Talantix.io
5CA People•02.03.26
AI SCORE 8.5

Engineering Team Lead - AI-native SaaS Platform

$140K–$200K/year
RemoteAI/Big Data
Software Engineering•AI Development•SaaS•Azure•API Development•CI/CD•Observability•Multi-Tenancy

About the Role

We are seeking an Engineering Team Lead to join our remote-first team at 5CA People. This Engineering Team Lead remote position is pivotal as we build and release the next set of AICX capabilities into market-ready products. You will play a crucial role in maturing existing solutions, shipping new features, and enhancing technical execution and architectural quality.

What You'll Do

  • Own technical direction, architecture, and code quality for our AI-native SaaS platform.
  • Make formal decisions on architectural, technical, and product trade-offs.
  • Stabilize and mature the existing platform while continuing feature development.
  • Run predictable release cycles with pragmatic quality standards.
  • Drive improvements in reliability, security, and observability across the platform.
  • Manage engineers and support their career development.
  • Prepare the team for future scaling and potential outsourcing.

Requirements

  • 7+ years of professional software engineering experience, with 2+ years in an engineering leadership role.
  • Experience building and operating multi-tenant SaaS platforms in production.
  • Proven experience with AI-native software development tools.
  • Technical MSc degree or equivalent practical experience.
  • English proficiency at C1 level or higher (written and spoken).

Nice to Have

  • Experience with Azure-native SaaS architecture.
  • Familiarity with API-first, module-oriented systems.
  • Knowledge of observability and telemetry-driven systems.

What We Offer

  • Competitive salary range of $140,000 - $200,000 per year.
  • Remote work flexibility with a global team.
  • Opportunities for professional growth and development.
  • Supportive and inclusive company culture.
  • Access to cutting-edge technology and tools.
Language Requirements
EnglishC1
BasicIntermediateAdvancedNative
Why This Job8.5 of 10

This Engineering Team Lead role offers a unique opportunity to lead AI-native SaaS development in a remote-first environment, with competitive compensation and significant growth potential.

Salary Range
Required
0/1
Optional
0/1
Bonus
0/1

Who Will Succeed Here

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Deep expertise in AI development with hands-on experience in building AI-native SaaS applications using Azure, ensuring familiarity with machine learning frameworks like TensorFlow or PyTorch.

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Strong understanding of CI/CD practices and tools such as Jenkins or Azure DevOps, enabling efficient and automated deployment processes in a remote work environment.

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Proven ability to lead a geographically distributed team, demonstrating self-motivation, excellent time management, and a proactive approach to problem-solving in a fast-paced technology landscape.

Learning Resources

→AI for Everyonecourse
→Building Scalable SaaS Applications on Azureguide
→CI/CD for Beginnersarticle

Career Path

Engineering Team Lead - AI-native SaaS Platform(Now)→Engineering Manager or Director of Engineering(2-4 years)→Chief Technology Officer (CTO)(5-7 years)

Market Overview

Market Size 2024
$1.5B
Annual Growth
22.5%
AI Adoption in SaaS
65%
Investment in AI SaaS
+150%
Labour Demand for AI Engineers
+35%
Avg Salary for Engineering Team Lead
$160K

Skills & Requirements

Required
Software EngineeringAI DevelopmentSaaS
Growing in Demand
Machine Learning Operations (MLOps)Cloud SecurityData Engineering
Declining
Traditional On-Premise DevelopmentMonolithic Architecture

Domain Trends

Rise of AI-native SaaS Solutions
Over 75% of new SaaS applications are integrating AI functionalities to enhance user experience and efficiency.
Shift to Multi-Tenancy Architectures
The adoption of multi-tenancy in SaaS platforms has increased by 40% as companies seek to optimize resource usage and scalability.
Emphasis on Observability in DevOps
Companies are investing 30% more in observability tools to improve system reliability and performance, reflecting the growing complexity of AI systems.

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