Note: The job is a remote job and is open to candidates in USA. Ztek Consulting is seeking an accomplished Lead Applied AI Engineer to architect and deliver advanced AI systems integrating Generative AI, AI agents, and modern enterprise platforms. The role focuses on designing, deploying, scaling, and governing production-grade AI solutions while defining technical standards, leading enterprise AI adoption, and mentoring engineering teams.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- Partner with platform and infrastructure teams to ensure readiness for AI workloads, including:
- GPU infrastructure
- Model serving platforms
- Feature stores
- Scalable data storage
- Networking infrastructure
- Define requirements for enterprise AI platform capabilities and integration patterns
- Mentor engineers through:
- Architecture reviews
- Design guidance
- Code reviews
- Career development support
- Promote engineering excellence through:
- Best-practice documentation
- Technical training
- Communities of practice
- Foster a culture of responsible and ethical AI development
- Ensure AI solutions adhere to enterprise governance and compliance requirements
- Maintain documentation of:
- System behavior
- Decision logic
- Evaluation methodologies
- Apply responsible AI principles including:
- Fairness
- Transparency
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