enterprise transformations in financial services. You will define end-to-end architectures, build prototypes, and guide production rollout across data ingestion, model serving, and orchestration.

The role requires deep Python expertise, experience with LLMs and RAG, and proficiency in AI frameworks and APIs to deliver scalable, secure, and observable AI systems.

Qualifications

• Strong experience as an AI Architect / ML Architect / GenAI Architect / Lead AI Engineer.
• Deep hands-on programming experience with Python.
• Strong understanding of Machine Learning and Deep Learning fundamentals.
• Hands-on experience with frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent.
• Strong experience building LLM-based applications and GenAI solutions.
• Experience with LLM orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Strong experience with RAG architectures, embeddings, vector search, semantic retrieval, and reranking.
• Experience designing and building agentic AI solutions with tool calling, workflow orchestration, memory, and multi-step reasoning.
• Strong understanding of prompt engineering, prompt versioning, prompt evaluation, and structured outputs.
• Experience building REST APIs and backend AI services using frameworks such as FastAPI, Flask, or equivalent.
• Strong understanding of model lifecycle concepts including training, fine-tuning, inference, evaluation, monitoring, and versioning.
• Experience integrating AI solutions with enterprise data sources, APIs, databases, messaging systems, and application platforms.
• Understanding of data pipelines, feature engineering, data quality, and model input validation.
• Experience building scalable and production-ready AI systems rather than only proof-of-concepts.

Responsibilities

• Define end-to-end GenAI and ML architecture from data ingestion to production deployment.
• Design enterprise-grade solutions leveraging LLMs, traditional ML, retrieval-augmented generation, agents, and AI orchestration.
• Build and validate prototypes using Python.
• Architect RAG solutions with document ingestion, embeddings, retrieval, and response generation.
• Design agentic AI architectures for tool usage, workflow orchestration, memory, and multi-agent interactions.
• Define patterns for integrating AI with enterprise data, APIs, data platforms, and legacy systems.
• Partner with Data Scientists, ML Engineers, Data Engineers, and business stakeholders to translate use cases.
• Define reusable AI architecture patterns and components across use cases.
• Establish patterns for prompt management, model abstraction, routing, versioning, and evaluation.
• Design architectures for model inference, feature pipelines, and real-time or batch scoring.
• Implement Python-based AI services, APIs, pipelines, and orchestration components.
• Ensure AI solutions meet security, privacy, scalability, reliability, performance, explainability, governance, and auditability.
• Evaluate open-source and commercial AI frameworks to meet enterprise requirements.
• Define approaches for model monitoring, hallucination detection, evaluation, guardrails, observability, and human-in-the-loop controls.
• Lead architecture and code reviews and guide production rollout.

Skills

Python

PyTorch

TensorFlow

LangChain

LangGraph

LlamaIndex

Semantic Kernel

RAG

Embeddings

Vector Search

Agentic AI

FastAPI

APIs

Model Lifecycle

Tools

FastAPI

Flask

scikit-learn

Job description

Photon seeks a hands-on AI Architect to lead GenAI and ML architecture for large-scale enterprise transformations in financial services. You will define end-to-end architectures, build prototypes, and guide production rollout across data ingestion, model serving, and orchestration.

The role requires deep Python expertise, experience with LLMs and RAG, and proficiency in AI frameworks and APIs to deliver scalable, secure, and observable AI systems.