Sia Partners is looking for an Engineering Manager – AI Platforms to support the design and delivery of next-generation AI and Generative AI platforms within Sia’s AI Factory. This role is pivotal in bridging high-level product vision with robust, cloud-native engineering execution.
As an Engineering Manager, you will serve as the bridge between Data Science research and production-grade software engineering. You will be responsible for the health, growth, and delivery of a cross-functional team, ensuring that our data-driven AI services are scalable, secure, and seamlessly integrated into our global consulting framework. You will not only guide the technical architecture of our Large Language Model (LLM) platforms but also foster a culture of scientific excellence, rapid experimentation, and professional development.
Your leadership will ensure that Sia remains at the forefront of the AI landscape, transforming complex statistical models and algorithms into robust, platform-centric solutions that deliver measurable value to our clients worldwide.
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.