Engineering Team Lead

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Join iQueue as an Engineering Team Lead, AI Systems \& Multi-Agent Platforms, where you will lead the design, delivery, and evolution of advanced AI-driven systems across our core platform initiatives. This role is ideal for a hands-on technical leader who can guide a team while architecting scalable, production-grade solutions in multi-agent orchestration, federated learning, LLM fine-tuning, knowledge distillation, and enterprise AI infrastructure.

You will be responsible for translating ambitious product and research goals into reliable, auditable, and high-impact software systems. Working across platform engineering, applied AI, data, and product, you will help shape the technical roadmap for intelligent systems that coordinate multiple agents, operate across distributed environments, and continuously improve through feedback, evaluation, and model refinement.

This is a leadership role for someone who can balance technical depth, execution discipline, and people management—someone who can mentor engineers, align cross-functional stakeholders, and ensure the team delivers production-ready AI capabilities that are robust, scalable, and commercially meaningful.

Responsibilities

• Lead and manage a team of engineers building iQueue’s AI platform and related initiatives, providing technical direction, delivery oversight, coaching, and career development
• Own the architecture and implementation of complex systems spanning multi-agent coordination, event-driven services, semantic routing, model serving, and enterprise integrations
• Drive the design of interoperable agent frameworks, including agent orchestration, context exchange, constraint enforcement, decision tracking, and feedback-driven optimization
• Lead development of distributed and privacy-preserving ML workflows, including federated learning pipelines, adapter-based model updates, and model aggregation strategies
• Guide LLM fine-tuning and post-training workflows, including supervised adaptation, evaluation, model optimization, and knowledge distillation for efficient deployment across environments
• Partner closely with product, design, data, and business stakeholders to convert high-level requirements into scalable technical solutions and executable roadmaps
• Establish engineering best practices across code quality, testing, observability, model evaluation, deployment, and operational reliability
• Oversee code reviews, technical design reviews, and architectural decisions to ensure consistency, maintainability, performance, and security
• Manage team execution by setting priorities, removing blockers, aligning timelines, and ensuring predictable delivery of high-quality software and AI capabilities
• Contribute directly as a senior individual contributor when needed, especially in system design, critical implementation work, debugging, and platform hardening
• Build and improve systems for auditability, governance, explainability, and measurable business outcomes across intelligent decisioning workflows
• Support experimentation and research-to-production transition for new capabilities in autonomous agents, knowledge systems, simulation, optimization, and model lifecycle management

Qualifications

• Proven experience leading and managing software or AI engineering teams, with a track record of delivering complex technical products from concept through production
• Strong background in backend and platform engineering, with experience building scalable distributed systems, APIs, event-driven architectures, and cloud-native applications
• Hands-on experience with AI/ML systems, especially in one or more of the following areas: multi-agent systems, LLM application development, fine-tuning, retrieval-augmented systems, model evaluation, or knowledge distillation
• Experience designing and operating production systems that combine software engineering with applied machine learning or generative AI components
• Strong understanding of system architecture, orchestration patterns, observability, performance optimization, and software lifecycle management
• Experience leading technical teams through ambiguity, balancing research-oriented work with delivery discipline and production constraints
• Ability to mentor engineers, foster collaboration, and create a high-performance team culture grounded in accountability, learning, and technical excellence
• Excellent communication skills, with the ability to explain complex technical concepts to engineers, product leaders, and executive stakeholders
• Experience working with Python, TypeScript, distributed data systems, ML frameworks, and modern infrastructure tooling is strongly preferred
• Familiarity with federated learning frameworks, parameter-efficient fine-tuning methods, model compression, evaluation pipelines, and secure or privacy-aware AI deployment is highly desirable

Bachelor’s or higher degree in Computer Science, Software Engineering, Machine Learning, or a related field; advanced industry or research experience is a strong plus

What Success Looks Like

You build and lead a team that consistently ships high-quality AI platform capabilities

You create a strong engineering culture that combines experimentation with production rigor

You help iQueue scale its multi-agent and intelligent systems architecture into reliable, measurable business outcomes

You ensure our AI initiatives are not only innovative, but also maintainable, auditable, and deployable in real-world enterprise settings

This is an opportunity to lead at the intersection of *AI engineering, distributed systems, and applied intelligence**. At iQueue, you will help define how advanced agentic systems, privacy-preserving learning, and efficient LLM deployment come together in production. If you are excited by building teams, shaping architecture, and delivering next-generation AI systems with real operational impact, we would love to hear from you.*

Pay: $117,739\.26 - $141,793\.52 per year

Benefits:

• Flexible schedule
• Paid time off

Work Location: Remote