AI Foundation Model Engineer

About the role

Design, build, deploy, and optimize enterprise-grade AI systems powered by foundation models, LLMs, retrieval-augmented generation, and agentic AI workflows. Convert AI concepts into secure, scalable, observable, and supportable production systems suitable for a regulated financial-services environment.

Responsibilities

• Production LLM applications, RAG pipelines, AI services, and model-serving integrations.
• End-to-end LLMOps/MLOps lifecycle from experimentation to deployment, monitoring, evaluation, rollback, and continuous improvement.
• Model adaptation, inference optimization, APIs, observability, and operational readiness for GenAI solutions.
• 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.
• Adapt and optimize models using LoRA, PEFT, instruction tuning, distillation, transfer learning, quantization, and domain adaptation techniques.
• Develop scalable APIs, microservices, model-serving components, and integration patterns across cloud, hybrid, or containerized environments.
• Optimize inference workloads for latency, throughput, token efficiency, cost, reliability, and user experience.
• Create production documentation, runbooks, release notes, test evidence, and audit-ready implementation records.

Requirements

• 7+ years in AI/ML engineering, platform engineering, software engineering, or applied machine learning.
• Hands-on experience with LLMs, transformers, embeddings, RAG, semantic search, and GenAI application patterns.
• Strong Python engineering skills with PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Experience deploying production AI services using APIs, containers, Kubernetes, CI/CD, cloud-native services, and monitoring platforms.
• Practical knowledge of model evaluation, fine-tuning, inference optimization, and secure data handling.

Qualifications

• Banking, risk, compliance, financial crime, operations, or enterprise technology background.
• Experience with Azure OpenAI, AWS Bedrock, Vertex AI, Databricks, vLLM, Triton, MLflow, Kubeflow, or model gateways.
• Exposure to model risk, AI governance, audit controls, AI cost governance, and private or open-source LLM deployments.

Skills

• Python engineering skills.
• Experience with cloud platforms.
• Knowledge of LLMs and related technologies.
• Experience with MLOps and DevOps practices.

Benefits

Actual compensation will depend on a number of factors, including the candidate's relevant experience, technical skills, and other qualifications. This position is eligible for company benefits including participation in medical, dental, and vision insurance, flexible spending or health savings account, and AD&D insurance, employee assistance, participation in a 401k program, and additional voluntary or legally-required benefits.

Pay

$80/hr - $88/hr

Schedule

N/A