We are seeking a highly experienced Engineering Manager (EM) to lead a 30+ member engineering team building a central Agentic AI platform that will power 500+ enterprise-scale AI agents. This role requires a leader with strong product engineering acumen, proven delivery in large-scale applications, and the ability to align cross-functional engineering teams (ML, Platform Engineering, and Software Development)
The EM will be responsible for driving shared services like LLM gateways, inference optimization layers, MCP servers, agent templates, and FinOps capabilities, ensuring standardization, scalability, and performance across the enterprise.
You will partner with product and architecture leaders, critically review PI and sprint plans, and ensure technical debt is intentionally tracked and factored into the product roadmap.
Responsibilities
Team Leadership & Strategy
- Lead, mentor, and grow a team of 30+ engineers across ML, Platform Engineering, and Software Development domains.
- Define and drive engineering strategy for the Agentic AI platform, ensuring scalability to support 500+ agents across enterprise use cases.
- Establish best practices for architecture, coding standards, DevOps, observability, and reliability.
Engineering & Shared Services
- Oversee the design and development of shared services such as: LLM gateways & orchestration layers, Inference optimization services (GPU/CPU utilization, quantization, batching, scaling), MCP (Multi-Channel/Control Plane) servers, Agent templates and scaffolding tools, FinOps for AI workloads (cost optimization, GPU scheduling, monitoring)
- Ensure reliability, scalability, and interoperability across all services.
Execution & Delivery
- Review Program Increment (PI) and sprint plans to ensure alignment with business priorities. Ensure SDLC best practices and detailed documentation in stories
- Partner with Product Managers and Architects to balance new feature delivery with technical debt remediation.
- Ensure timely delivery of production-ready systems with high availability, security, and performance.
Product Development Lifecycle
- Own the end-to-end product development lifecyclefrom concept architecture implementation deployment monitoring.
- Ensure engineering excellence in CI/CD pipelines, test automation, and release processes.
- Drive adoption of FinOps and AIOps practices to optimize cost, performance, and operational efficiency.
Customer Success:
- Managing customer communication & relationships with customer SAs and engineering managers
- Ensure customer success by establishing customer's trust and fostering collaborative partnership, responsible for CSAT/NPS.
What is required
- 12+ years of progressive experience in software engineering, with at least 5+ years in engineering leadership roles.
- Proven track record in building and scaling enterprise-grade platforms (preferably AI/ML/Agentic systems).
- Strong understanding of: LLM technologies and inference optimization (LoRA, quantization, GPU scheduling), Platform Engineering (Kubernetes, Terraform, observability, scaling services) and Software Development (backend APIs, distributed systems, microservices).
- Demonstrated expertise in taking complex applications to production at scale.
- Experience in FinOps for AI/ML platforms (GPU cost management, optimization).
- Strong ability to critically review architecture, sprint plans, and technical trade-offs.
- Excellent stakeholder management skillsworking with Product, Architecture, and Business leaders.
- Experience in managing large engineering teams (30+) and leading cross-functional teams, and in-depth knowledge in framing, designing and execution of solutions for challenging business problems
- Must have excellent analytical, quantitative and conceptual thinking skills
- Excellent interpersonal, communication and presentation skills are required
What's in it for you
- Be part of a strategic leadership role shaping how enterprises build and scale Gen AI solutions. Lead the delivery of a large enterprise Agentic AI platforms, scaling to 500+ agents.
- Directly influence customer success, growth, and enterprise AI adoption.
- Own the end-to-end delivery lifecycle across innovation, engineering, and operations.
- The opportunity to work with a diverse, lively and proactive group of techies who are constantly raising the bar on how GPUs can effectively be utilised to build large scale Agentic platform that can host 500+ usecases (agents).
- Flexible working options available to foster productivity and work/life balance.
