Tech Lead (Development Lead)

Job description

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.