Job Description

Lead Engineer – Role & Responsibilities
As a Lead Engineer, you will provide technical leadership and architectural direction while remaining hands-on in building scalable, resilient, and production-grade systems. You will play a critical role in shaping engineering standards, driving modernization, and enabling intelligent, AI-powered capabilities across the platform.
Key Responsibilities

• Technology Evaluation & Innovation
• • Evaluate emerging technologies and contribute to architectural decision-making, considering alignment with Target’s technical ecosystem, long-term maintainability, scalability, and total cost of ownership.
  • Lead research initiatives and proof-of-concept efforts to validate new tools, frameworks, and platforms before adoption.
• Architecture & Engineering Excellence
• • Design and own scalable, secure, high-performance architectures.
  • Establish and evolve engineering standards and best practices in complex or ambiguous environments.
  • Lead service design, lifecycle management, and overall technical governance of team-owned platforms.
  • Ensure code quality, infrastructure standards, and long-term sustainability of services.
• Hands-On Development & Delivery
• • Contribute directly to development efforts, particularly on complex and high-impact components.
  • Ensure solutions are production-ready, deployable, resilient, and scalable.
  • Drive implementation quality through strong testing, automation, and CI/CD practices.
• Enterprise Impact & Thought Leadership
• • Provide technical thought leadership to promote reusable components and consistent architectural patterns.
  • Participate in planning and designing services with enterprise-wide impact.
  • Guide the team in resolving routine and moderately complex technical challenges, escalating risks when necessary.
• Observability & Reliability Engineering
• • Champion a culture of observability and operational excellence.
  • Ensure monitoring, logging, and metrics are embedded into services.
  • Leverage operational data to continuously improve system stability, performance, and reliability.
  • Align monitoring strategies with organizational observability principles.
• AI & Intelligent Systems Integration
• • Integrate Large Language Models (LLMs) and Generative AI capabilities into core applications.
  • Design and implement safeguards to mitigate hallucinations and improve AI reliability.
  • Build and scale agentic frameworks and AI-driven automation solutions to enhance business processes.
• Data Governance & Platform Optimization
• • Influence and evolve data standards, policies, and governance practices.
  • Configure, optimize, and monitor data management platforms with minimal oversight.
  • Identify performance and efficiency improvements across data systems.

Required Skills & Expertise

• Advanced proficiency in Java, including backend systems and automation workflows
• Strong experience in Microservices Architecture and distributed systems
• Expertise with Spring Boot or Micronaut, including reactive programming models
• Hands-on experience with AI/LLM integration and GenAI-enabled applications
• Experience with Messaging Systems such as Kafka or RabbitMQ
• Proficiency with Databases, including NoSQL (Cassandra, MongoDB) and SQL (PostgreSQL)
• Experience building and managing CI/CD pipelines (Jenkins, GitLab, or similar)
• Strong background in Unit & Integration Testing (JUnit, Spock, TestContainers)
• Experience with Cloud Platforms (AWS, GCP, Azure)
• Expertise in Containerization & Orchestration (Docker, Kubernetes)
• Strong understanding of Monitoring & Observability tools (Grafana, ELK, Prometheus)
• Solid grasp of Event-Driven Architecture patterns in distributed environments

Preferred Qualifications

• Proficiency in Python or Kotlin
• Experience with Legacy System Modernization and refactoring initiatives
• Knowledge of Security Best Practices, including OWASP standards and secure coding principles
• Familiarity with Agile methodologies such as Scrum or Kanban

Level:
6