The posting
- 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.
