EI
AI Engineer
Echo IT Solutions, Inc
🇺🇸 United States
On-site
2 months ago
- AI
- Machine Learning
- Natural Language Processing
- RAG
- LangChain
- LlamaIndex
- Hugging Face
- PyTorch
- AWS Bedrock
- OpenSearch
- Kubernetes
- Docker
- Terraform
- CI/CD
- MLOps
- AWS Cloud
- IaC
- AWS
- KYC
- LoRA
- AI/ML
- Python
- TensorFlow
- Semantic Kernel
- Devops
2 months ago
| 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.
- 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.
- 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.
- 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.
AI Engineer · Echo IT Solutions, Inc