Title: AI Engineering, Research, Security & Governance Roles (6 Openings) – Onsite, NY/NJ, AZ, FL, CA
About the Opportunity
Our client, a leading global banking institution, is building out its enterprise AI platform and is hiring across six AI roles. The work covers production GenAI, agentic AI, AI security, and Responsible AI in a regulated financial-services environment. Use cases include KYC, credit underwriting, financial crime, sanctions screening, and customer 360.
Location: Onsite 4 days a week at one of the client’s sites: New York / Jersey City (NY/NJ), Tempe, AZ, Tampa, FL, Los Angeles, CA, or San Francisco, CA.
Interview: Face-to-face interview at the client’s location (Jersey City) is required for most roles.
Work authorization: All visa types considered, with valid US work authorization.
1. Distinguished AI Engineer (Enterprise AI Platform)
Top-tier individual-contributor technical leadership for a large enterprise AI platform (Jersey City, NJ).
- Lead design of platform services for model access, routing, serving, RAG, agents, orchestration, evaluation, and observability
- Balance trade-offs across latency, resiliency, security, data isolation, and cost
- Build reusable assets: APIs, SDKs, Terraform/IaC modules, CI/CD templates, and evaluation tooling
- Build in security controls, including authorization-aware retrieval, secure tool execution, and prompt-injection defenses
- Mentor senior engineers and drive adoption of platform patterns
Must have: 10+ years in software engineering, distributed systems, cloud/platform, or AI/ML infrastructure, with a record of building or scaling multi-team production platforms. Deep hands-on expertise in several of: LLM platforms, model serving, AI gateways, RAG, vector search, agent frameworks, LLMOps/MLOps, or AI observability. Strong grounding in distributed systems, Kubernetes, IAM, and secrets management.
2. Lead Applied AI Engineer
Senior hands-on lead for production AI solutions on the client’s enterprise AI platform.
- Deliver LLM applications, RAG pipelines, and agentic workflows end to end
- Own the applied AI lifecycle: design, evaluation, deployment, monitoring, rollback, and continuous improvement
- Make evidence-based calls on model selection, retrieval strategy, inference optimization, cost, and build vs. buy
- Define evaluation and release-readiness practices (groundedness, safety, latency, cost)
- Embed security, privacy, Responsible AI, and model-risk requirements into each solution
Must have: 7+ years in AI/ML engineering, Applied AI, or software engineering.
3. Applied AI Researcher (GenAI, NLP & Agentic AI)
Bridge advanced research and regulated banking use cases.
- Conduct applied research in LLMs, GenAI, NLP, information retrieval, multimodal AI, and agentic AI
- Design experiments for performance, robustness, safety, and production feasibility
- Prototype solutions for KYC, underwriting, financial crime, and sanctions screening
- Build evaluation methods: golden datasets, adversarial testing, and human review
- Translate prototypes into production requirements for an AWS-hosted platform
Must have: MS or PhD preferred in AI/ML, CS, statistics, or a related field. Strong ML, deep learning, NLP, and transformers foundation. Hands-on with LLMs, embeddings, RAG, and model evaluation. Python with PyTorch, TensorFlow, Hugging Face, or scikit-learn.
Nice to have: AWS Bedrock, SageMaker, MLflow, Databricks; publications or patents; banking or compliance research experience.
4. Prompt Engineer (LLM Interaction Design & GenAI Quality)
Design, test, and govern prompts for enterprise business use cases, not generic chatbots.
- Build system prompts, few-shot examples, tool-use instructions, and citation behavior for chatbots, copilots, RAG, and workflow agents
- Create reusable prompt libraries aligned to enterprise standards
- Evaluate for groundedness, hallucination rate, safety, latency, and token cost
- Run adversarial testing for prompt injection, jailbreaks, and data leakage
- Work with engineers on prompt versioning and CI/CD deployment
Must have: Strong understanding of LLM behavior, tokenization, context windows, RAG, and embeddings. Hands-on with OpenAI/Azure OpenAI, AWS Bedrock, Anthropic, LangChain, LlamaIndex, Semantic Kernel, or Copilot Studio. Strong writing and stakeholder skills.
Nice to have: NLP or conversational AI background; financial services, legal, or compliance experience.
5. AI Security & Compliance Engineer (AI/ML Security, Cloud Security, DevSecOps)
Secure AI and GenAI systems against both new LLM risks and traditional cloud threats.
- Design secure architectures for LLM apps, RAG pipelines, model serving, and agentic workflows
- Threat-model prompt injection, jailbreaks, data leakage, and retrieval poisoning
- Implement AWS controls: IAM, encryption, secrets, network segmentation, API security, and logging
- Build security into MLOps/LLMOps, CI/CD, and Terraform modules
- Support AI red teaming, vulnerability management, and audit-ready documentation
Must have: Strong cybersecurity, cloud security, AppSec, or DevSecOps background. Experience securing cloud-native platforms, APIs, containers, Kubernetes, CI/CD, and IaC. Strong AWS security depth (or equivalent). Working knowledge of GenAI risks.
Nice to have: Securing production AI platforms; financial-services security or audit background; Power Platform and Copilot Studio governance.
6. AI Safety & Responsible AI Lead (AI Governance, Model Risk & Ethical AI)
Define and run Responsible AI governance across the AI lifecycle.
- Define Responsible AI standards, risk-classification methods, and operating models for AI and GenAI
- Set up governance for use-case intake, risk assessment, model review, approvals, and monitoring
- Build safety frameworks covering fairness, bias, explainability, hallucination, and human oversight
- Define guardrails for LLMs, RAG, agentic workflows, and citizen-development solutions (Power Platform / Copilot Studio)
- Govern high-risk workflows such as KYC, credit underwriting, and sanctions screening
Must have: Deep knowledge of Responsible AI, model governance, model risk, explainability, and fairness. Experience implementing AI governance controls in enterprise environments. Knowledge of LLM risks (hallucination, prompt injection, data leakage). Ability to turn regulation into practical controls.
Nice to have: Banking, insurance, or fintech experience; building AI risk taxonomies and control libraries; familiarity with Power Platform and Copilot Studio.
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