Lead AI Engineer

We will count on you to:

• Lead and Manage AI Engineering Teams: Oversee one or more teams or projects, providing technical guidance, mentorship, and fostering a culture of innovation and excellence in AI development.
• AI Solution Design & Development: Work closely with Architects to design, develop, and deploy scalable AI models and solutions, including machine learning, natural language processing, and prompt engineering, ensuring alignment with business objectives.
• AI Integration & Optimization: Collaborate with data scientists, data engineers, and product teams to integrate AI models into existing products and new initiatives, optimizing for performance, usability, and ethical considerations.
• Research & Innovation: Stay abreast of the latest advancements in AI, machine learning, and related fields. Drive experimentation with emerging AI frameworks, tools, and methodologies to maintain a competitive edge.
• Code Quality & Best Practices: Write and review high-quality, maintainable code, leveraging AI development tools such as GitHub Copilot, and promote practices like TDD, CI/CD, and modular design.
• AI Model Lifecycle Management: Oversee the end-to-end lifecycle of AI models from data collection and preprocessing to training, validation, deployment, and monitoring ensuring robustness and compliance.
• Cross-Functional Collaboration: Work closely with product managers, solutions architects, and other stakeholders to define AI requirements, develop prototypes, and deliver production-ready solutions.
• AI Governance & Ethics: Advocate for responsible AI practices, including fairness, transparency, and security, ensuring models adhere to organizational and regulatory standards.
• Thought Leadership & Community Engagement: Contribute to internal and external AI communities, sharing insights, best practices, and strategic direction for AI initiatives.
• Foster a Culture of Learning: Mentor engineers in AI, machine learning, prompt engineering, and related disciplines, promoting continuous skill development and innovation.

What you need to have:

• Proven experience leading AI-focused engineering teams and delivering AI-driven products or solutions.
• Strong understanding of AI/ML frameworks such as TensorFlow, PyTorch, LangFlow, or similar.
• Extensive hands-on experience with machine learning model development, deployment, and monitoring.
• Proficiency in programming languages such as Python, JavaScript, TypeScript, C#, or others relevant to AI development.
• Experience with cloud platforms (AWS, Azure, GCP) for deploying AI solutions at scale.
• Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines for AI workflows.
• Knowledge of data engineering, data pipelines, and big data technologies relevant to AI.
• Strong understanding of AI ethics, fairness, and security considerations.
• Excellent communication skills, capable of translating complex AI concepts to technical and non-technical audiences.
• Demonstrated ability to build, mentor, and lead high-performing AI teams.
• Implement rigorous validation protocols to ensure model accuracy, robustness, and fairness.
• Develop and maintain testing frameworks for AI models, including unit tests, integration tests, and performance benchmarks.
• Establish KPIs and metrics to evaluate model performance in production environments.
• Maintain version control for datasets, models, and code to ensure reproducibility.
• Incorporate security best practices to prevent model exploitation or adversarial attacks.
• Conduct regular risk assessments related to AI system deployment.
• Integrate automated testing into CI/CD pipelines to catch issues early.

Technical Skills & Qualifications:

• Deep expertise in AI/ML frameworks (TensorFlow, PyTorch, LangFlow, etc.).
• Experience with natural language processing (NLP), prompt engineering, and generative AI models.
• Familiarity with middleware and messaging systems such as Kafka, RabbitMQ, or similar.
• Experience with cloud-native AI deployment, including serverless architectures and container orchestration.
• Strong background in software development best practices, including SDLC, TDD, CI/CD, and security.
• Working knowledge of databases (SQL, NoSQL) and data modeling for AI applications.
• Experience with modern front-end/back-end frameworks (e.g., React, , .NET) is a plus.
• Knowledge of ethical AI practices, model interpretability, and compliance standards.