Senior Machine Learning Engineer

Job Description Join us at Zendesk, where we're on a mission to power exceptional service for every person on the planet. We're accelerating that ambition by building products rooted in AI, automation, and intelligent customer experiences, because behind every interaction lies an opportunity to make a human connection. We’re seeking a Senior Machine Learning Engineer to lead the next wave of GenAI infrastructure at Zendesk. You’ll build and operate benchmarking, evaluation, and inference infrastructure that ensures our AI-driven experiences are reliable, safe, cost‑effective, and aligned with customer support workflows. Your work will enable product and ML teams to move fast while keeping quality, observability, and trust at the center. Summary As a Senior ML Engineer on the AI/ML Platform team, you’ll design and deliver production‑grade systems for LLM benchmarking (A/B and offline), LLM Proxy access, observability, cost control, and orchestration for multi‑step/agentic workflows. You’ll partner with ML researchers, applied teams, and product managers to make GenAI features safe, measurable, and scalable across Zendesk products. What you’ll be doing Build and maintain benchmarking frameworks for LLMs (A/B tests, offline/e2e evaluation pipelines) to measure quality, latency, and cost trade‑offs. Design, develop, and operate LLM Proxy functionality: routing, safety filters, caching, rate limiting, and cost attribution across vendors. Implement monitoring, observability, and alerting for LLM services (latency, error rates, hallucination signals, cost per call). Lead the design and automation of evaluation suites and gold‑standard datasets for ticket replies, summaries, intent detection, and recommendations. Collaborate with applied ML, product, and platform teams to translate evaluation findings into model improvements, release criteria, and mitigation strategies. Build orchestration tooling for multi‑step agentic workflows and integrations that safely interact with external tools and Zendesk products. Establish engineering best practices around security, reliability, testing, CI/CD, and performance tuning for ML services. Mentor engineers, help define roadmap priorities, and drive cross‑team initiatives that increase platform adoption and developer productivity. What you bring to the role 5 years building and running ML systems or backend platforms in production, with strong ownership from design to deployment. Hands-on experience with LLM systems, inference-serving patterns, or GenAI application infrastructure (prompting, caching, vendor routing). Strong backend engineering skills (Python or another server-side language), familiarity with distributed systems, and production CI/CD practices. Experience with Kubernetes, Docker, cloud platforms (AWS/GCP/Azure), and monitoring/observability tooling. Demonstrated ability to design evaluation pipelines, build gold‑standard datasets, or run rigorous A/B and offline evaluations. Clear communicator who can translate technical tradeoffs into product decisions and measurable outcomes. Curious, collaborative, and committed to building reliable, safe, and cost‑effective GenAI infrastructure. Basic Qualifications BS in Computer Science, Engineering, or related field, or equivalent practical experience. 5 years industry experience delivering production ML systems or backend services. Proficiency in Python and experience with containerized deployments and cloud infrastructure. Proven experience designing and running model evaluation, monitoring, or A/B testing for ML services. Preferred Qualifications Experience with LLM vendors (OpenAI, Anthropic, Google, Llama) and vector databases. Background in building agentic orchestration or tool-using AI systems. Prior experience implementing cost attribution, vendor routing, or request-level caching for LLMs. Advanced degree (MS/PhD) or demonstrated research contributions in ML/NLP. Location Pune, India (On site) Ple ... (truncated, view full listing at source)