AI Foundation Model Engineer (LLM / Agentic AI / GenAI)

Location: Jersey City, NJ (Hybrid – 4 Days Onsite/Week)
Duration: Long-Term Contract (Through June 30, 2027)
Interview: Mandatory Face-to-Face Interview in Jersey City, NJ
 

Job Description

Hiring a Senior AI Foundation Model Engineer to build enterprise-scale AI applications powered by Large Language Models (LLMs), Agentic AI, and Retrieval-Augmented Generation (RAG). This role requires hands-on experience designing, deploying, and optimizing production AI systems on AWS while collaborating with cloud, DevOps, and platform engineering teams.

The ideal candidate has extensive experience with production-grade AI applications, cloud-native deployment, LLMOps/MLOps, and modern AI frameworks.

Responsibilities

• Design, develop, and deploy enterprise-grade LLM and Agentic AI applications.

• Build scalable RAG pipelines using embeddings, vector databases, semantic search, reranking, and response grounding.

• Develop AI-powered solutions including knowledge assistants, document intelligence, workflow automation, summarization, and decision-support systems.

• Deploy AI services on AWS using Bedrock, SageMaker, OpenSearch, Lambda, EKS/ECS, and related cloud-native technologies.

• Develop reusable AI services and APIs using Python, LangChain, LlamaIndex, Hugging Face, Semantic Kernel, PyTorch, and TensorFlow.

• Optimize inference performance, latency, scalability, reliability, token usage, and infrastructure costs.

• Collaborate with DevOps teams using Terraform, Infrastructure as Code (IaC), CI/CD pipelines, release management, and rollback strategies.

• Implement LLMOps/MLOps best practices including monitoring, observability, prompt logging, evaluation, drift detection, and feedback loops.

• Embed Responsible AI, security, privacy, governance, and model risk controls into AI application design.

• Prepare production documentation, implementation guides, release notes, runbooks, and audit documentation.

Required Skills

• 7 years of experience in AI/ML Engineering, Applied Machine Learning, Software Engineering, or Platform Engineering.

• Strong hands-on experience with:

  • Large Language Models (LLMs)

  • Generative AI

  • Agentic AI

  • Retrieval-Augmented Generation (RAG)

  • Transformers

  • Embeddings

  • Semantic Search

• Strong Python programming skills.

• Experience with PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, or Semantic Kernel.

• Experience deploying production AI applications using Docker, Kubernetes, APIs, CI/CD, and cloud-native platforms.

• Hands-on AWS AI experience including Bedrock, SageMaker, OpenSearch, Lambda, and EKS/ECS.

• Experience with Terraform, Infrastructure as Code (IaC), DevOps pipelines, model evaluation, inference optimization, and secure AI deployments.

Preferred Skills

• Banking or Financial Services experience.

• Experience with Azure OpenAI, Vertex AI, Databricks, MLflow, Kubeflow, Triton, vLLM, Kendra, or enterprise model gateways.

• Knowledge of AI Governance, Responsible AI, Model Risk Management, AI Cost Governance, and private/open-source LLM deployments.

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