AI Engineer

•  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.

Flexible work from home options available.