Lead Applied AI Engineer

Job Details

• Job Title: Lead Applied AI Engineer
• Location: New York, NY / Louisville, KY
• Duration: 6 months
• Preferred Work Location:
  • 1700 Broadway, Suite 3400, New York, NY 10019
  • Louisville, KY
• Experience Required: 8–10 years
• Role Category: AI Engineer
• Skill Category: AI and Automation

Role Summary

• Architect, build, deploy, and scale production-grade AI solutions.
• Integrate:
  • Generative AI
  • AI agents
  • Modern enterprise platforms
• Support large-scale business operations.
• Ensure strong standards for:
  • Security
  • Reliability
  • Governance
  • Responsible AI
• Define enterprise AI technical standards and engineering best practices.
• Lead enterprise AI adoption.
• Mentor engineering teams.
• Work across AI innovation, enterprise architecture, platform engineering, and responsible AI governance.

Key Responsibilities

AI Solution Architecture

• Architect end-to-end AI systems.
• Design advanced RAG (Retrieval-Augmented Generation) pipelines.
• Develop multi-stage retrieval and re-ranking architectures.
• Design agent orchestration frameworks for multiple specialized agents.
• Integrate multiple AI models based on their specific strengths.
• Build solutions with:
  • Modularity
  • Extensibility
  • Scalability
  • Operational excellence

AI Engineering Standards & Optimization

• Define enterprise standards for:
  • Prompt engineering
  • Prompt templates
  • Prompt versioning
  • Testing methodologies
  • Evaluation frameworks
• Establish performance optimization strategies covering:
  • Model selection
  • Caching
  • Resource utilization
  • Cost optimization

Production Deployment & Reliability

• Lead deployment of AI solutions into production.
• Implement:
  • Observability
  • Logging and tracing
  • Reliability engineering
  • Graceful degradation
  • Circuit breakers
  • Real-time monitoring dashboards
  • Automated alerting
  • Incident response procedures
• Ensure AI services meet enterprise service-level objectives and reliability expectations.

Data & Retrieval Architecture

• Design scalable data ingestion frameworks for:
  • Structured data
  • Unstructured documents
  • Real-time event streams
• Develop:
  • Vector database architectures
  • Hybrid search capabilities
  • Data preprocessing pipelines
  • Data quality monitoring frameworks
• Implement data cleansing, enrichment, and governance processes.
• Ensure high-quality inputs for AI systems.

AI Evaluation & Continuous Improvement

• Establish quantitative AI evaluation frameworks.
• Implement:
  • A/B testing
  • Performance benchmarking
  • User feedback analysis
  • Telemetry-based optimization
• Continuously improve:
  • Prompts
  • Retrieval strategies
  • Agent workflows
  • Model configurations

Platform & Infrastructure Collaboration

• Partner with platform and infrastructure teams to support AI workloads.
• Work with:
  • GPU infrastructure
  • Model-serving platforms
  • Feature stores
  • Scalable data storage
  • Networking infrastructure
• Define requirements for enterprise AI platforms and integration patterns.

Technical Leadership & Mentoring

• Mentor engineers through:
  • Architecture reviews
  • Design guidance
  • Code reviews
  • Career development
• Promote engineering excellence through:
  • Best-practice documentation
  • Technical training
  • Communities of practice
• Foster responsible and ethical AI development.

Responsible AI & Compliance

• Ensure AI solutions meet enterprise governance and compliance requirements.
• Maintain documentation covering:
  • System behavior
  • Decision logic
  • Evaluation methodologies
• Apply responsible AI principles:
  • Fairness
  • Transparency
  • Accountability
  • Bias mitigation
• Support applicable regulatory and industry requirements.

Required Qualifications

• 7+ years of software engineering experience with a strong focus on AI/ML engineering.
• Proven experience building and operating distributed systems at scale.
• Demonstrated success delivering AI-driven business outcomes.
• Experience leading large and complex technical initiatives.
• Bachelor's degree in:
  • Computer Science
  • Engineering
  • Data Science
  • Related discipline
• Equivalent practical experience may be considered.

Generative AI Expertise

• Deep experience designing and deploying production-grade Generative AI solutions.
• Experience with:
  • Advanced RAG architectures
  • Multi-hop retrieval
  • Reasoning systems
  • Agent orchestration frameworks
  • Tool-using AI agents
  • Memory-enabled AI systems
  • Multi-model AI architectures
  • Conversational AI platforms

Enterprise Solution Delivery

• Lead complex AI initiatives involving multiple cross-functional teams.
• Translate business objectives into:
  • Technical solutions
  • AI architectures
  • Delivery roadmaps
• Drive initiatives from concept through:
  • Development
  • Production deployment
  • Optimization

Technical Skills

• Python
• FastAPI
• React
• Distributed systems
• Vector databases
• Embedding models
• LLM APIs
• Agent orchestration frameworks
• Modern cloud-native architectures

AI Engineering Best Practices

• Establish enterprise standards for:
  • Prompt engineering
  • Version control
  • Testing
  • AI evaluation
  • Model observability
  • Cost tracking
  • Performance tracking
  • Benchmarking
  • Data-driven optimization

Responsible AI & Governance

• Strong understanding of:
  • Responsible AI principles
  • Model governance
  • Risk management
  • Model validation
  • Change management
  • Production monitoring
  • Deployment practices in regulated environments

Preferred Qualifications

• Technical leadership across organizational boundaries.
• Strong mentoring and coaching capabilities.
• Ability to collaborate with:
  • Product Management
  • Data Science
  • Engineering
  • Security
  • Compliance
  • Architecture
  • Business stakeholders
• Experience in regulated industries preferred:
  • Healthcare
  • Life Sciences
  • Insurance

Primary Skills for TAG Search

Must Have

• Generative AI
• Agentic AI
• RAG Architecture
• AI Agents
• Multi-Agent Systems
• Python
• FastAPI
• Vector Databases
• LLM Integration
• AI Platform Engineering
• Production AI Deployment
• AI Evaluation Frameworks
• Prompt Engineering
• Observability & Monitoring
• Enterprise Architecture

Strongly Preferred

• React
• Cloud AI Platforms
• Azure
• OpenAI / Azure OpenAI
• Healthcare Domain Experience
• Responsible AI
• AI Governance
• Distributed Systems Engineering

Keywords

• Lead Applied AI Engineer
• AI Engineer
• Applied AI
• Generative AI
• Agentic AI
• AI Agents
• Multi-Agent Systems
• RAG
• LLM
• LLM Integration
• Python
• FastAPI
• React
• Vector Databases
• AI Platform Engineering
• AI Architecture
• AI Evaluation
• Prompt Engineering
• AI Governance
• Responsible AI
• Azure OpenAI
• Cloud AI
• Distributed Systems
• AI and Automation