> Markdown version of [/jobs/ext/735296-ai-foundation-model-engineer](https://www.wearedevelopers.com/jobs/ext/735296-ai-foundation-model-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Foundation Model Engineer - **Company:** Usg Inc. - **Location:** Jersey City, NJ, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Engineering, Continuous Integration, Data Security, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Tensorflow, Search Technologies, Software Deployment, Software Engineering, Data Logging, Enterprise Software Applications, Pytorch, Transfer Learning, Large Language Models, Model Validation, Software Application Programming, Generative AI, Containerization, AI Platforms, Kubernetes, HuggingFace, Machine Learning Operations, Virtual Agents, Restful APIs, Serverless Computing, Databricks, Microservices - **Published:** June 29, 2026 - **Apply:** https://www.dice.com/job-detail/eeed3b1c-9d81-44e6-a493-8535e48c96e7 ## About the Role The ideal candidate combines strong AI/ML engineering expertise with cloud-native software development and production deployment experience., * 7+ years of experience in AI/ML Engineering, Applied Machine Learning, Platform Engineering, Software Engineering, or related disciplines. * Hands-on experience developing applications using Large Language Models (LLMs), Transformers, embeddings, Retrieval-Augmented Generation (RAG), semantic search, and Generative AI architectures. * Strong Python development experience with frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Semantic Kernel, or equivalent AI frameworks. * Experience deploying production AI services using REST APIs, microservices, containers, Kubernetes, CI/CD pipelines, cloud-native services, and monitoring platforms. * Strong understanding of model evaluation, fine-tuning, inference optimization, secure data handling, and AI application performance tuning. * Experience working with cloud platforms and distributed AI workloads. * Excellent problem-solving, software engineering, and collaboration skills., * Experience within Banking, Financial Services, FinTech, Risk Management, Compliance, Financial Crime, Operations, or Enterprise Technology. * Experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, Databricks, vLLM, Triton Inference Server, MLflow, Kubeflow, AI model gateways, or similar enterprise AI platforms. * Familiarity with Responsible AI, AI Governance, Model Risk Management, Audit Controls, AI Cost Governance, and private or open-source LLM deployments. * Experience deploying enterprise-scale AI platforms in regulated environments. ## Description Seeking an experienced AI Foundation Model Engineer to design, build, deploy, and optimize enterprise-grade AI solutions powered by Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), and agentic AI workflows. This role is responsible for developing scalable, secure, and production-ready AI applications while ensuring operational excellence, observability, governance, and compliance within enterprise environments., * Design and develop LLM-powered applications including knowledge assistants, document intelligence platforms, workflow agents, summarization tools, and decision-support systems. * Build Retrieval-Augmented Generation (RAG) pipelines using embeddings, semantic search, vector databases, chunking strategies, reranking, response grounding, and citation mechanisms. * Fine-tune and optimize foundation models using techniques such as LoRA, PEFT, instruction tuning, transfer learning, knowledge distillation, quantization, and domain adaptation. * Develop scalable APIs, microservices, model-serving infrastructure, and integration services across cloud, hybrid, and containerized environments. * Optimize inference workloads for latency, throughput, token efficiency, scalability, reliability, cost optimization, and user experience. * Implement observability solutions for AI applications including prompt logging, retrieval quality metrics, hallucination detection, model drift monitoring, service health, user feedback, and cost telemetry. * Embed security, privacy, Responsible AI, model governance, and enterprise risk controls throughout the AI application lifecycle. * Create production documentation, deployment guides, runbooks, release documentation, testing evidence, and audit-ready implementation artifacts. * Collaborate with AI Researchers, Platform Engineers, Security, Product, Architecture, and Business teams to deliver enterprise AI capabilities. ## Related Videos - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [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) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)