Lead Applied AI Engineer

iSite Technologies Corp.; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

iSite Technologies Corp. is seeking a Lead Applied AI Engineer to architect and deliver advanced AI systems that seamlessly integrate Generative AI capabilities, AI agents, and modern enterprise platforms.

You will define technical standards, lead enterprise AI adoption, mentor engineering teams, ensure security, reliability, governance, and responsible AI practices, and push scalable, production‑grade AI solutions across the organization.

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 and leading large, complex technical initiatives.

Responsibilities

• Architect comprehensive end-to-end AI systems, including advanced Retrieval-Augmented Generation pipelines and multi-stage retrieval architectures.
• Define enterprise standards for prompt engineering, prompt templates, testing methodologies, and evaluation frameworks.
• Lead deployment of AI solutions into production with observability, logging, reliability practices, and incident response procedures.
• Design scalable data ingestion frameworks for structured and unstructured data, real-time event streams, and vector database integrations.
• Develop vector DB architectures and hybrid search capabilities; implement data quality monitoring and governance processes.
• Establish quantitative evaluation frameworks for AI systems and enable A/B testing, telemetry-based optimization, and continuous improvement.
• Collaborate with platform and infrastructure teams to ensure readiness for AI workloads (GPU infra, model serving platforms, feature stores, etc.).
• Provide technical leadership through architecture reviews, design guidance, code reviews, and career development support.

Skills

Generative AI

Python

FastAPI

React

Vector databases

LLM APIs

Agent orchestration frameworks

Distributed systems

AI Engineering Best Practices

Embedding models

Education

Bachelor's degree in Computer Science, Engineering, Data Science, or related discipline

Job description

Lead Applied AI Engineer

Any Visa

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:
• 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

Accountability

Bias mitigation

Support compliance with applicable regulatory and industry requirements.

Experience

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 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:
• 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

• 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

• 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

Keyword

Desirable Skills:

Skills: AI and Automation

Experience Required: 8-10