Java Full Stack Developer

This role is for a Java Full Stack Developer focused on project delivery within Deloitte's Project Delivery Talent Model. Responsibilities include full-stack development using Python, Java, and Go, cloud-native engineering on AWS/GCP, Infrastructure as Code (IaC) with Terraform/Ansible, and embracing SRE principles. The role requires experience with CI/CD, risk management, and collaboration. While the team focuses on AI & Engineering solutions, the core responsibilities of this specific role are centered around traditional software development and cloud infrastructure, not direct AI/ML model building or research.

What you'd actually do

  1. Full Stack Development: Design, develop, and maintain high-quality, scalable, and robust full-stack applications and services using Python, Java, and/or Go.
  2. Cloud-Native Engineering: Build and deploy containerized (Docker, Kubernetes) and serverless applications on AWS and GCP, leveraging cloud-native services for compute, data, and messaging.
  3. Infrastructure as Code (IaC): Develop and manage cloud infrastructure using IaC principles and tools such as Terraform and Ansible to ensure automated, repeatable, and secure environment provisioning.
  4. Operational Excellence (SRE): Embrace a "you build it, you run it" philosophy. Take ownership of the entire lifecycle of your services, including automated testing, deployment, monitoring, and operational support. Proactively identify and resolve root causes of issues to ensure service reliability and performance.
  5. Risk & Control Management: Adhere to Citi's technology standards and risk management frameworks. Identify, assess, and mitigate risks in your designs and code, ensuring all solutions are compliant with security policies and data regulations.

Skills

Required

  • 3+ years of experience in software, systems, or embedded engineering.
  • 3+ years of experience working Java/J2EE
  • 3+ years of experience developing or deploying AI solutions, custom hardware, or high-performance platforms.
  • 3+ years of experience with Linux internals, device drivers, and kernel or embedded systems programming.
  • Strong proficiency in one or more of the following programming languages: Python, Java, Go
  • Strong understanding of AI/ML frameworks (PyTorch, TensorFlow, ONNX) and performance/model optimization.
  • Hands-on experience with public cloud platforms, specifically AWS and/or Google Cloud (GCP).
  • Containerization Technologies: Expertise in Docker and container orchestration platforms like Kubernetes (e.g., Amazon EKS).
  • Infrastructure-as-Code: Demonstrable knowledge of IaC tools such as Terraform and/or Ansible.
  • Strong architectural skills with a focus on building well-engineered, testable, and resilient applications.
  • Experience with automated testing frameworks and a commitment to building quality into the development process.
  • Understanding of Site Reliability Engineering (SRE) practices and experience in an environment with an operational ownership model.
  • Familiarity with hardware-software co-design (ASICs, FPGAs, or SoCs).
  • Demonstrated skill in performance profiling, benchmarking, and system tuning.
  • Knowledge of distributed systems, cloud/edge computing, and containerization (Docker, Kubernetes).
  • Understanding of network protocols, security best practices, and scalable API design.
  • Experience with Git, CI/CD pipelines, and modern DevOps practices.
  • Bachelor's degree, preferably in Computer Science, Information Technology, Computer Engineering, or related IT discipline; or equivalent experience

Nice to have

  • Proven ability to communicate complex technical concepts clearly and effectively.
  • Analytical ability to manage multiple projects and prioritize tasks into manageable work products
  • Can operate independently or with minimum supervision
  • Excellent Written and Communication Skills
  • Ability to deliver technical demonstrations

What the JD emphasized

  • 3+ years of experience developing or deploying AI solutions, custom hardware, or high-performance platforms.
  • Strong understanding of AI/ML frameworks (PyTorch, TensorFlow, ONNX) and performance/model optimization.