{"id":52322,"date":"2026-08-03T14:26:42","date_gmt":"2026-08-03T14:26:42","guid":{"rendered":"https:\/\/www.monmouth.edu\/career-development\/job\/ai-foundation-model-engineer\/"},"modified":"2026-08-03T20:25:11","modified_gmt":"2026-08-04T00:25:11","slug":"ai-foundation-model-engineer","status":"publish","type":"ch_job","link":"https:\/\/www.monmouth.edu\/career-development\/job\/ai-foundation-model-engineer\/","title":{"rendered":"AI Foundation Model Engineer"},"content":{"rendered":"

AI Foundation Model Engineer – Jersey City, NJ<\/strong><\/p>\n

LLM \/ Agentic AI \/ Full-Stack AI Engineering<\/strong><\/p>\n

Design, build, deploy, and optimize enterprise-grade AI systems powered by foundation models, LLMs, retrieval-augmented generation, and agentic workflows. The role converts AI concepts into secure, scalable, observable, and supportable production systems on the enterprise AI-ready platform (AIRP), which is currently AWS-hosted while following a cloud-agnostic architecture blueprint.<\/p>\n

Hands-on AWS AI and cloud engineering is a major asset because AIRP currently runs on AWS.<\/p>\n

Candidates should be comfortable working with Terraform\/IaC and CI\/CD teams to move AI services and infrastructure through controlled deployment pipelines.<\/p>\n

Experience should map to business AI use cases such as KYC, credit underwriting, pitch book generation, Banker 360, Customer 360, deal library intelligence, financial crime quality, and sanctions screening.<\/p>\n

Primary ownership<\/strong><\/p>\n

Production LLM applications, RAG pipelines, AI services, and model-serving integrations for AIRP.<\/p>\n

End-to-end LLMOps\/MLOps lifecycle from experimentation to deployment, monitoring, evaluation, rollback, and continuous improvement.<\/p>\n

Reusable AI service components, APIs, prompts, retrieval logic, and observability patterns that can be federated across multiple business use cases.<\/p>\n

Key responsibilities<\/strong><\/p>\n

Design and implement LLM-powered applications such as knowledge assistants, document intelligence solutions, workflow agents, summarization tools, and decision-support systems.<\/p>\n

Build RAG pipelines using embeddings, chunking strategies, vector databases, semantic retrieval, reranking, response grounding, and citation patterns.<\/p>\n

Integrate AI capabilities with AWS-hosted platform components, including model APIs, model gateways, data services, container platforms, and enterprise authentication patterns.<\/p>\n

Collaborate with cloud engineering teams on Terraform modules, IaC templates, environment promotion, CI\/CD pipelines, release controls, and rollback procedures.<\/p>\n

Adapt and optimize models using LoRA, PEFT, instruction tuning, distillation, transfer learning, quantization, and domain adaptation techniques where appropriate.<\/p>\n

Optimize inference workloads for latency, throughput, token efficiency, cost, reliability, and user experience.<\/p>\n

Implement model and application observability, including prompt logs, retrieval quality, hallucination indicators, drift signals, feedback loops, cost telemetry, and service health.<\/p>\n

Embed security, privacy, Responsible AI, and model risk controls into AI application design and delivery.<\/p>\n

Create production documentation, runbooks, release notes, test evidence, and audit-ready implementation records.<\/p>\n

Must-have:<\/strong><\/p>\n

7+ years in AI\/ML engineering, platform engineering, software engineering, or applied machine learning.<\/p>\n

Hands-on experience with LLMs, transformers, embeddings, RAG, semantic search, and GenAI application patterns.<\/p>\n

Strong Python engineering skills with PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.<\/p>\n

Experience deploying production AI services using APIs, containers, Kubernetes, CI\/CD, cloud-native services, and monitoring platforms.<\/p>\n

Practical exposure to AWS AI\/cloud services or comparable cloud-native AI deployment experience, with ability to ramp quickly on AWS-hosted AIRP patterns.<\/p>\n

Working knowledge of Terraform\/IaC, DevOps pipelines, release management, model evaluation, inference optimization, and secure data handling.<\/p>\n

Preferred experience<\/strong><\/p>\n

Banking, risk, compliance, financial crime, operations, or enterprise technology background.<\/p>\n

Experience with AWS Bedrock, SageMaker, OpenSearch, Kendra, Lambda, EKS\/ECS, Azure OpenAI, Vertex AI, Databricks, vLLM, Triton, MLflow, Kubeflow, or model gateways.<\/p>\n

Exposure to cloud-agnostic application patterns, reusable IaC modules, model risk, AI governance, audit controls, AI cost governance, and private or open-source LLM deployments.<\/p>\n