Gen AI Engineer

Job Title: AI Architect (Generative AI) – PhD Required

Location: Dallas, TX or San Jose, CA

Client: Toyota Financial solutions(TFS)
Employment Type: Full-Time (FTE)

Rate: open full time

Job Summary

We are seeking an experienced  Gen AI Engineer in Artificial Intelligence, Computer Science, Machine Learning, Data Science, or a related discipline to lead the design and implementation of next-generation Generative AI solutions. The ideal candidate will have extensive experience architecting enterprise AI platforms using AWS Bedrock and Google Vertex AI, with a proven track record of building scalable, production-grade AI applications powered by Large Language Models (LLMs).

This role requires a strong technical leader who can collaborate with cross-functional teams to define AI strategy, architect cloud-native AI solutions, and drive innovation using the latest Generative AI technologies.

Required Qualifications

• 10+ years of software engineering, machine learning, or AI development experience.
• 5+ years of hands-on experience with AWS Bedrock.
• 5+ years of hands-on experience with Google Vertex AI.
• Strong experience designing enterprise-scale AI and ML architectures.
• Deep understanding of Large Language Models (LLMs), Foundation Models, and Generative AI.
• Strong programming experience in Python.
• Experience building Retrieval-Augmented Generation (RAG) applications.
• Experience with AI orchestration frameworks such as LangChain or LlamaIndex.
• Strong knowledge of prompt engineering, AI agents, embeddings, and vector databases.
• Experience with cloud platforms including AWS and Google Cloud Platform (Google Cloud Platform).
• Experience implementing MLOps pipelines, model deployment, monitoring, and governance.
• Strong understanding of REST APIs, microservices, Docker, and Kubernetes.

Key Responsibilities

• Design and architect enterprise-grade Generative AI solutions using AWS Bedrock and Google Vertex AI.
• Develop AI-powered applications leveraging foundation models, LLMs, and AI agents.
• Build scalable Retrieval-Augmented Generation (RAG) architectures using vector databases.
• Lead AI platform strategy, architecture reviews, and technology roadmap initiatives.
• Collaborate with engineering, data science, and business teams to translate business requirements into AI solutions.
• Evaluate and integrate foundation models from Anthropic, Amazon Nova, Meta Llama, Google Gemini, and other leading providers.
• Develop secure, scalable, and highly available AI architectures following enterprise best practices.
• Build AI workflows using LangChain, LlamaIndex, and prompt engineering techniques.
• Design and implement MLOps pipelines for model deployment, monitoring, versioning, and governance.
• Optimize AI workloads for performance, scalability, reliability, and cost.
• Mentor engineering teams and establish AI architecture standards and best practices.
• Stay current with emerging AI technologies and recommend innovative solutions to improve business outcomes.

Preferred Qualifications

• Experience with multi-agent AI systems and autonomous AI workflows.
• Experience with vector databases such as Pinecone, FAISS, Weaviate, or OpenSearch.
• Experience with ML frameworks including TensorFlow or PyTorch.
• Knowledge of Responsible AI, AI governance, security, and compliance.
• Experience with CI/CD pipelines, Infrastructure as Code (Terraform/CloudFormation), and Kubernetes.
• AWS Certified Machine Learning Specialty, AWS Solutions Architect, Google Professional Machine Learning Engineer, or equivalent cloud certifications are highly preferred.

Preferred Skills

• AWS Bedrock
• Google Vertex AI
• Generative AI
• Large Language Models (LLMs)
• RAG (Retrieval-Augmented Generation)
• AI Agents
• LangChain
• LlamaIndex
• Python
• Prompt Engineering
• Vector Databases
• MLOps
• Docker
• Kubernetes
• AWS
• Google Cloud Platform (Google Cloud Platform)