Tech Lead (Development Lead)

The Tech Lead is responsible for providing technical leadership to a development team, ensuring software quality, proper architectural implementation, and efficient delivery of technological solutions aligned with business objectives..

Responsibilities:

• Lead the technical development of AI and Machine Learning-based solutions.
• Development of service-based applications, using React for the UI and Django for the backends.
• Design system architectures that integrate ML models into applications and services.
• Coordinate workflows between Software Engineers, Data Scientists, and ML Engineers.
• Oversee the development of data pipelines, model training, and production deployment (MLOps).
• Define standards for development, testing, and model monitoring.
• Perform code reviews and ensure engineering best practices.
• Participate in the selection of AI frameworks and toolsets.
• Ensure system scalability, performance, and reliability.
• Mentor the team in development and ML best practices.
• Provide technical guidance to the developer team.
• Design and define the technical architecture of applications and services.
• Collaborate with Product, QA, and DevOps teams.
• Drive decision-making regarding technologies, frameworks, and tools.

Qualifications:

• 6–10+ years of experience in software development.
• English proficiency: B1+ or higher.
• Bachelor's Degree in Computer Science, Information Systems, or related field.
• Proven experience leading technical teams.
• Hands-on experience with Machine Learning and AI systems.
• Proficiency in languages such as Python, Java, JavaScript, Go, or TypeScript.
• Experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Strong background in software architecture and distributed systems.
• Knowledge of MLOps, CI/CD, and model deployment.
• Experience with cloud platforms (AWS, GCP, or Azure).

Desirable skills:

• Knowledge of DevOps and cloud infrastructure management, including containers and orchestration with Docker and Kubernetes.
• Familiarity with Agile methodologies (Scrum, Kanban) and collaborating in multidisciplinary teams.
• Technical mentoring and leadership abilities, promoting engineering excellence and software quality.
• Experience in testing, validation, and automation of AI models.