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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Avathon, Inc - **Location:** United States - **Experience:** Expert - **Salary:** $110,000.0 - $130,000.0 - **Contract:** Internship / Graduate position - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Encodings, Continuous Integration, Software Debugging, Python (Programming Language), Machine Learning, Routing, Performance Tuning, Tensorflow, Software Deployment, Management of Software Versions, Pytorch, Delivery Pipeline, Large Language Models, Prompt Engineering, Model Validation, Generative AI, Backend, Containerization, AI Platforms, Information Technology, HuggingFace, Machine Learning Operations, Restful APIs, Automation Anywhere - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/ai-engineer-avathon-8300364 ## About the Role With 3-5 years of hands-on industry experience, you are expected to bring expertise in AI system design, ML engineering, LLM deployment, and scalable software development within fast-paced startup environments., * Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field * 3-5 years of hands-on industry experience in AI Engineering, Machine Learning Engineering, Applied AI, or related roles * Strong experience building and deploying LLM-based applications in production environments * Solid expertise with Python and modern AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, or similar * Strong understanding of transformer architectures, LLM fine-tuning, prompt engineering, RAG systems, and vector databases * Experience building scalable APIs and backend systems supporting AI workflows * Familiarity with cloud platforms such as AWS, GCP, or Azure * Strong software engineering fundamentals including system design, debugging, performance optimization, and production reliability * Experience with containerization, deployment pipelines, and collaborative engineering environments * Strong analytical thinking, ownership mindset, and ability to work in ambiguous, fast-moving startup environments * Strong communication skills and ability to work cross-functionally with technical and business stakeholder, * Exposure to Retrieval-Augmented Generation (RAG), vector databases, or embedding-based search systems * Familiarity with LLM observability and evaluation tools (e.g., Langfuse, LangSmith, Arize Phoenix, Weights & Biases) * Hands-on experience with practical LLM deployment -- prompt versioning, cost/latency tracking, guardrails, or hallucination detection * Exposure to LLM evaluation frameworks (e.g., RAGAS, DeepEval) or LLM-as-judge evaluation patterns * Basic understanding of MLOps practices and model lifecycle management * Experience working on applied AI projects in academic, internship, or startup settings * Interest in industrial AI and asset-intensive environments * Industry exposure in one or more of the following domains: Mining, Oil & Gas, Aerospace, Supply Chain, Logistics, or Renewable Energy ## Description Senior AI Engineer - Generative AI & LLMs At Avathon, we are building cutting-edge AI solutions that transform operations across asset-intensive industries such as Supply Chain, Logistics, Energy, Mining, Aerospace, and Industrial Manufacturing. As an AI Engineer, you will play a critical role in designing, developing, and deploying scalable AI systems with a strong focus on Generative AI, Large Language Models (LLMs), and production-grade machine learning applications. This role is ideal for someone with strong engineering depth who can bridge research and production-building robust AI platforms, optimizing LLM workflows, and delivering high-impact solutions across forecasting, route optimization, anomaly detection, predictive maintenance, and intelligent automation., * Design, build, and deploy production-grade AI/ML systems with strong emphasis on Generative AI and LLM-powered applications * Develop and optimize end-to-end LLM pipelines including RAG architectures, fine-tuning, prompt orchestration, evaluation, and observability * Build scalable backend services and APIs for AI applications using modern engineering best practices * Implement and productionize transformer-based models and GenAI workflows for enterprise use cases * Design vector search systems, embedding pipelines, and retrieval frameworks for knowledge-intensive applications * Partner closely with Product, Engineering, and Business teams to translate operational challenges into scalable AI solutions * Drive experimentation, benchmarking, model evaluation, and performance optimization with scientific rigor * Improve inference efficiency, latency optimization, cost management, and reliability of deployed AI systems * Establish guardrails, hallucination detection, monitoring, and responsible AI practices for production deployments * Contribute to MLOps workflows including CI/CD, model lifecycle management, observability, and cloud deployment * Stay current with the latest advancements in LLMs, agentic systems, foundation models, and applied AI engineering, Location: This role is not remote. Candidates must be based in the Bay Area, CA and are expected to report to our Pleasanton office 5 days a week. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Creating a routing app with Google Maps API from scratch](https://www.wearedevelopers.com/videos/831-creating-a-routing-app-with-google-maps-api-from-scratch) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)