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.