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.