Senior Manager - Software Development (Machine Learning)

Job description

Location Details: India, Remote At GoDaddy the future of work looks different for each team. Some teams work in the office full-time; others have a hybrid arrangement (they work remotely some days and in the office some days) and some work entirely remotely.​ This is a remote position, so you’ll be working remotely from your home. You may occasionally visit a GoDaddy office to meet with your team for events or meetings. Join Our Team... Here at GoDaddy, the ML Engineering (MLE) team exists as the backbone of our machine learning infrastructure, enabling ML scientists and product teams across Domains to ship models to production reliably, efficiently, and at scale. This team owns the full lifecycle of ML systems — from CI/CD pipelines and model serving infrastructure to GPU workload orchestration and observability. Through disciplined engineering practices, thoughtful system design, and close collaboration with ML scientists, data engineers, and product teams, we deliver the platform that powers domain search, pricing, recommendations, and emerging AI experiences for millions of customers worldwide. We are currently looking for an experienced, highly motivated Senior Engineering Manager to lead our ML Engineering team based in India. This is an established team with existing engineers — we expect the candidate to ramp up quickly on our ML infrastructure stack, build strong relationships with the team, and partner with both India-based teams and US-based teams to drive execution and grow the team further. This individual will join us on our journey to build and scale ML infrastructure that serves real-time predictions at low latency, automates model deployment and promotion, and provides the observability and reliability guarantees that production ML systems demand. Become part of a team that bridges the gap between ML research and production engineering — shipping systems that directly impact GoDaddy's core revenue. What you'll get to do... • Lead a team of ML infrastructure engineers in India; mentor, coach, and grow the team as we scale • Partner with US-based MLE leadership to align on technical direction, roadmap, and priorities • Architect and evolve our ML serving infrastructure — model endpoints, inference pipelines, autoscaling, and GPU workload scheduling on AWS (ECS/EKS, SageMaker) - Own the CI/CD platform for ML — automated pipelines for model training, testing, deployment, promotion, and rollback across multiple environments • Partner with ML scientists to operationalise models — translating research outputs into production-grade, monitored, and maintainable services - Evaluate architecture and identify engineering gaps; drive technical direction for the team's systems and services • Collaborate daily with ML scientists, data engineers, backend teams, and product managers across time zones to align on roadmap, priorities, and delivery - Build and maintain platform capabilities: model registry, feature serving, experiment infrastructure, and policy-as-code systems • Develop cloud-native solutions to harden our ML platform and infrastructure at scale • Drive quarterly and yearly roadmap planning; identify and communicate progress, risks, and tradeoffs to leadership - Continuously improve productivity and sustainability through great engineering practices — code quality, testing, release processes, and documentation • Handle on-call rotation, production issue investigation, and post-mortems for ML serving infrastructure • Actively participate in all phases of the software development lifecycle, from requirements gathering and technical design through development, testing, rollout, and support Your experience should include... • 5+ years of engineering management or leadership experience, including hiring, building teams, and performance management • 10+ years of software development experience with Python, Go, and/or TypeScript - 3+ years working with AWS (ECS/EKS, SageMaker, DynamoDB, S3, or equivalents) • Proven track record in architecting and developing distributed systems using cloud technologies - Hands-on technical experience leading engineering teams, including working with distributed teams and setting technical direction • Strong system design and microservices skills — API contracts, failure modes, system decomposition - Deep CI/CD experience — building automated pipelines for deployment, testing, and promotion • Experience with containerisation and orchestration (Docker, Kubernetes, or ECS) - Excellent English communication skills — daily coordination across time zones with multiple partner teams • Ownership mindset — can drive roadmap, delivery, and people decisions independently You might also have... • Experience with ML model serving frameworks (vLLM, TorchServe, Triton, SageMaker endpoints) • Familiarity with observability stacks (OpenTelemetry, Prometheus, Grafana, CloudWatch) - Experience with infrastructure as code (CDK, CloudFormation) • Background in GPU workload scheduling and cost optimisation - Comfort using AI-assisted development tools to accelerate delivery • Experience with Agile/Scrum methodologies and tools (e.g., JIRA, Confluence) • Bachelor's degree in Computer Science, Mathematics, or other technical p