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# AI/ML Engineer

- **Company:** CCS, LLC
- **Location:** Plano, TX, United States
- **Experience:** Experienced
- **Salary:** $135,200.0 - $156,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Engineering, Continuous Integration, Python (Programming Language), OpenAI, Pinecone, Qdrant, Data Ingestion, LangChain, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Llamaindex, Cloudformation, Git Flow, HuggingFace, Weaviate, Ray Serve, Milvus, Machine Learning Operations, Api Design, Terraform, Data Pipelines, Serverless Computing, OpenSearch, Docker, Microservices
- **Published:** October 16, 2026
- **Apply:** https://www.juju.com/job/15_98504ecc4

## About the Role

* Bachelor's Degree
 * 6+ years cloud architecture experience
 * 3+ years building production GenAI/LLM systems on AWS.
 * Strong Python and AWS expertise, including Lambda, ECS/EKS, S3, SageMaker, Docker and Kubernetes.
 * Production experience with vector databases and designing ingestion + embedding pipelines for both batch and streaming workloads.
 * Hands-on with prompt design, evaluation, LLM orchestration, and RAG implementation patterns.
 * Experience deploying and operating model- serving or MCP - like server infrastructure (selfhosted or managed).
 * Proficient with IaC and delivery tooling, including Terraform/CloudFormation, GitOps, and CI pipelines.
 * Experience with model-serving infrastructure, such as Amazon SageMaker, NVIDIA Triton, Ray Serve, or similar platforms.
 * Hands-on experience with GenAI libraries and frameworks, including LangChain, LlamaIndex, Hugging Face, and OpenAI APIs.
 * Deep operational expertise with vector databases, such as Pinecone, Milvus, Weaviate, or Qdrant.
 * AWS Solutions Architect, AWS DevOps Engineer, or equivalent industry certifications.

## Description

* Cloud Architecture & Infrastructure, Design scalable, secure AWS architectures
 * LLM & GenAI Platforms, Lead integration of API-based and self-hosted LLMs, implement RAG solutions
 * Prompting & Evaluation, Develop prompt engineering strategies, reusable templates, and evaluation frameworks
 * Vector Databases & Retrieval Pipelines, Implement and maintain vector stores (OpenSearch, Pinecone, Milvus, Qdrant)
 * Data Ingestion & Processing Pipelines
 * Microservices & Serverless Systems
 * Python Development & AI Tooling
 * Security, Governance & Cross-Functional Leadership