Senior Agentic AI Platform Engineer - Remote (EST)

CEI

Dallas, Northern (TX, KY)

Hybrid

USD 96,000 - 138,000

Full time

31 hours ago

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Job summary

CEI is seeking a Senior Agentic AI Developer to design, build, and deploy enterprise-grade AI agents and intelligent automation solutions. This role focuses on agentic AI applications, RAG systems, LLM-powered workflows, and scalable AI platforms using Python and modern AI frameworks.

The ideal candidate has hands-on experience building production AI systems, integrating LLMs, orchestrating multi-agent workflows, and deploying cloud-native applications that support real-world business processes.

Qualifications

Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field.
5+ years of software development experience with strong proficiency in Python.
Experience building and deploying Generative AI, LLM, or Agentic AI solutions in production environments.
Experience developing RAG architectures and integrating vector databases.
Strong understanding of prompt engineering, embeddings, semantic search, and LLM evaluation techniques.
Experience building APIs and microservices using Python frameworks such as FastAPI.
Experience with cloud platforms such as AWS, Azure, or GCP.
Experience with containerization and deployment technologies including Docker and Kubernetes.
Strong troubleshooting, debugging, and problem-solving skills.

Responsibilities

Design, develop, and deploy agentic AI applications using modern LLM frameworks and orchestration platforms.
Build Retrieval-Augmented Generation (RAG) solutions leveraging enterprise data sources, vector databases, and semantic search technologies.
Develop AI agents capable of tool calling, workflow automation, reasoning, and decision support.
Create scalable APIs and backend services to support AI-enabled products and applications.
Design and implement document ingestion, knowledge retrieval, embedding generation, and context management pipelines.
Collaborate with product, engineering, and business teams to identify and deliver AI-driven solutions.
Evaluate, test, and optimize LLM performance, response quality, latency, and reliability.
Implement monitoring, observability, security, and governance controls for enterprise AI systems.
Participate in architecture discussions, proof-of-concept development, and technical design reviews.
Create and maintain technical documentation, implementation plans, and best practices for AI development.