Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We’re optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity. With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes. 

Sia’s AI & Data Business Unit is the powerhouse of our firm’s innovation—merging cutting-edge Data Science, Generative AI, and advanced digital solutions to transform industries. With over 350 experts worldwide, we tackle projects from proof-of-concept to large-scale deployment, always pushing the boundaries of AI capabilities. Our 12 R&D labs in Europe and North America drive continuous research in areas like computer vision, MLOps, and deep learning, partnering closely with our business consultants for real-world impact. By joining Sia’s AI & Data team, you’ll step into a vibrant, collaborative environment that nurtures professional growth and empowers you to shape the future of AI-driven consulting. 

Sia 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.

• 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).
• Infrastructure: solid understanding of vector databases (Milvus, Pinecone, Weaviate) and distributed system design for large-scale data processing.
• Management Skills: strong experience in Agile/Scrum methodologies, capacity planning, and managing globally distributed teams.
• AI-Native Engineering Leadership: experience managing teams that utilize Cursor, GitHub Copilot, or Claude Code as a core part of their daily workflow.
• Education: Bachelor’s or Master’s degree in Data Science, Computer Science, AI, or a related quantitative field.
• Soft Skills: exceptional emotional intelligence, conflict resolution, and the ability to inspire a team during rapid technological shifts.

EM Product Traits — What Success Looks Like

• Platform Ownership: owns AI platform foundations (infra, tooling, pipelines) as long-lived products.
• Enablement First: measures success by how fast and safely product teams ship using the platform.
• Technical Depth: understands AI/ML platform trade-offs (compute, latency, cost, scalability, security).
• Production Readiness: ensures platforms support model productionization, monitoring, and lifecycle management.
• Reliability & Scale: drives uptime, resilience, and performance for multi-team AI workloads.
• Developer Experience: reduces friction in CI/CD, environments, experimentation, and deployments.

Cost & Efficiency Awareness: balances performance with cloud and compute cost controls.

Compensation & Benefits  

Sia invests in total rewards that support how our people work, live, and grow.  

Compensation  

• Base Salary: $150,000–$180,000. Actual compensation within this range will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by applicable law. 
• Annual Performance Bonus    

Benefits  

• Medical, dental, and vision; company-paid life and AD&D; voluntary supplemental insurance and EAP 
• 401(k) with company match and immediate vesting; HSA and FSA options  
• College savings and student loan repayment programs  
• Paid parental leave; generous PTO, nine company holidays, and one floating holiday  
• Cell phone stipend; well-being and professional development programs  

Benefits are subject to applicable plan terms and eligibility requirements.  

Workplace 
Sia values in-person collaboration. When serving clients, consultants may be required to work onsite at a client location based on engagement needs. Between client engagements, consultants are expected to work from a Sia office or approved coworking location at least three days per week. Candidates should reside within a reasonable commuting distance of a Sia office or approved coworking location. Sia maintains offices and coworking arrangements across multiple U.S. markets, including New York City, Charlotte, Greater Seattle, San Francisco Greater Bay Area, Los Angeles, Houston, Atlanta, Baltimore, Chicago, Washington D.C. 

Work Authorization 

Applicants must be legally authorized to work in the United States at the time of application and throughout their employment. This position is not eligible for employment visa sponsorship now or in the future. 

Our Commitment 

At Sia, we believe in fostering a diverse, equitable, and inclusive culture where our employees and partners are valued and thrive in a sense of belonging.  

Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.