Lead Applied AI Engineer

Any Visa

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:
• 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. …