Multiple Role for AI Engineer

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.

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.

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.

Preferred experience

Banking, risk, compliance, financial crime, operations, or enterprise technology background.

Experience with AWS Bedrock, SageMaker, OpenSearch, Kendra, Lambda, EKS/ECS, Azure OpenAI, Vertex AI, Databricks, vLLM, Triton, MLflow, Kubeflow, or model gateways.

Exposure to cloud-agnostic application patterns, reusable IaC modules, model risk, AI governance, audit controls, AI cost governance, and private or open-source LLM deployments.