Lead Java Cloud Engineer - AWS, Microservices, AI Tools

ARCHER; Palo Alto (CA); On-site; USD 180,000 - 230,000; Full time

Benefits offered by this job

Health Care Coverage

On‑Site Health And Wellness Centers

Retirement Savings Plan

Backup Childcare

Tuition Reimbursement

Mental Health Support

Financial Coaching

Job summary

ARCHER is seeking a senior engineering leader to direct an Agile team responsible for designing, building, testing, and operating secure, scalable technology products.

You will provide technical guidance, influence product and operational decisions, and promote governed, responsible use of AI‑assisted engineering tools and SDLC practices to improve code quality, delivery speed, automation, and operational outcomes.

Qualifications

• Requires formal software engineering training or certification and at least five years of applied experience across system design, development, testing, and operations.
• Candidates need advanced Java, microservices, Kafka, AWS, and PostgreSQL or similar skills, practical cloud‑native experience, and demonstrated leadership in secure, responsible use of enterprise‑authorized AI development tools.

Responsibilities

• Lead an agile engineering team in designing, building, testing, and operating secure, scalable technology products.
• Provide technical guidance and influence product and operational decisions; promote governed, responsible use of approved AI‑assisted engineering tools and SDLC practices.

Skills

Java

Microservices

Kafka

AWS

PostgreSQL

Cloud‑Native Development

System Design

Application Development

Software Testing

Operational Stability

AI-Assisted Software Development

Secure Coding

Code Review

Responsible AI Use

Technical Leadership

Agile Development

Education

Software engineering training or certification

Job description

ARCHER is seeking a senior engineering leader to direct an Agile team responsible for designing, building, testing, and operating secure, scalable technology products.

You will provide technical guidance, influence product and operational decisions, and promote governed, responsible use of AI‑assisted engineering tools and SDLC practices to improve code quality, delivery speed, automation, and operational outcomes.