Engineering Lead / Solution Architect

Role Overview

We are looking for a hands-on Engineering Lead / Solution Architect to lead the design, development, and production readiness of AI-powered and SaaS applications. The role combines solution architecture, technical decision-making, hands-on coding, code quality ownership, and developer mentoring. This is a technical leadership role rather than a project management position.

Key Responsibilities

Solution Architecture & Technical Design

• Design scalable, secure, maintainable SaaS and AI application architectures.
• Translate business requirements into technical designs and implementation plans.
• Define frontend, backend, API, database, and integration architecture; select appropriate technologies and infrastructure.
• Create architecture diagrams, technical specifications, and Architecture Decision Records (ADRs).
• Assess feasibility, scalability, performance, security, and technical risks before development begins.

Technical Leadership & Code Quality

• Lead and mentor a team of approximately 67 software developers, including junior engineers.
• Review code and pull requests; establish coding standards, Git workflows, and branch protection practices.
• Write production-quality code for complex or critical modules and resolve challenging technical issues.
• Identify design flaws, security vulnerabilities, performance issues, and technical debt.
• Guide developers on debugging, refactoring, testing, and maintainable software design.

AI-Assisted Development & AI Architecture

• Define standards for responsible AI-assisted coding using tools such as GitHub Copilot, Cursor, Claude Code, or similar tools.
• Review AI-generated code for correctness, security, maintainability, and performance.
• Establish safe practices for handling confidential information, source code, and credentials when using AI tools.
• Design and review AI-powered application architectures, including Retrieval-Augmented Generation (RAG) solutions.
• Guide document ingestion, embeddings, vector search, retrieval pipelines, and LLM API integrations.
• Evaluate AI response quality, latency, security, and operating costs; support integrations with ML or computer vision systems when required.

DevOps, Security & Production Readiness

• Define CI/CD pipelines, deployment standards, and development, staging, and production environments.
• Work with cloud platforms such as AWS, Azure, or DigitalOcean and container technologies such as Docker.
• Establish secure configuration and secrets management, logging, monitoring, alerting, and error tracking.
• Review authentication, authorization, database migrations, backups, disaster recovery, and rollback procedures.
• Support production incidents, root-cause analysis, and release-readiness reviews.

Quality Assurance, Mentoring & Delivery

• Define unit, integration, API, and end-to-end testing standards and promote automated test coverage.
• Work with QA to define release acceptance criteria and prevent releases with critical unresolved defects.
• Mentor developers through code reviews, knowledge-sharing sessions, and structured feedback.
• Break complex features into manageable technical tasks, estimate effort, and identify dependencies.
• Coordinate technical work across products and report progress, risks, and engineering quality metrics.

Required Technical Skills

• Backend development with Python, Node.js, TypeScript, or equivalent technologies.
• REST API design, authentication, authorization, API security, and microservices or modular-monolith architecture.
• Working knowledge of React, Next.js, or similar frontend frameworks.
• Relational databases such as PostgreSQL, including schema design, indexing, migrations, and query optimization.
• Understanding of vector databases, caching, LLM APIs, RAG, embeddings, and retrieval pipelines.
• Docker, CI/CD tools such as GitHub Actions, Linux administration, cloud infrastructure, monitoring, and incident management.
• Git and pull-request workflows, SOLID principles, design patterns, automated testing, secure coding, and OWASP principles.
• Technical documentation, architecture diagrams, and technical decision records.

Required Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or equivalent practical experience.
• 8–12 years of software engineering experience, including at least 3 years in technical leadership or solution architecture.
• Demonstrated experience delivering and supporting production applications.
• Strong hands-on coding, debugging, system design, API architecture, and database design skills.
• Experience leading and mentoring developers, with practical knowledge of cloud deployment, CI/CD, and application security.
• Strong technical communication and documentation skills.

Preferred Qualifications

• Experience building SaaS products from initial development through production.
• Experience with multi-tenant applications and AI-powered products using RAG or LLM integrations.
• Familiarity with Supabase, PostgreSQL, Docker, DigitalOcean, Coolify, or similar deployment platforms.
• Experience governing AI-assisted development teams and improving observability, security testing, and performance.
• Experience building reusable shared services or platforms used by multiple products.

Expected Outcomes

• Approved architecture diagrams, technical specifications, and engineering standards.
• Consistent code review practices, improved code quality, and controlled technical debt.
• Reliable build, test, deployment, and production-release processes.
• Clear technical task breakdowns, estimates, and engineering status reporting.
• Structured developer mentoring and measurable improvement in team capability.
• A practical technical improvement roadmap and production-readiness plan.

First 90 Days

• Days 1–30: Assess existing architecture, repositories, developer capabilities, engineering practices, and critical risks; establish coding and review standards.
• Days 31–60: Improve architecture consistency, testing, CI/CD, release controls, and developer mentoring; address high-priority technical risks.
• Days 61–90: Establish a repeatable release process, validate production readiness for a priority product, report engineering metrics, and present a six-month improvement roadmap.

Success Measures

• Reduction in recurring defects, security findings, and maintainability issues.
• Improved production stability, incident recovery, delivery predictability, and review effectiveness.
• Increased automated test coverage and developer independence.
• Consistent architecture standards, reduced high-risk technical debt, and controlled infrastructure/AI service costs.