I
Lead Applied AI Engineer
I-Flow
1 hour ago
Full-time
On-site
Job Title:Lead Applied AI Engineer
Preferred work location: :1700 Broadway, Suite 3400, New York, NY 10019 or Louisville,KY
Duration: 6 months
Experience Required: 8-10
JD :
Lead Applied AI Engineer
Role Summary
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
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:
o Prompt engineering
Prompt templates and versioning Testing methodologies Evaluation frameworks Establish performance optimization strategies covering: o Model selection criteria
Caching patterns Resource utilization Cost optimization Production Deployment & Reliability • Lead deployment of AI solutions into production environments with:
o 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:
o Structured data sources
Unstructured documents Real-time event streams Develop: o 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: o A/B testing capabilities
Performance benchmarking User feedback analysis Telemetry-based optimization Drive continuous improvements across: o Prompts
Retrieval strategies Agent workflows Model configurations Platform & Infrastructure Collaboration • Partner with platform and infrastructure teams to ensure readiness for AI workloads, including:
o 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:
o Architecture reviews
Design guidance Code reviews Career development support Promote engineering excellence through: o 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: o System behavior
Decision logic Evaluation methodologies Apply responsible AI principles including: o Fairness
Transparency Accountability Bias mitigation Support compliance with applicable regulatory and industry requirements.
Required Qualifications
Experience • 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 and leading large, complex technical initiatives. Education • Bachelor's degree in Computer Science, Engineering, Data Science, or a related discipline.
Equivalent practical experience may be considered. Generative AI Expertise • Deep experience designing and deploying production-grade Generative AI solutions including:
o Advanced RAG architectures
Multi-hop retrieval and reasoning systems Agent orchestration frameworks Tool-using AI agents Memory-enabled AI systems Multi-model AI architectures Conversational AI platforms Enterprise Solution Delivery • Experience leading complex AI initiatives involving multiple cross-functional teams.
Ability to translate business objectives into: o Technical solutions
AI architectures Delivery roadmaps Experience driving initiatives from concept through production deployment and optimization.
Technical Skills
Strong hands-on expertise in: • Python
FastAPI React Distributed systems Vector databases Embedding models LLM APIs Agent orchestration frameworks Modern cloud-native architectures AI Engineering Best Practices
Experience establishing enterprise standards for: • Prompt engineering
Version control and testing AI evaluation methodologies Model observability Cost and performance tracking Benchmarking frameworks Data-driven optimization practices 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 • Experience providing technical leadership across organizational boundaries.
Strong mentoring and coaching capabilities. Demonstrated ability to collaborate effectively with: o Product Management
Data Science Engineering Security Compliance Architecture Business stakeholders Experience in healthcare, life sciences, insurance, or other regulated industries preferred. 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 preferred) Healthcare Domain Experience Responsible AI / AI Governance Distributed Systems Engineering
Role Descriptions: AI Engineer Essential Skills: AI Engineer Desirable Skills: Keyword: Skills: AI and Automation
JD :
Lead Applied AI Engineer
Role Summary
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
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:
o Prompt engineering
Prompt templates and versioning Testing methodologies Evaluation frameworks Establish performance optimization strategies covering: o Model selection criteria
Caching patterns Resource utilization Cost optimization Production Deployment & Reliability • Lead deployment of AI solutions into production environments with:
o 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:
o Structured data sources
Unstructured documents Real-time event streams Develop: o 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: o A/B testing capabilities
Performance benchmarking User feedback analysis Telemetry-based optimization Drive continuous improvements across: o Prompts
Retrieval strategies Agent workflows Model configurations Platform & Infrastructure Collaboration • Partner with platform and infrastructure teams to ensure readiness for AI workloads, including:
o 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:
o Architecture reviews
Design guidance Code reviews Career development support Promote engineering excellence through: o 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: o System behavior
Decision logic Evaluation methodologies Apply responsible AI principles including: o Fairness
Transparency Accountability Bias mitigation Support compliance with applicable regulatory and industry requirements.
Required Qualifications
Experience • 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 and leading large, complex technical initiatives. Education • Bachelor's degree in Computer Science, Engineering, Data Science, or a related discipline.
Equivalent practical experience may be considered. Generative AI Expertise • Deep experience designing and deploying production-grade Generative AI solutions including:
o Advanced RAG architectures
Multi-hop retrieval and reasoning systems Agent orchestration frameworks Tool-using AI agents Memory-enabled AI systems Multi-model AI architectures Conversational AI platforms Enterprise Solution Delivery • Experience leading complex AI initiatives involving multiple cross-functional teams.
Ability to translate business objectives into: o Technical solutions
AI architectures Delivery roadmaps Experience driving initiatives from concept through production deployment and optimization.
Technical Skills
Strong hands-on expertise in: • Python
FastAPI React Distributed systems Vector databases Embedding models LLM APIs Agent orchestration frameworks Modern cloud-native architectures AI Engineering Best Practices
Experience establishing enterprise standards for: • Prompt engineering
Version control and testing AI evaluation methodologies Model observability Cost and performance tracking Benchmarking frameworks Data-driven optimization practices 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 • Experience providing technical leadership across organizational boundaries.
Strong mentoring and coaching capabilities. Demonstrated ability to collaborate effectively with: o Product Management
Data Science Engineering Security Compliance Architecture Business stakeholders Experience in healthcare, life sciences, insurance, or other regulated industries preferred. 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 preferred) Healthcare Domain Experience Responsible AI / AI Governance Distributed Systems Engineering
Role Descriptions: AI Engineer Essential Skills: AI Engineer Desirable Skills: Keyword: Skills: AI and Automation