- Company Name
- Toyota North America
- Job Title
- Senior Fullstack Gen AI Engineer
- Job Description
-
**Job title**
Senior Fullstack Gen AI Engineer
**Role Summary**
Design, develop, and deploy secure, scalable generative AI (GenAI) solutions as a highly technical individual contributor within a large architectural organization. The role involves building production-ready GenAI applications, integrating multimodal models and retrieval‑augmented generation (RAG) into enterprise platforms, and driving experimentation with the latest AI frameworks and services.
**Expectations**
- Deliver end‑to‑end GenAI solutions that meet business, performance, cost, and compliance requirements.
- Collaborate with domain architects, product teams, and engineers to translate business problems into technical prototypes and production systems.
- Provide hands‑on guidance, proofs of concept, and reusable patterns for GenAI adoption across the organization.
**Key Responsibilities**
- Architect scalable GenAI systems using large language models (LLMs), multimodal models, and RAG techniques.
- Fine‑tune foundation models on domain data; engineer prompts, instruction tuning, and RLHF.
- Integrate GenAI capabilities via APIs, SDKs, and orchestration tools into cloud‑native platforms.
- Implement responsible AI practices: bias detection, hallucination mitigation, explainability.
- Monitor, optimize, and cost‑manage model performance with quantization, distillation, and caching.
- Lead prototypes, proofs of concept, and evaluate new models, agents, and frameworks (LangChain, LlamaIndex, etc.).
- Ensure compliance with data privacy, security, and regulatory standards.
- Contribute architecture trade‑offs, reusable patterns, and documentation for cross‑team delivery.
- Stay current on cloud services, architecture trends, and GenAI tooling.
**Required Skills**
- 10+ years of software engineering experience.
- Proven production deployment of GenAI applications.
- Strong Python programming; familiarity with transformers, LangChain, Hugging Face libraries.
- Deep understanding of LLMs, embeddings, and vector databases (FAISS, Pinecone, Weaviate).
- Cloud platform expertise (AWS, Azure, GCP) and container orchestration (Docker, Kubernetes).
- CI/CD for ML workflows; versioning with MLflow or DVC.
- Knowledge of prompt engineering, few‑shot learning, and agent‑based systems.
- Experience with microservices, APIs, and event‑driven architecture.
- Ability to communicate technical solutions and trade‑offs to technical and non‑technical stakeholders.
**Required Education & Certifications**
- Bachelor’s degree or higher in Computer Science, Engineering, or related field.
- AWS AI/ML certification is a plus.
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