Engineering Lead

How to apply

Email [email protected] with subject: Software Engineer - [Your Full Name]. Include your CV, GitHub/portfolio, and 2-3 examples of production systems you built or shipped. Applications that do not follow this format may not be reviewed.

About the role

We are building Maxify, an AI-native operating platform where AI colleagues execute real business workflows through company systems while remaining accountable to source data, permissions, evaluations, and measurable outcomes.

We are looking for a hands-on Engineering Lead to own the technical foundations, engineering roadmap, and team needed to turn strong demand into a scalable, dependable product. You will move fluidly between architecture, implementation, debugging, product judgment, and people leadership.

This is not a coordination-only management role. You will ship production software hands-on, set the technical bar, and partner directly with the founder on product and engineering strategy.

What you will own

• Own the technical architecture and engineering roadmap.
• Build reliable agent systems across orchestration, memory, tools, permissions, evaluations, observability, and human oversight.
• Lead the design, implementation, testing, deployment, and operation of AI-powered products.
• Ship production software hands-on and lead architecture decisions and code reviews.
• Establish standards for code quality, security, reliability, CI/CD, and speed.
• Partner with product, data, and design teams and resolve technical risks and dependencies.
• Recruit, mentor, and develop engineers.
• Partner directly with the founder on product and technical strategy.

What we are looking for

• Proven experience building and operating complex production software.
• Active hands-on engineering depth.
• Proficiency in Python, Java, or TypeScript.
• Experience designing scalable cloud architecture on AWS, GCP, or Azure.
• Robust CI/CD experience.
• Engineering leadership across architecture, delivery, feedback, and team development.
• Ability to move between coding, debugging, product judgment, architecture, and team leadership.
• Pragmatic trade-offs, clear communication, urgency, reliability, and ownership.
• A degree is welcome, but equivalent practical experience carries equal weight.

Strong signals

• You have personally shipped and operated complex production systems.
• You can explain a major architecture decision, what failed, and how you prevented recurrence.
• You remain hands-on while raising the output and judgment of engineers around you.
• You have established measurable reliability, security, evaluation, observability, or recovery standards.
• You use AI development tools extensively while validating outputs and retaining technical ownership.

Nice to have

• Experience with LLM applications or agent systems.
• Experience with distributed systems, data pipelines, or MLOps.
• Experience with developer platforms or workflow infrastructure.
• Startup or high-growth company experience.

AI leverage expectations

Design and operate AI colleagues that understand business context, execute workflows, use governed tools and permissions, retain appropriate memory, pass evaluations, remain observable, and escalate to humans within clear boundaries.

What this role is not

• People management without hands-on engineering ownership.
• Prompt writing without production software accountability.
• Pure machine-learning research or model training.
• Architecture work disconnected from delivery and measurable outcomes.
• Building speculative infrastructure before real workflows require it.

Success in the first 90 days

• Validate the target architecture and roadmap.
• Ship material production improvements hands-on.
• Establish measurable reliability, security, evaluation, observability, and recovery standards.
• Clarify ownership and delivery cadence.
• Identify the first engineering hiring and capability priorities.

How we work

• We move quickly and build for permanence.
• We prefer small, reversible decisions over speculative architecture.
• Documentation, testing, evaluation, and observability are part of the product.
• AI accelerates the work; it does not remove engineering accountability.
• We measure success through customer outcomes, revenue impact, cost reduction, and lower operational attention.