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.
