AI Foundation Model Engineer; LLM​/Agentic AI​/Full-Stack AI Engineering

AI Foundation Model Engineer (LLM / Agentic AI / Full-Stack AI Engineering)

Location:

210 Hudson Street, Jersey City, NJ, 07311 (3-4 days onsite per week)

Interview:
May require an in-person (F2F) interview

Duration: 12 Month

We are seeking a Senior AI Foundation Model Engineer to design, build, deploy, and optimize enterprise-grade AI systems powered by foundation models, LLMs, retrieval-augmented generation (RAG), and agentic workflows. This role converts AI concepts into secure, scalable, observable, and supportable production systems on our enterprise AI-ready platform (AIRP), currently AWS-hosted while following a cloud-agnostic architecture blueprint.

• Hands-on AWS AI and cloud engineering experience is a major asset, as AIRP currently runs on AWS.
• Comfort working with Terraform/IaC and CI/CD teams to move AI services and infrastructure through controlled deployment pipelines.
• Experience mapping 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.

• Production LLM applications, RAG pipelines, AI services, and model-serving integrations for AIRP.
• End-to-end LLMOps/MLOps lifecycle — from experimentation to deployment, monitoring, evaluation, rollback, and continuous improvement.
• Reusable AI service components, APIs, prompts, retrieval logic, and observability patterns federated across multiple business use cases.

• Design and implement LLM-powered applications such as knowledge assistants, document intelligence solutions, workflow agents, summarization tools, and decision-support systems.
• Build RAG pipelines using embeddings, chunking strategies, vector databases, semantic retrieval, reranking, response grounding, and citation patterns.
• Integrate AI capabilities with AWS-hosted platform components, including model APIs, model gateways, data services, container platforms, and enterprise authentication patterns.
• Collaborate with cloud engineering teams on Terraform modules, IaC templates, environment promotion, CI/CD pipelines, release controls, and rollback procedures.
• Adapt and optimize models using LoRA, PEFT, instruction tuning, distillation, transfer learning, quantization, and domain adaptation techniques.
• Optimize inference workloads for latency, throughput, token efficiency, cost, reliability, and user experience.
• Implement model and application observability — prompt logs, retrieval quality, hallucination indicators, drift signals, feedback loops, cost telemetry, and service health.
• Embed security, privacy, Responsible AI, and model risk controls into AI application design and delivery.
• Create production documentation, runbooks, release notes, test evidence, and audit-ready implementation records.

• 7+ years in AI/ML engineering, platform engineering, software engineering, or applied machine learning.
• Strong AWS cloud engineering experience.
• AWS AI/ML or GenAI exposure, including Bedrock, Sage Maker, LLMs, RAG, embeddings, model serving, or AI platform engineering.
• Strong Terraform / Infrastructure as Code experience, including creating reusable cloud-agnostic IaC templates/modules.
• Strong Dev Ops / CI/CD pipeline experience, especially moving Terraform code through deployment pipelines.
• Enterprise platform engineering experience in regulated environments.
• Security, governance, audit, compliance, and Responsible AI awareness.
• Hands-on experience with LLMs, transformers, embeddings, RAG, semantic search, and GenAI application patterns.
• Strong Python engineering skills with PyTorch, Tensor Flow, Hugging Face, Lang Chain, Llama Index, Semantic Kernel, or equivalent frameworks.
• Experience deploying production AI services using APIs, containers, Kubernetes, CI/CD, cloud-native services, and monitoring platforms.

• Banking, risk, compliance, financial crime, operations, or enterprise technology background, with exposure to use cases such as:
• KYC
• Credit underwriting
• Governance tracking
• Pitch book generation
• Banker 360 / Customer 360
• Deal library intelligence
• Financial crime quality
• Sanctions screening
• Experience with AWS Bedrock, Sage Maker, Open Search, Kendra, Lambda, EKS/ECS, Azure OpenAI, Vertex AI, Databricks, vLLM, Triton, MLflow, Kubeflow, or model gateways.
• 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.
• Citizen development / Microsoft stack experience (for relevant roles):
  Microsoft Power Platform, Copilot Studio, Power Apps, Power Automate, Power BI.