Lead Applied AI Engineer

We are seeking an accomplished Lead Applied AI Engineer to architect and deliver advanced AI systems that seamlessly integrate Generative AI capabilities, AI agents, and modern enterprise platforms. This role is responsible for designing, building, deploying, and scaling production-grade AI solutions that support large-scale business operations while maintaining high standards of security, reliability, governance, and responsible AI practices. The Lead Applied AI Engineer will define technical standards, lead enterprise AI adoption, establish engineering best practices, and mentor engineering teams. This position operates at the intersection of AI innovation, enterprise architecture, platform engineering, and responsible AI governance.

Key Responsibilities

AI Solution Architecture

Architect comprehensive end-to-end AI systems including:

• Advanced RAG (Retrieval-Augmented Generation) pipelines
• Multi-stage retrieval and re-ranking architectures
• Agent orchestration frameworks coordinating multiple specialized agents
• Multi-model AI integrations leveraging model-specific strengths

Design solutions with modularity, extensibility, scalability, and operational excellence to support evolving business requirements.

AI Engineering Standards & Optimization

Define enterprise standards for:

• Prompt engineering
• Prompt templates and versioning
• Testing methodologies
• Evaluation frameworks

Establish performance optimization strategies covering:

• Model selection criteria
• Caching patterns
• Resource utilization
• Cost optimization

Production Deployment & Reliability

Lead deployment of AI solutions into production environments with:

• Comprehensive observability
• Logging and tracing
• Reliability engineering practices
• Graceful degradation mechanisms
• Circuit breaker implementation
• Real-time monitoring dashboards
• Automated alerting
• Incident response procedures

Ensure AI services meet stringent service-level objectives and enterprise reliability expectations.

Data & Retrieval Architecture

Design scalable data ingestion frameworks that process:

• Structured data sources
• Unstructured documents
• Real-time event streams

Develop:

• Vector database architectures
• Hybrid search capabilities
• Data preprocessing pipelines
• Data quality monitoring frameworks

Ensure high-quality inputs for AI systems through cleansing, enrichment, and governance processes.

AI Evaluation & Continuous Improvement

Establish quantitative evaluation frameworks for AI systems. Implement:

• A/B testing capabilities
• Performance benchmarking
• User feedback analysis
• Telemetry-based optimization

Drive continuous improvements across:

• Prompts
• Retrieval strategies
• Agent workflows
• Model configurations