What are the responsibilities and job description for the AI Engineer position at ClinDCast LLC?
Job Title: AI/ML Engineer – Generative AI & LLM
Job Summary
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.
Key Responsibilities
Generative AI & LLM Development
- 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.
AI Agents & Intelligent Automation
- 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.
MLOps & Deployment
- 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.
Collaboration & Delivery
- 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.
Flexible work from home options available.