AI/ML Engineer

Ccs•Plano, TX

10d

About The Position

The company is passionate about CX. The collective experiences in leadership roles across firms enable CCS to bring passion, perspective, data-driven decision making to guide, advise, and support organizations throughout their CX journey. We are a “people” company. We are a flat organization. We believe the best idea win. We believe in radical truth. We attract strong-minded people with fierce intellect. Mission Statement: CCS is recognized as a leader in the Contact Center space. Our longstanding history and industry-leading position speak to our success in providing CX solutions centered around the leaders in contact center solutions and strategic technology partners that empower organizations to actualize ROI and sustain a truly competitive advantage in a fast-changing CX environment.

Requirements

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.

Responsibilities

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