AI Engineer

LLM / Agentic AI / Full-Stack AI Engineering Level Senior Individual Contributor Target / alternate titles LLM Engineer; GenAI Engineer; Machine Learning Engineer - LLM; AI Platform Engineer; NLP Engineer; Applied ML Engineer; RAG Engineer Core keywords LLM, GenAI, RAG, embeddings, vector database, LangChain, LlamaIndex, Hugging Face, PyTorch, AWS Bedrock, SageMaker, OpenSearch, Kubernetes, Docker, Terraform, CI/CD, MLOps, LLMOps, model serving Recruiter red flags Only notebook or prototype experience; no AWS/cloud deployment ownership; weak API engineering; no Terraform/IaC or pipeline exposure; cannot explain evaluation, security, or rollback controls.


Role purpose
Design, build, deploy, and optimize enterprise-grade AI systems powered by foundation models, LLMs, retrieval-augmented generation, and agentic workflows. The role converts AI concepts into secure, scalable, observable, and supportable production systems on the enterprise AI-ready platform (AIRP), which is currently AWS-hosted while following a cloud-agnostic architecture blueprint.

Client-specific emphasis

• Hands-on AWS AI and cloud engineering is a major asset because AIRP currently runs on AWS.
• Candidates should be comfortable working with Terraform/IaC and CI/CD teams to move AI services and infrastructure through controlled deployment pipelines.
• Experience should map to business AI use cases such as KYC, credit underwriting, pitch book generation, Banker 360, Customer 360, deal library intelligence, financial crime quality, and sanctions screening.

Primary ownership

• Production LLM applications, RAG pipelines, AI services, and model-serving integrations for AIRP.
• End-to-end LLMOps/MLOps lifecycle from experimentation to deployment, monitoring, evaluation, rollback, and continuous improvement.
• Reusable AI service components, APIs, prompts, retrieval logic, and observability patterns that can be federated across multiple business use cases.

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.
• Integrate AI capabilities with AWS-hosted platform components, including model APIs, model gateways, data services, container platforms, and enterprise authentication patterns.
• Collaborate with cloud engineering teams on Terraform modules, IaC templates, environment promotion, CI/CD pipelines, release controls, and rollback procedures.
• Adapt and optimize models using LoRA, PEFT, instruction tuning, distillation, transfer learning, quantization, and domain adaptation techniques where appropriate.
• 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 exposure to AWS AI/cloud services or comparable cloud-native AI deployment experience, with ability to ramp quickly on AWS-hosted AIRP patterns.
• Working knowledge of Terraform/IaC, DevOps pipelines, release management, model evaluation, inference optimization, and secure data handling.