Full Stack Software Engineer-Manufacturing Digital Engineering

We are seeking a highly skilled, collaborative, and forward-thinking AI/ML Full Stack Engineer to join our product team. In this role, you will deliver robust full-stack development leveraging cloud-native microservices on GCP, while also driving AI Engineering initiatives — designing and integrating GenAI models, agentic workflows, and ML pipelines to solve complex business challenges.

You'll work closely with a team of engineers who use automated CI/CD pipelines to continuously ship clean, secure, and production-ready code. A key aspect of this role is building strong observability and telemetry into our systems — collecting both operational metrics and business metrics through logging, monitoring, tracing, and alerting — to ensure performance, reliability, and rapid issue detection across our platforms.

The ideal candidate is passionate about software craftsmanship, building scalable and high-performance applications, and staying current with cutting-edge AI capabilities.

Full Stack Engineering

- Design and develop responsive, performant web applications using Angular/React, TypeScript, and modern frontend frameworks

- Build scalable backend services and RESTful APIs using Spring Boot, Java, and microservices architecture

- Build reusable frameworks and work closely with DevOps to ensure the platform is highly available, scalable, and fault tolerant

- Migrate existing legacy applications to GCP and modernize codebases to current frameworks (Spring Boot, Angular/React)

- Conduct code reviews and ensure adherence to standards, design patterns, and architecture principles

AI/ML Engineering

- Contribute to AI-driven initiatives, including integrating GenAI capabilities and agentic workflows within the GCP ecosystem

- Design, develop, and deploy ML models using Python, TensorFlow, PyTorch, or scikit-learn

- Build end-to-end ML pipelines — data preprocessing, feature engineering, model training, evaluation, and deployment on Vertex AI

- Collaborate with product managers and stakeholders to translate business problems into AI/ML solutions

Observability & Telemetry

- Build strong observability into the platform, including automated performance monitoring, logging, and distributed tracing (e.g., Splunk, Dynatrace, OpenTelemetry)

- Instrument systems to collect both operational metrics (latency, error rates, throughput) and business metrics (usage patterns, adoption, value delivery)

- Ensure high availability, quick issue detection, and reliable production support through proactive alerting

Quality & DevOps

- Actively participate in Test-Driven Development (TDD), CI/CD, and DevOps practices as part of software craftsmanship and Agile XP

- Automate unit, integration, and performance testing (JUnit, Selenium/Playwright) and ensure application security through SAST/DAST practices

- Implement robust CI/CD processes, quality gates, and maintain high code coverage standards

Data Engineering (Supporting)

- Work with data pipelines and cloud-based data storage/processing technologies for handling large datasets

- Leverage GCP data services (BigQuery, Dataflow, Pub/Sub) for analytical and ML workloads

- Apply data preprocessing, cleaning, and feature engineering to prepare data for model training

Required

- Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent experience)

- 5+ years of professional software development experience

- Strong proficiency in Java (Spring Boot, Spring Cloud, Spring Security) and front-end frameworks (Angular or React, TypeScript)

- Experience building and deploying cloud-native applications on Google Cloud Platform (Cloud Run, App Engine, Cloud Functions, BigQuery, Pub/Sub)

- Hands-on experience with Python for AI/ML development using frameworks such as TensorFlow, PyTorch, or scikit-learn

- Experience with microservices architecture, REST API design, and containerization (Docker, Kubernetes)

- Proven experience with TDD methodology, CI/CD pipelines (GitHub Actions, Jenkins, Tekton), and code quality tools (SonarQube, Checkmarx)

- Experience building observability solutions — monitoring, logging, tracing, and alerting for both operational and business metrics

- Proficiency with version control (GitHub) and Infrastructure as Code concepts (Terraform is a plus)

- Strong problem-solving, analytical, and communication skills

- Experience working in Agile/XP environments

Preferred

- Experience with Generative AI/LLM applications, prompt engineering, or agentic AI workflows

- Familiarity with Vertex AI, AI Platform, or similar managed ML services

- Experience with data pipeline tools (Dataflow, DBT, Astronomer/Airflow)

- Knowledge of MLOps practices — model versioning, experiment tracking, model serving

- Experience with databases (PostgreSQL, BigQuery, MongoDB) and data modeling

- Familiarity with Tekton and Terraform for CI/CD and infrastructure provisioning

- GCP Professional certifications (Cloud Engineer, ML Engineer, or Data Engineer)