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

AI Foundation Model Engineer

LLM / Agentic AI / Full-Stack AI Engineering
Level: Senior Individual Contributor
Experience: 9+ years

Role Overview

We are seeking a Senior AI Foundation Model Engineer to build and deploy secure, scalable, enterprise-grade AI solutions using LLMs, RAG and agentic workflows. The role involves developing production AI applications and reusable services for an AWS-hosted, cloud-agnostic AI platform.

Key Responsibilities

·  Build LLM applications, RAG pipelines, knowledge assistants, document intelligence solutions and workflow agents.

·  Develop embeddings, semantic search, reranking, grounding and citation capabilities.

·  Deploy and manage AI services using APIs, Docker, Kubernetes, CI/CD and cloud-native infrastructure.

·  Collaborate on Terraform/IaC, environment promotion, release controls and rollback procedures.

·  Optimize models and inference for accuracy, latency, throughput, token usage, reliability and cost.

·  Implement LLMOps/MLOps covering evaluation, monitoring, observability, feedback loops and continuous improvement.

·  Ensure security, privacy, Responsible AI, governance and audit readiness.

·  Maintain production documentation, runbooks and release records.

Required Skills

·  Strong hands-on experience with LLMs, transformers, GenAI, RAG, embeddings and vector databases.

·  Advanced Python skills and experience with PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Semantic Kernel or similar frameworks.

·  Production deployment experience using APIs, containers, Kubernetes, CI/CD and monitoring tools.

·  Practical AWS AI/cloud experience, preferably with Bedrock, SageMaker, OpenSearch, Lambda and EKS/ECS.

·  Working knowledge of Terraform/IaC, MLOps/LLMOps, model evaluation, inference optimization and secure data handling.

Preferred Experience

·  Banking, risk, compliance, financial crime or enterprise technology experience.

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

·  Knowledge of LoRA, PEFT, instruction tuning, quantization, model governance and private/open-source LLM deployments.

Alternate Titles: LLM Engineer, GenAI Engineer, AI Platform Engineer, RAG Engineer, Applied ML Engineer or NLP Engineer.