We are looking for an experienced Senior Software Full Stack Engineer to join our engineering team. This role is critical to designing, developing, and delivering scalable, high-quality software solutions that power our products. You will work at the intersection of business strategy and technical execution — collaborating with Product Owners, Product Managers, and Architects to translate business needs into optimal technical solutions, while championing engineering excellence, Agile best practices, and the adoption of agentic AI tools across the software development lifecycle (SDLC).

This role is ideal for an engineer who is not only technically strong in full stack and microservices development, but also curious and proactive about integrating agentic AI development practices — such as AI coding agents, autonomous/semi-autonomous workflows, and Model Context Protocol (MCP) servers — into everyday engineering work to improve speed, quality, and consistency.

Responsibilities

- Solution Design & Development: Work closely with Product Owners, Product Managers, and Architects to design and develop optimal, scalable technical solutions that meet both business and user needs.
- Backlog & Risk Management: Understand business priorities, user needs, and technical feasibility to help prioritize the product backlog, and proactively manage risks and dependencies across features and teams.
- Microservices Development: Design and develop microservices architecture, building loosely coupled services that decompose larger applications into smaller, well-defined, independently deployable components.
- API Development: Design, build, and maintain APIs that enable seamless communication and integration between individual microservices and external systems.
- Code Quality & Automation: Enforce best practices in refactoring, continuous integration, and automation to ensure code is clean, maintainable, and scalable over time.
- Agile & DevOps Practices: Actively participate in Daily Stand-ups and other Agile ceremonies (sprint planning, retrospectives, backlog grooming). Champion the adoption of Agile, DevOps, and software craftsmanship practices, including CI/CD, Test-Driven Development (TDD), and Pair Programming.
- Agentic AI in the Development Lifecycle:
- Integrate agentic AI tools and coding assistants (e.g., GitHub Copilot, Claude, Cursor, or similar AI agents) into day-to-day development workflows to accelerate coding, testing, debugging,



documentation, and code review.
- Design and implement agentic development workflows where AI agents autonomously or semi-autonomously handle well-defined engineering tasks (e.g., scaffolding services, generating unit tests, refactoring code, resolving bugs) under human oversight.
- Configure, integrate, and work with Model Context Protocol (MCP) servers to connect AI agents with internal tools, APIs, codebases, and data sources in a secure and governed manner.
- Evaluate and apply prompt engineering and context management best practices to maximize the effectiveness, reliability, and safety of AI-assisted development outputs.
- Establish guardrails, validation steps, and human-in-the-loop checkpoints to ensure AI-generated code meets quality, security, and compliance standards before merging.
- Continuously experiment with and evaluate emerging agentic AI tools, frameworks, and patterns (e.g., multi-agent orchestration, autonomous coding agents, AI-driven CI/CD pipelines), and share learnings and recommendations with the broader engineering team.
- Partner with architects and platform teams to define standards and best practices for responsible, secure, and scalable use of agentic AI across the SDLC.

- Technical Mentorship: Provide technical guidance and mentorship to junior and mid-level engineers, fostering a culture of continuous learning, engineering excellence, and effective AI-augmented development.
- Continuous Improvement: Stay current with emerging technologies, tools, and industry trends — especially in agentic AI and AI-assisted software engineering — and proactively recommend improvements to team processes, architecture, and tooling.

Qualifications

- Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
- 5+ years of professional software development experience, with demonstrated full stack expertise (front-end and back-end).
- Strong experience designing and building microservices architectures.




- Proficiency in one or more contemporary programming languages - Java, JavaScript/TypeScript Python
- Experience with front-end frameworks (React, Angular) and back-end frameworks (Spring Boot, Node.js)
- Solid understanding of RESTful API design and development; experience with API gateways and service-to-service communication.
- Hands-on experience with CI/CD pipelines and DevOps tooling
- Practical, hands-on experience using agentic AI coding tools and assistants (e.g., GitHub Copilot, Claude, Cursor or similar) as part of the daily development workflow.
- Working knowledge of agentic development processes — understanding how AI agents plan, reason, and execute multi-step engineering tasks with appropriate human oversight.
- Familiarity with Model Context Protocol (MCP) servers and how they are used to connect AI agents/LLMs with internal tools, data sources, and codebases.
- Strong understanding of Agile/Scrum methodologies and experience actively participating in Agile ceremonies.
- Practical experience with Test-Driven Development (TDD), unit testing, integration testing, and automated testing frameworks.
- Experience working with GCP cloud platforms.
- Excellent problem-solving skills, with the ability to balance technical feasibility with business priorities.
- Strong communication and collaboration skills, with experience working in cross-functional teams.
- Preferred Qualifications

- Experience building or configuring custom MCP servers/tools to extend AI agent capabilities within an enterprise environment.
- Experience designing multi-agent or orchestrated agentic AI workflows for software engineering tasks (e.g., automated code review agents, test-generation agents, documentation agents).
- Familiarity with prompt engineering, context window management, and retrieval-augmented generation (RAG) concepts as applied to software development.
- Familiarity with event-driven architecture and messaging systems (e.g., Kafka, Pub/Sub).
- Experience with database technologies, both relational (SQL) and NoSQL.
- Prior experience mentoring junior engineers or leading small technical initiatives, including upskilling teams on agentic AI adoption.
- Awareness of governance, security, and responsible-AI considerations when integrating AI agents into enterprise development pipelines.
- Automotive, manufacturing, or enterprise-scale technology experience is a plus.

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