Senior Technical Lead

We are looking for an experienced Technical Lead with demonstrated technical expertise and strong leadership capabilities to drive the design, development, integration, and operational excellence of complex technology platforms.

This role requires a hands-on technical leader who can balance strategic direction with execution, ensure engineering quality, manage competing priorities, and drive continuous improvement across the technology landscape.

The candidate should be forward-thinking and pragmatic in leveraging Artificial Intelligence (AI), Generative AI, and automation to improve engineering productivity, system reliability, operational efficiency, and service delivery.

Key Responsibilities

1. Technical Leadership and Architecture

• Lead the design, development, enhancement, and integration of complex enterprise applications and technology platforms.
• Define and enforce technical standards, architectural principles, coding practices, and engineering best practices.
• Evaluate technology choices and provide recommendations aligned with business objectives, scalability, security, and maintainability.
• Drive integration across custom-built web applications, cloud services, APIs, and packaged enterprise solutions.
• Identify technical risks, dependencies, architectural bottlenecks, and opportunities for modernization.
• Provide hands-on technical guidance for complex problem-solving, troubleshooting, and root-cause analysis.

2. Engineering

• Provide technical direction across front-end, back-end, database, API, and integration layers.
• Guide teams in developing secure, scalable, resilient, and high-performing applications.
• Oversee cloud-based application architecture, deployment, monitoring, and operational support across relevant cloud platforms.
• Drive CI/CD, DevOps, infrastructure automation, observability, and application lifecycle management.
• Ensure adherence to security, performance, availability, disaster recovery, and data protection requirements.
• Support the evaluation and integration of packaged solutions, enterprise platforms, and third-party products.

3. Delivery Management

• Lead, mentor, and develop engineers, promoting technical excellence, accountability, and continuous learning.
• Prioritize work based on business value, urgency, risk, technical dependencies, and resource availability.
• Establish clear ownership, delivery expectations, milestones, and measurable outcomes.
• Manage delivery risks, resolve technical blockers, and escalate issues proactively.

4. Stakeholder and Cross-Functional Management

• Collaborate with business stakeholders, product managers, solution architects, operations, security, and external vendors.
• Translate business requirements into technical solutions, implementation plans, and actionable engineering tasks.
• Communicate technical issues, risks, dependencies, and trade-offs clearly to both technical and non-technical audiences.
• Manage competing stakeholder expectations and negotiate priorities based on business impact and technical feasibility.

5. Operational Excellence and Service Reliability

• Drive systematic incident management, problem management, root-cause analysis, and preventive action planning.
• Ensure that teams follow established troubleshooting, change management, code review, testing, release, and deployment processes.
• Promote evidence-based investigation rather than rushed fixes or assumptions.
• Establish operational metrics and service health indicators to monitor application performance, availability, incident trends, and delivery effectiveness.
• Reduce recurring incidents, technical debt, manual effort, and avoidable operational overhead.
• Establish knowledge-sharing practices, technical documentation, runbooks, and continuous improvement reviews.

6. AI Adoption and Engineering Innovation

• Leverage AI-assisted development tools to improve coding productivity, code reviews, test generation, documentation, and troubleshooting.
• Explore AI-driven approaches for incident triage, log analysis, anomaly detection, knowledge retrieval, and operational support.
• Evaluate opportunities for AI-powered workflows, intelligent automation, and agent-assisted operations where appropriate.
• Measure the impact of AI initiatives through tangible improvements in productivity, service quality, resolution time, and operational efficiency.