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Sia Partners Mumbai is seeking an Engineering Manager - Product to drive next-gen AI/GenAI platforms within our AI Factory. You will bridge product vision with cloud-native engineering, collaborating with product, data science, and platform teams to deliver scalable solutions.
The role demands strong Python expertise, distributed systems experience, AWS, and leadership to guide teams across time zones. Onsite in Mumbai with global impact.
Sia Partners is a next-generation global management consulting firm, founded in 1999 and headquartered in Paris, France. The firm is recognised for its innovative approach, combining strategy and management consulting with data science and creativity. Sia Partners serves a diverse range of sectors, including energy, banking, healthcare, and technology, providing services to over 1,000 clients worldwide, including many Fortune 500 companies. With a strong emphasis on delivering tangible results and superior value, Sia Partners is committed to helping clients navigate the digital revolution and achieve transformation. The firm operates with a global presence, employing over 3,500 consultants across 48 locations in 20 countries
Our Mumbai office was launched in 2024, marking an exciting new chapter for us. We’re building the team with people who are eager to shape something from the ground up — combining the agility and entrepreneurial energy of a startup with the backing and reach of a global brand.
Sia Partners is looking for an Engineering Manager - Productto 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.
You will work closely with product, data science, and platform teams to translate business and client requirements into robust technical solutions. The role requires strong Python expertise, experience with distributed systems and microservices, and a pragmatic approach to delivering reliable, scalable systems in enterprise environments. In addition to technical delivery, you will contribute to architectural decisions, raise engineering standards, and support client-facing engagements through technical leadership and clear communication.
Education: Master’s or PhD in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, or Physics.
Over 8 years in software development, with at least 2 years in a leadership role managing software or cloud engineering teams. Proventrack recordin the technology product sector with a deep understanding of SaaS lifecycles.
Core Skills:Expertise inalgorithms, data structures, and distributed systems. Deep understanding of cloud-native design patterns andmicroservicesarchitecture.
Languages & Frameworks: Strongproficiencyin Python (core)orWorking familiarity with Node.js and React.js is highly valued.
AI-Native Engineering Leadership: Experience managing teams thatutilizeCursor, GitHub Copilot, or Claude Code as a core part of their daily workflow.
Cloud Expertise: Extensive experience building and launching modern SaaS products on public cloud platforms, specifically AWS. Proficient in containerization and orchestration (Docker, Kubernetes) and Infrastructure-as-Code (Terraform/CloudFormation).
Leadership & Communication: A decisive leader who prioritizes action and outcomes. Outstanding interpersonal skills with the ability to collaborate across global functions. Strong command of English (written and verbal) for effective communication at all organizational levels.
Global Collaboration:Demonstratedability to lead in a global environment, attracting andretainingtop engineering talent while working across different time zones and cultures.
AI Product Ownership: Owns delivery of AI/GenAI products from problem framing to production impact.
Outcome-Driven Delivery: Focuses on business value, not just model or feature completion.
Product–Engineering Alignment: Partners tightly with Product / Business to convert AI use cases into executable plans.
Technical Judgment: Understands AI system trade-offs (latency, cost, accuracy, reliability) and challenges decisions when needed.
Delivery Predictability: Breaks complex AI initiatives into incremental, shippable milestones.
Quality & Reliability: Ensures production-grade AI systems (monitoring, failure handling, iteration readiness).
Client & Stakeholder Communication: Clearly explains AI limitations, risks, and trade-offs to non-technical stakeholders.
Team Leadership: Builds and grows high-ownership product engineers and applied AI teams.
Managing Ambiguity:Operateseffectively with evolving requirements and experimental problem spaces.
Calm Ownership: Leads with accountability under delivery pressure and changing priorities.
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.