Machine Learning Engineer Lead

Responsibilities

• Leadership & Mentoring

Lead, mentor, and support a team of Python engineers. Foster a culture of collaboration, continuous improvement, and innovation. Provide technical guidance in developing clean, efficient, and scalable code.

• Strategic & Technical Direction

Collaborate with product managers to define technical requirements and delivery roadmaps. Work with data scientists to integrate and optimize generative AI models. Oversee the design and implementation of high-quality software solutions.

• Collaboration & Communication

Engage regularly with cross-functional teams including product management, data science, and DevOps. Act as the primary technical liaison to ensure smooth communication across teams. Facilitate agile ceremonies and ensure the team meets sprint commitments.

• Project Management & Delivery

Define project objectives, timelines, and deliverables. Evaluate progress against goals using effective metrics and feedback loops.Manage resource allocation and risk assessment for the engineering team.

• Technical Excellence & Innovation

Stay abreast of the latest developments in Python programming and AI technologies. Champion best practices in code design, testing, deployment, and maintenance. Promote a culture of innovation and experimentation within the team.

• DevOps & Infrastructure Collaboration

Work closely with DevOps teams to ensure robust CI/CD pipelines. Optimize deployment processes and system performance. Ensure that the AI product is scalable, secure, and maintainable through effective operational practices.

Qualifications

• Expert in designing scalable, modular, and robust architectures to support AI models and services.
• Proficiency in Python and its major frameworks.
• Proficiency with Docker for containerizing applications, like containerization (Docker/Kubernetes), and cloud platforms. Familiarity with agile development methodologies.
• Strong experience leading engineering teams in a fast-paced environment.
• Demonstrated expertise in developing AI-based or data-driven applications.
• Knowledge of cloud services (AWS, GCP, or Azure) to deploy and manage applications at scale.
• Familiarity with generative AI algorithms and architectures, including transformers and GANs.
• Familiarity with monitoring tools (e.g., Prometheus, Grafana) and logging systems (e.g., Splunk) to ensure system health and performance.
• Understanding training, deploying, and fine-tuning machine learning models.
• Excellent leadership, communication, and interpersonal skills. Ability to convey technical concepts clearly to non-technical stakeholders. Strong problem-solving skills and a proactive approach to decision-making.