Lead Software Engineer

• Lead Greenfield E-Commerce Development: Serve as a hands-on Lead Engineer, driving the architecture, technical design, and implementation of a complex microservices ecosystem to build a cutting-edge, greenfield open-stack platform for a large-scale e-commerce initiative.
• Modernize Customer Experience Platforms: Spearhead the modernization of technology stacks across core e-commerce domains-including cart/checkout, post-purchase, and customer service platforms-leveraging MACH (Microservices, API-first, Cloud-native, Headless) architecture principles with a focus on controlled data duplication.
• Champion Modern Engineering Practices: Model and advocate for industry-leading software engineering practices-including Test-Driven Development (TDD), Behavior-Driven Development (BDD), CI/CD pipelines, Shift-Left testing, and 12-Factor App design-ensuring seamless integration with both existing Ford software products and external cloud-based services.
• Drive Architectural Collaboration: Partner closely with Ford's Enterprise Architecture organization to evaluate and rationalize technologies, maximize the value of existing enterprise offerings, and proactively identify strategic technology gaps and opportunities.
• Own the Backend Ecosystem: Assume end-to-end responsibility and accountability for API design and the development of a robust backend Spring Boot microservices suite deployed on Google Cloud Platform (GCP).

• Architect & Modernize eCommerce Microservices: Own the overall design and evolution of one or more microservice modules within our eCommerce product suite. Analyze the existing application footprint to architect and recommend solutions that transition workloads into a modernized, future-proof architectural landscape.
• Elevate Commerce Platform Capabilities: Leverage deep commerce platform engineering expertise to significantly enhance Ford's digital capabilities, ensuring our platforms are engineered to scale seamlessly and meet growing global demands.
• Pioneer AI-Assisted Engineering: Lead the strategy, adoption, and integration of Large Language Models (LLMs) and generative AI tools across the development team, establishing best practices and guardrails for secure, high-quality AI-assisted coding.
• Architect Agentic AI Workflows: Design, build, and deploy autonomous AI agents and multi-agent systems (using frameworks like LangChain, CrewAI, or AutoGen) to automate complex development tasks, orchestrate workflows, and reduce cognitive load on engineering teams.
• Optimize the SDLC with AI: Architect and implement AI-driven enhancements throughout the entire Software Development Life Cycle (SDLC)-including automated unit test generation, intelligent CI/CD pipelines, automated code reviews, and predictive bug detection etc.
• Drive Developer Productivity: Model and promote the daily use of advanced AI coding assistants (e.g., GitHub Copilot, custom internal LLMs) to accelerate feature delivery, streamline code refactoring, and automate technical documentation.
• Build Intelligent POCs: Research emerging technologies in the generative AI and Agentic AI space, rapidly building Proofs of Concept (POCs) to evaluate how new AI models and frameworks can solve complex engineering challenges.
• Innovate with Cutting-Edge Technologies: Design and build Proofs of Concept (POCs) utilizing emerging technologies, while actively contributing to the development and deployment of robust, highly scalable, and intelligent cloud-based solutions.

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Leadership Skills:

• Excellent communication and interpersonal skills
• Ability to work effectively in a remote/virtual work setting with other global team members
• Proven facilitation skills - able to effectively drive discussion among diverse perspectives and reach a decision or recommendation
• Effectively work with cross-functional teams across the organization - inside and outside of the technology and software organization

Functional/Technical Skills:

• Proficient in Python (the industry standard for AI orchestration) alongside the core enterprise backend language like Java (Spring Boot)

• Experience with the following: Microservices architectures (using Spring boot/Micronaut/Ratpack/Quarkus), Cloud-Native architectures, Event-driven architectures, APIs, Domain-Driven Design, Public Cloud (Google Cloud), Serverless, Kubernetes, Docker, DevOps, building scalable, reliable, available solutions, and/or performance testing.

• Expert-level daily usage of AI coding assistants (e.g., GitHub Copilot, Cursor, Amazon Q) and experience customizing them for team-wide productivity.

• Advanced expertise in prompt engineering techniques (e.g., Chain-of-Thought, Few-Shot prompting, ReAct) to optimize LLM performance and reliability

• Experience integrating and managing enterprise LLM APIs (e.g., OpenAI, Anthropic Claude, Google Gemini/Vertex AI).

• Strong technical background with the capability of being hands-on

• Conversant in multiple programming languages. Thorough knowledge of multi-threading, concurrency, and parallel processing concepts including scalability, performance, and consistency characteristics of a microservices driven eCommerce architecture

• Good expertise in REST, Messaging (KAFKA, RABBITMQ, cloud pub/sub etc.), stream processing (SPARK, STORM etc.), NoSQL as well as database Systems (RDBMS, NO SQL Stores like Cassandra, HBase, Mongo, Memcached etc.)

• Ability to build or integrate LLMs into CI/CD pipelines for automated code reviews, vulnerability scanning, unit test generation, and automated documentation.

• Experience in Cloud Native systems, Transactional Systems, Multi-Tenancy, five-nines availability and Containerization technologies

• Experience in collaborating and partnering with other technical domain experts such as cloud, security, SRE, and DevOps.

• Experience in building Structured, Semi-Structured and Unstructured data stores with a good understanding of RDBMS, No-SQL databases and strong exposure to data modelling, data access patterns, data replication, active-active polyglot persistence setup.

• Experience in implementation of CQRS and staged event driven applications on Spring integration/Apache Camel/Mulesoft platforms