AI Foundation Model Engineer

Title: AI Foundation Model Engineer

Location: Jersey City NJ(Hybrid)

Type: Contract

Role purpose:

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

Primary ownership:

• 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.

Key responsibilities:

• 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.
• Implement model and application observability including 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.

Must-have candidate profile:

• 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.

Preferred experience:

• 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.

Initial screening questions:

• Describe a production LLM or RAG system you built. What was your role and what changed after launch
• How did you evaluate groundedness hallucination rate retrieval quality latency and cost
• What model adaptation or fine-tuning method have you used and why
• How did you secure sensitive data and prevent leakage in the AI pipeline
• What observability and rollback mechanisms did you implement