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# Gen AI Engineer

- **Company:** CCS, LLC
- **Location:** Plano, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Computer Programming, Continuous Integration, Revision Control Systems, Python (Programming Language), Lex (Software), Azure Machine Learning, Software Engineering, Workflow Management Systems, Reinforcement Learning, Google Cloud, Large Language Models, Prompt Engineering, Generative AI, Containerization, Kubernetes, HuggingFace, Machine Learning Operations, Docker
- **Published:** August 9, 2026
- **Apply:** https://www.wayup.com/i-j-Gen-AI-Engineer-CCS-INC-980983070517048/

## About the Role

10+ years of software engineering and development experience
Strong experience in building and deploying GenAI applications in production.
Strong programming with Python and familiar with GenAI libraries (Transformers, LangChain, Hugging Face, etc.).
Deep understanding of LLMs, embeddings, vector databases (e.g., FAISS, Pinecone, Weaviate).
Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
Familiarity with CI/CD for ML workflows and versioning tools like MLflow or DVC.
Hands-on experience designing and building cloud-native solutions (preferably on AWS)
GenAI tools and frameworks (e.g., LLMs, vector databases, prompt orchestration, LangChain, Bedrock) will be a PLUS
Familiar with AWS AI/ML services (e.g., SageMaker, Bedrock, Comprehend, Lex) is a PLUS
AWS AI certification
Financial Services experience

## Description

Design scalable and robust GenAI architectures using LLMs, multimodal models, and retrieval-augmented generation (RAG).
Fine-tune foundation models using domain-specific data.
Implement prompt engineering, instruction tuning, and reinforcement learning from human feedback (RLHF).
Integrate GenAI capabilities into enterprise platforms using APIs, SDKs, and orchestration tools.
Implement responsible AI practices including bias detection, hallucination mitigation, and explainability.
Drive experimentation with new models, agents, and frameworks (e.g., LangChain, LlamaIndex, OpenAI, Anthropic, etc.).


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