Backend Engineering Lead

As a Backend Engineering Lead you'd be able to work on and solve many interesting challenges which we are facing, learn new ways of working, and help build delightful high quality products for our customers.  

Key Responsibilities

Technical Vision and Strategy

• Defines and communicates the technical vision and strategy for the engineering team, aligning it with overall business objectives.
• Evaluates emerging technologies and industry trends — including AI/ML capabilities, LLM-based tooling, and agentic frameworks — to inform technical roadmaps and architectural decisions.

 

Team Leadership and Mentorship

• Leads and mentors a team of engineers, fostering a culture of collaboration, innovation, and continuous learning.
• Provides guidance and support to team members, empowering them to grow both technically and professionally.
• Builds AI literacy across the team — ensuring engineers can effectively leverage AI-assisted development tools (code generation, automated review, test generation) while maintaining critical thinking about AI outputs.
• Coaches engineers on prompt engineering, AI pair-programming workflows, and when to trust vs. verify AI-generated code.

 

Project Management and Execution

• Manages technical projects from inception to delivery, overseeing planning, execution, and quality assurance processes.
• Coordinates with cross-functional teams, including product management, design, and QA, to ensure successful project outcomes.
• Incorporates AI-augmented estimation and planning tools where appropriate, while maintaining accountability for delivery commitments.

 

Architecture and Design

• Designs and implements scalable, maintainable, and extensible software architectures, considering factors such as performance, security, and scalability.
• Leads architectural discussions and collaborates with senior engineers to drive consensus on design decisions.
• Architects systems with AI integration patterns in mind — designing clean boundaries for AI/ML components, defining contracts between deterministic and probabilistic systems, and planning for model versioning, fallback strategies, and graceful degradation.

Technical Excellence

• Sets high standards for code quality, performance, and maintainability, and ensures adherence to best practices and coding standards.
• Conducts code reviews and provides constructive feedback to ensure the delivery of high-quality software solutions.
• Establishes guardrails and review standards for AI-generated code — ensuring it meets the same quality bar as human-written code, with particular attention to security, edge cases, and maintainability.
• Champions responsible AI practices: output validation, bias awareness, hallucination mitigation, and appropriate human-in-the-loop controls for critical paths.
  
  

Operational Ownership

• Takes end-to-end ownership of the systems the team builds — from deployment through production reliability, performance, and incident response.
• Drives operational maturity: SLOs/SLIs, alerting hygiene, runbook coverage, on-call readiness, and post-incident reviews that produce lasting improvements, not just action items.
• Ensures the team builds with production in mind from day one — observability, graceful degradation, and failure modes are first-class design concerns, not afterthoughts.
• Instils a "you build it, you run it" culture where engineers feel genuine accountability for what they ship and how it behaves under real-world conditions.

 

Problem-Solving and Decision-Making

• Demonstrates advanced problem-solving skills, leveraging deep technical expertise and critical thinking to address complex technical challenges.
• Makes informed decisions under uncertainty, weighing technical trade-offs and considering long-term implications.
• Knows when AI is the right tool for a problem and when it isn't — resists the pressure to "add AI" where simpler, more deterministic solutions are more reliable and cost-effective.
• Leverages AI-assisted analysis (log analysis, incident correlation, root cause detection) to accelerate debugging and reduce mean time to resolution.

 

Stakeholder Management and Communication

• Builds strong relationships with stakeholders, including product managers, business leaders, and other engineering teams, to understand requirements and priorities.
• Communicates technical concepts and project status effectively to both technical and non-technical stakeholders, adapting communication style as needed.
• Translates AI capabilities and limitations into business terms — sets realistic expectations around what AI can deliver, timelines for AI-powered features, and the ongoing cost of maintaining AI systems (compute, data quality, model drift).
• Educates stakeholders on the difference between demo-grade AI and production-grade AI, managing enthusiasm without stifling innovation.

 

Risk Management and Mitigation

• Identifies and mitigates technical risks proactively, anticipating potential issues and implementing contingency plans to minimize impact on project timelines and deliverables.
• Maintains a focus on security, privacy, and compliance requirements, ensuring that systems and processes adhere to relevant standards and regulations.

 

Continuous Improvement and Innovation

• Drives a culture of continuous improvement and innovation within the engineering team, encouraging experimentation, knowledge sharing, and adoption of new technologies.
• Promotes automation, tooling, and process enhancements to improve efficiency, reliability, and scalability of development workflows.
• Runs structured experiments with AI tools and workflows — measuring real impact on velocity, quality, and developer experience rather than relying on anecdotal evidence.
• Builds internal knowledge sharing around AI use cases, effective patterns, and lessons learned — creating a feedback loop that accelerates the team's AI maturity.
• Actively contributes to agentic AI workflows for automating repetitive engineering tasks (test generation, migration scripts, documentation, incident triage) while maintaining human oversight on critical decisions.
  
  

Leadership and Influence

• Leads by example, demonstrating integrity, accountability, and a commitment to excellence in all aspects of work.
• Inspires and motivates team members to achieve their full potential, fostering a positive and inclusive team culture.
• Positions the team as AI-forward without being AI-reckless — models the discipline of evaluating AI tools critically, adopting what works, and discarding what doesn't.
• Shapes the engineering organization's AI adoption narrative, contributing to company-wide standards, governance frameworks, and best practices for AI in production systems.

 

Role Specific Technical Competencies:

• Strong technical background in (AWS, Spring Boot, Kotlin, K8s, Microservices, Kafka, Istio, event-driven architecture).
• Exposure to Cloud Technologies, SRE (Site Reliability Engineering), and DevOps concepts with a focus on automation.

Our Ideal Candidate: 

• 10+ years of overall experience with the majority of this experience as an Engineering Lead or Manager.
• Experience working in Fintech / Financial organisations is an added advantage.
• At least 5 years of demonstrable leadership experience, at an individual team level and working across demographically distributed teams and organizational boundaries.
• Good experience in creating actionable strategies, with clear outcomes, and continuous measurement of progress.
• Ability to produce and communicate effective and insightful thought leadership on technical topics.
• Strong technical background in (AWS, Spring Boot, Kotlin, K8s, Microservices, Kafka, Istio, event-driven architecture).
• Exposure to Cloud Technologies, SRE (Site Reliability Engineering), and DevOps concepts with a focus on automation.
• Ability to take ownership and deliver results in a challenging client-facing environment.
• A degree in a relevant area of study would be helpful but not mandatory