AI Engineer

Requirements

• 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.