Lead Applied AI Engineer
HireOn TechAbout the role
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:
o Advanced RAG (Retrieval-Augmented Generation) pipelines
o Multi-stage retrieval and re-ranking architectures
o Agent orchestration frameworks coordinating multiple specialized agents
o 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:
o Prompt engineering
o Prompt templates and versioning
o Testing methodologies
o Evaluation frameworks
- Establish performance optimization strategies covering:
o Model selection criteria
o Caching patterns
o Resource utilization
o Cost optimization
Production Deployment & Reliability
- Lead deployment of AI solutions into production environments with:
o Comprehensive observability
o Logging and tracing
o Reliability engineering practices
o Graceful degradation mechanisms
o Circuit breaker implementation
o Real-time monitoring dashboards
o Automated alerting
o 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:
o Structured data sources
o Unstructured documents
o Real-time event streams
- Develop:
o Vector database architectures
o Hybrid search capabilities
o Data preprocessing pipelines
o 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:
o A/B testing capabilities
o Performance benchmarking
o User feedback analysis
o Telemetry-based optimization
- Drive continuous improvements across:
o Prompts
o Retrieval strategies
o Agent workflows
o Model configurations
Platform & Infrastructure Collaboration
- Partner with platform and infrastructure teams to ensure readiness for AI workloads, including:
o GPU infrastructure
o Model serving platforms
o Feature stores
o Scalable data storage
o Networking infrastructure
- Define requirements for enterprise AI platform capabilities and integration patterns.
Technical Leadership & Mentoring
- Mentor engineers through:
o Architecture reviews
o Design guidance
o Code reviews
o Career development support
- Promote engineering excellence through:
o Best-practice documentation
o Technical training
o 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:
o System behavior
o Decision logic
o Evaluation methodologies
- Apply responsible AI principles including:
o Fairness
o Transparency
Responsibilities
- Architect comprehensive end-to-end AI systems
- Define enterprise standards for prompt engineering and testing methodologies
- Lead deployment of AI solutions into production environments
- Design scalable data ingestion frameworks
- Establish quantitative evaluation frameworks for AI systems
- Partner with platform and infrastructure teams for AI workloads
- Mentor engineers through architecture reviews and design guidance
- Ensure AI solutions adhere to enterprise governance and compliance requirements
Qualifications
- Proven experience in AI solution architecture
- Strong understanding of AI engineering standards and optimization
- Experience with production deployment and reliability engineering
- Knowledge of data and retrieval architecture
- Ability to establish AI evaluation and continuous improvement processes
- Technical leadership and mentoring skills
- Familiarity with responsible AI principles
Skills mentioned
About HireOn Tech
At HireOn Tech, we believe that the right talent fuels the future. As a next-generation talent acquisition company, we specialize in connecting visionary tech firms with exceptional professionals who drive innovation, scalability, and success. Founded with a mission to redefine recruitment, HireOn Tech blends strategic insight, cutting-edge technology, and human-centric values to deliver tailored staffing solutions. Whether you're a startup seeking agile developers or an enterprise scaling your AI capabilities, we curate talent that aligns with your goals and culture.