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