- Role Overview - Lead and grow a high-performing engineering team building and operating Bidgely's cloud-native applications and services. - Bridge technical execution and organizational leadership, ensuring scalable, reliable, and impactful product delivery. - Requires strong technical expertise in backend development (Java, Python, Agentic AI) with frontend familiarity, plus leadership skills to inspire and manage the team. - Serve as a "player-coach," stepping in to code, unblock deployments, decode legacy systems, and mentor engineers as needed. - Proactively identify team needs and actions, rather than waiting for instruction. - Report to the Head of Data Engineering. - Responsibilities include leading and mentoring engineers across backend, frontend, GenAI, MLOps, and Data platform layers. - Own end-to-end delivery of engineering initiatives for Bidgely's cloud application stack. - Drive technical strategy, architecture, and engineering best practices. - Manage project delivery, sprint planning, roadmap execution, and stakeholder communication. - Champion adoption of AI, LLM, and Agentic AI within products and engineering workflows. - Collaborate with Product, Data Science, Infrastructure, and QA to deliver high-quality solutions. - Foster a culture of ownership, continuous improvement, and psychological safety. - Drive migration from legacy code (JAR/WAR) to containerized, cloud-native microservices. - Manage workflows using Jira and Confluence for sprint hygiene and documentation. - Combine deep technical engagement with people leadership. Key Responsibilities - Engineering Leadership & People Management - Lead, manage, and grow a full stack engineering team focused on technical quality, ownership, and collaboration. - Conduct regular 1:1s, give feedback, and create growth plans for team members. - Foster a culture of curiosity, accountability, psychological safety, and learning. - Recruit, interview, and onboard new engineering talent. - Shield the team from distractions while aligning with business goals. - Recognize technical achievements and enable engineers’ best work. - Full Stack Technical Leadership - Provide technical guidance across backend (Java, Python), frontend, APIs, and data layers. - Drive architectural decisions for scalable, maintainable cloud-based systems. - Enforce engineering standards: code quality, testing, documentation, peer review. - Lead design reviews, ensuring solutions are performant, secure, and production-ready. - Stay up to date with evolving full stack tech and evaluate new tools for the stack. - Address technical debt strategically. - Modernize legacy codebases and lead re-platforming to containerized AWS deployments. - Enforce SOLID principles, design patterns, and best practices. - Conduct code reviews and provide senior-level support as required. - Mentor both junior and senior engineers, bridging skill gaps and guiding problem-solving. - Project & Delivery Management - Own engineering project delivery from scoping to post-launch monitoring. - Lead sprint planning, backlog grooming, and milestone tracking. - Identify and resolve risks, dependencies, and blockers proactively. - Maintain clear communication with leadership and stakeholders on delivery status. - Improve delivery velocity through process optimization, automation, and tooling enhancements. - Cloud-Native Application Development - Guide development and operation of full stack applications on cloud platforms (AWS, Azure, GCP). - Ensure best practices in microservices, containerization, CI/CD, and infrastructure-as-code. - Partner with Infrastructure for defining service contracts and operational standards. - Drive observability: structured logging, tracing, and alerting. - Ensure services meet reliability, scalability, and security standards. - AI, LLM & Agentic AI Enablement - Champion AI/LLM adoption in workflows: code generation, testing automation, productivity tools. - Collaborate with Data Science and Product teams for LLM-powered product features. - Guide responsible adoption of AI-assisted dev practices. - Stay updated on generative AI, LLM frameworks (LangChain, Strands), Agentic AI architectures. - Ensure engineering readiness for AI-powered product capabilities. - Cross-Functional Collaboration - Serve as main engineering liaison with Product, Data Science, Design, QA, and Infrastructure teams. - Participate in roadmap planning and provide engineering input for product decisions. - Support customer-facing technical discussions. - Collaborate with security on application-layer security standards. - Engineering Execution & Tooling - Own Jira and Confluence for sprint management and documentation. - Ensure engineers have clear task and priority visibility and acceptance criteria. - Drive continuous improvement via retrospectives, sprint reviews, and capacity planning. Success Metrics - Team Health & Growth - High team engagement, retention, and satisfaction levels. - Growth plans for all engineers with measured progress. - Effective, on-schedule hiring of top engineering talent. - Engineering Delivery - On-time, high-quality delivery of sprint and quarterly commitments. - Reduction in escaped defects and production incidents. - Improvement in lead time, cycle time, and deployment frequency. - Technical Excellence - Improvement in codebase health metrics (test coverage, code reviews, documentation). - Adoption of AI/LLM-enabled development tools for improved productivity. - Engineering systems meeting reliability, scalability, and security targets. Qualifications & Experience - Education: Bachelor's in Computer Science, Software Engineering, or related field (Master's a plus). - Minimum 8 years software engineering experience, with 2–4 as Engineering Manager or Tech Lead Manager. - Proficient as a full stack engineer: Java, Spring Boot, Spark, React JS, and Python (additional language experience a plus). - Experience managing and growing engineering teams (5–15 engineers). - Track record of delivering complex multi-team software projects. - Cloud-native application experience (AWS, Azure, GCP). - Strong project management, Agile/Scrum, and stakeholder management skills. - Preferred: Hands-on LLM API integration, agentic workflows, or AI/ML in product engineering. - Preferred: Experience in regulated industries or enterprise SaaS. - Proven ability to read/decode legacy codebases (JAR/WAR, monolithic) and lead migration to containerized microservices (Docker, Kubernetes). - Deep understanding of event-driven and distributed systems, messaging, queuing, consistency, and fault tolerance. - Highly motivated self-starter; fills process gaps proactively. - Hands-on proficiency with Jira and Confluence for sprint ceremonies, backlog, and documentation standards. - Technical & Domain Expertise - Strong backend proficiency: Java (Spring Boot, microservices), Python (FastAPI, Flask, or equivalent). - Solid frontend and API design skills (React, Angular, REST, GraphQL). - Experience with cloud-native patterns: containerization, CI/CD, serverless architectures. - Familiar with relational/NoSQL databases, data modeling, and optimization. - Working knowledge of cloud platforms, deployment models, and cost management. - Understanding of software security, authentication/authorization, and OWASP best practices. - Exposure to AI/ML and LLM frameworks (LangChain, LlamaIndex, OpenAI API); agentic AI architectures is a strong advantage. - Familiarity with observability tools (Datadog, Prometheus, ELK Stack). - Experience with legacy Java app packaging, deployment, and migration to containers. - Command of SOLID principles, design patterns (GoF), distributed architecture, message brokers (Kafka, RabbitMQ), and orchestration. - Core Competencies - Deep technical skill with leadership experience. - People-first leadership, promoting growth and best work. - Ownership of delivery, removing blockers proactively. - AI & LLM fluency for engineering and product integration. - Ability to translate technical complexity for cross-functional stakeholders. - Growth mindset, continuous learning, comfortable with ambiguity. Why Join Bidgely? - Shape the future of clean energy and sustainability. - Work in a fast-paced, diverse, cross-functional environment. - Competitive pay, benefits, and growth opportunities. - Make an impact at the intersection of AI, data, and climate tech. Diversity, Equity, Inclusion and Equal Opportunity - Bidgely values unique backgrounds, perspectives, and abilities to benefit the company, clients, and community. - Commitment to an inclusive workplace where all can grow and contribute.
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