AI Engineer

ClinDCast•Warren, AL

4d•Remote

About The Position

We are seeking a skilled AI/ML Engineer with expertise in Generative AI, Large Language Models (LLMs), and agent-based systems. The ideal candidate will design, develop, and deploy scalable AI solutions, leveraging modern frameworks and cloud-based MLOps practices to deliver production-grade systems. This role involves close collaboration with cross-functional teams to translate business requirements into intelligent, reliable AI applications.

Requirements

Expertise in Generative AI
Expertise in Large Language Models (LLMs)
Expertise in agent-based systems
Experience with modern frameworks
Experience with cloud-based MLOps practices
Experience with Retrieval-Augmented Generation (RAG) pipelines
Experience with vector databases
Experience with prompt engineering strategies
Experience with evaluation frameworks
Experience with frameworks like LangChain, LlamaIndex, or similar tools
Experience with AI agents capable of multi-step reasoning and task execution
Experience with agentic workflows
Experience with human-in-the-loop mechanisms
Experience with APIs, enterprise platforms, and orchestration tools
Experience with MLOps pipelines including training, validation, deployment, and monitoring
Experience with CI/CD pipelines for machine learning models
Experience deploying models as scalable APIs or batch services using cloud-native platforms
Experience monitoring model performance for drift, degradation, and anomalies in production
Experience with model governance, versioning, and lineage tracking
Experience working with business stakeholders to translate requirements into AI-driven solutions
Experience participating in Agile/Scrum development processes
Experience creating technical documentation including solution designs, APIs, and operational guides
Experience mentoring junior team members

Nice To Haves

Experience with foundation models

Responsibilities

Design, fine-tune, and deploy LLM-based solutions for enterprise use cases such as document intelligence, summarization, and conversational AI.
Build Retrieval-Augmented Generation (RAG) pipelines using vector databases to enhance response accuracy and contextual grounding.
Develop prompt engineering strategies and evaluation frameworks to ensure output quality, consistency, and safety.
Integrate LLMs with enterprise systems using frameworks like LangChain, LlamaIndex, or similar tools.
Evaluate and benchmark different foundation models to select optimal solutions for business needs.
Architect and implement AI agents capable of multi-step reasoning and task execution.
Develop agentic workflows using modern design patterns for complex, multi-turn interactions.
Implement human-in-the-loop mechanisms to ensure compliance, reliability, and risk control.
Integrate AI agents with APIs, enterprise platforms, and orchestration tools.
Establish guardrails, monitoring, and audit logging for responsible AI usage.
Build and maintain end-to-end MLOps pipelines including training, validation, deployment, and monitoring.
Implement CI/CD pipelines for machine learning models to enable continuous delivery.
Deploy models as scalable APIs or batch services using cloud-native platforms.
Monitor model performance for drift, degradation, and anomalies in production.
Maintain model governance, versioning, and lineage tracking for auditability.
Work with business stakeholders to translate requirements into AI-driven solutions.
Participate in Agile/Scrum development processes and contribute to sprint deliverables.
Create technical documentation including solution designs, APIs, and operational guides.
Mentor junior team members and contribute to best practices in AI engineering.