Lead Software Engineer – AI

Responsibilities

• Leading the development and implementation of innovative software and web applications, ensuring they align with business objectives and user requirements.
• Integrating new systems seamlessly with existing infrastructure, focusing on scalability, security, and continuous performance improvement.
• Mentoring team members and providing technical training, fostering a collaborative environment for knowledge sharing and professional growth.
• Collaborating with cross-functional teams to ensure successful application integration, advocating for best practices in software development.
• Participating in or leading peer programming sessions, design sprints, or prototyping sessions.
• Driving the creation, maintenance, and accessibility of comprehensive documentation for all development activities.
• Monitoring application performance metrics rigorously, utilizing data to guide enhancements and ensure delivery aligns with project goals.
• Providing expert technical advice and support to internal stakeholders and external partners, effectively communicating complex concepts.
• Collaborating with the Quality Assurance team to confirm applications meet rigorous testing standards and fulfil technical requirements.

• 6+ years of experience in software engineering, with strong focus on AI/ML systems in production.
• Strong programming skills in Python (Go a plus).
• Experience building and deploying Agents or LLM-based applications into production environments.
• Experience with distributed systems and real-time inference architectures.
• Knowledge of core AI domains such as NLP, recommendation systems, or time series modeling.
• Experience with model lifecycle practices (evaluation, monitoring, retraining, and iteration).
• Experience with cloud platforms (AWS, Azure, or GCP) and AI tooling ecosystems.
• Ability to translate business problems into scalable AI solutions and drive implementation end-to-end.

• Software Engineering
• AI/ML Systems
• Python
• Go
• NLP
• Recommendation Systems
• Time Series Modeling
• Real-Time Inference Architectures
• Application Performance Monitoring
• Documentation Creation

• Mentoring
• Collaboration
• Communication