Lead Applied AI Engineer

Ztek Consulting INC; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

Ztek Consulting INC is seeking a Lead Applied AI Engineer to architect and deliver advanced AI systems that integrate Generative AI, AI agents, and enterprise platforms. You will design, build, and deploy production-grade AI solutions supporting large-scale operations with security, reliability, and governance at the forefront.

You will define technical standards, lead enterprise AI adoption, mentor teams, and collaborate with platform and data organizations to ensure scalable, responsible AI

Qualifications

• Experience designing end-to-end AI systems for enterprise use.
• Proven ability to define technical standards and best practices.
• Hands-on experience with deployment in production.
• Strong collaboration with platform and data teams.
• Familiarity with responsible AI and governance.

Responsibilities

• Lead AI solution architecture spanning RAG, retrieval, and agents.
• Define engineering standards for prompts, testing, and evaluation.
• Oversee production deployment with observability and reliability.
• Design scalable data ingestion, vector databases, and search.
• Drive continuous improvement through experiments and telemetry.
• Mentor engineers and promote best-practice development.
• Ensure governance, compliance, and responsible AI practices.

Skills

AI architecture

RAG pipelines

Agent orchestration

Enterprise system integration

Responsible AI governance

Tools

Vector databases

Model serving platforms

GPU infrastructure

Observability tools

Job description

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

Platform & Infrastructure Collaboration

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.

Technical Leadership & Mentoring

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.

Responsible AI & Compliance

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