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Technical Support Engineer (Inference) - India Weekends

Together AI· Customer Success
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Hybrid (Pune, Bangalore)Full-timePosted 7mo ago

About the role

Technical Support Engineer focused on AI inference, training, and fine-tuning services. Acts as first line of defense for customers, resolving complex technical challenges, maintaining healthy Kubernetes-based inference endpoints, and collaborating across Engineering, Research, Product, and Go-To-Market teams to drive customer success and product improvements.

Responsibilities

  • Engage directly with customers to tackle and resolve complex technical challenges involving GPU clusters and inference/fine-tuning services; ensure swift and effective solutions.
  • Act as a customer facing SRE to ensure inference endpoints (running on Kubernetes) remain healthy, stable, and performant.
  • Become a product expert in Gen AI solutions, serving as the last line of technical defense before issues are escalated to Engineering and Product teams.
  • Assist with hardware and platform migrations by validating system health and traffic routing; monitor dashboards to detect anomalies and escalated with data-backed analysis.
  • Manage customer-facing communications during incidents and degradations; translate technical findings into clear, evidence-backed updates.
  • Contribute infrastructure changes for model deployment, capacity rebalancing, and cluster configuration via infra-as-code (pull requests).
  • Flag engine-level bugs with logs and reproduction steps for engineering.
  • Collaborate with Engineering, Research, Product, and Go-To-Market teams to address customer concerns and ensure satisfaction.
  • Transform customer insights into action by identifying patterns in support cases and informing roadmap with Engineering and Go-To-Market teams.
  • Maintain detailed documentation of configurations, procedures, troubleshooting guides, and FAQs.
  • Be flexible in providing support coverage during holidays, nights and weekends as required.

Qualifications

  • 6+ years in a customer-facing technical role, SRE, DevOps, or infrastructure engineering, with at least 1 year in a support role for an AI service.
  • Strong technical background with knowledge of AI/ML, GPU technologies, and HPC environments.
  • Advanced production-level experience with Kubernetes, SLURM, infrastructure as code (e.g., Ansible), high-performance networks, NFS-based storage, and container infrastructure.
  • Familiarity with HPC storage systems such as Vast and Weka.
  • Ability to diagnose complex network-layer issues and read traces.
  • Strong knowledge of Python, TypeScript, and/or JavaScript with testing/debugging using curl and Postman-like tools.
  • Experience with observability tooling (Prometheus, Grafana) at scale.
  • Deep familiarity with REST API debugging and HTTP semantics.
  • Experience with LLM inference frameworks and LoRA fine-tuning and common training failure modes.
  • Experience with Infrastructure as Code and Git-based workflows.
  • Background in GPU cluster management.
  • Cloud platform experience (AWS, GCP, and/or Azure).
  • Foundational understanding of installation, configuration, administration, troubleshooting, and securing of compute clusters.
  • Strong problem-solving, proactive troubleshooting, and ability to work cross-functionally.
  • Excellent communication and interpersonal skills; able to explain complex concepts to non-technical stakeholders.
  • Ability to operate in dynamic environments with multiple projects and frequent context switching.

Skills

Required:Customer-facing technical expertiseKubernetesInfrastructure as CodePython, TypeScript, JavaScriptObservability toolsREST API debuggingGPU cluster managementCloud platforms
Nice to have:SLURM

Benefits

  • startup equity
  • health insurance
  • remote work flexibility (region-dependent)
Last updated Sep 9, 2026. Interested? Apply on the company site.

About Together AI

The AI Acceleration Cloud - Fast Inference, Fine-Tuning & Training

Run and fine-tune generative AI models with simple APIs and scalable GPU clusters. Train & deploy at scale on The AI Acceleration Cloud.

AI native cloudFine-TuningGPU clustersInferenceTrainingai-infrastructure-and-developmentbrex-fastest-growing-2025liquid-ai-recruiting-target
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