Engineering Manager – AI Platforms

Key Responsibilities

• Team Leadership & Mentoring: Manage, coach, and grow a team of Software Engineers, ML & GenAI Engineers. Conduct performance reviews, define career paths, and foster an inclusive environment that encourages innovation in algorithmic design and deployment.
• Platform Strategy: Own the roadmap for Sia’s AI Platforms, evolving them from experimental models to enterprise-grade foundations that support RAG, agentic workflows, and automated model fine-tuning at scale.
• Data Science Excellence: Oversee the development of scalable machine learning workflows, ensuring best practices in statistical rigor, model validation, and the transition from research notebooks to production-ready code.
• MLOps & GenAIOps Governance: Drive the adoption of robust MLOps pipelines (CI/CD, model monitoring, drift detection) to ensure the reliability and observability of deployed Data Science models.
• Cross-Functional Collaboration: Partner with Lead Data Scientists, Product Managers, and Cloud Architects to align technical execution with business objectives and client needs.
• Technical Oversight: Provide architectural guidance and conduct reviews for complex AI integrations involving vector databases, orchestrators (LangChain, LlamaIndex), and multi-cloud environments.
• Security & Compliance: Ensure all AI platforms adhere to global security standards (GDPR, SOC2) and implement Responsible AI guardrails to mitigate bias and ensure data privacy in model training and inference.
• Stakeholder Communication: Act as a technical advocate, translating complex Data Science and platform capabilities into clear, value-driven insights for executive leadership.
• Experience: 8+ years of experience in the data/software space, with at least 3+ years in a formal people management or technical leadership role leading Data Science or ML teams.
• Data Science Mastery: Deep understanding of the Data Science lifecycle, including exploratory data analysis, feature engineering, and advanced modeling techniques (Deep Learning, NLP, etc.).
• AI/GenAI Depth: Hands-on experience building and scaling applications powered by LLMs (OpenAI, Claude, Llama) and implementing complex RAG architectures.
• Platform Expertise: Proven track record with Kubernetes, Docker, and cloud-native AI services (AWS Bedrock/SageMaker, Azure AI, or Google Vertex AI).