Full Stack Software Engineer-Manufacturing Digital 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

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

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

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

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

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

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)