Skip to main content
Tallo logoTallo logo
Apply for this opportunity

This job application is on an outside website. Be sure to review the job posting there to verify it's the same.

Gen AI Engineer

Job

CCS INC

Plano, TX (In Person)

$93,600 Salary, Full-Time

Posted 4 days ago (Updated 1 day ago) • Actively hiring

Expires 7/8/2026

Review key factors to help you decide if the role fits your goals.
Pay Growth
?
out of 5
Not enough data
Not enough info to score pay or growth
Job Security
?
out of 5
Not enough data
Calculating job security score...
Total Score
100
out of 100
Average of individual scores

Were these scores useful?

Skill Insights

Compare your current skills to what this opportunity needs—we'll show you what you already have and what could strengthen your application.

Job Description

Gen AI Engineer
CCS INC - 4.1
Plano, TX Job Details Full-time $40 - $50 an hour 15 hours ago Benefits Health insurance Dental insurance Paid time off Vision insurance Qualifications Software engineering
Python Full Job Description Benefits:
Bonus based on performance Competitive salary Dental insurance Health insurance Paid time off Vision insurance
Qualifications:
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
Responsibilities:
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.).