Technical Support Engineer

About Simplismart

Simplismart is a GenAI inference platform to deploy, scale, and monitor any GenAI model (LLMs, speech, vision, or diffusion) across cloud or on-prem. Built for strict SLAs, enterprise-grade security, and full observability. Its modular design lets you optimize for cost or latency or auto-select the best topology per workload.

Role Overview

We are seeking a Technical Support Engineer with a strong focus on MLOps to provide enterprise-level assistance to our customers. In this role, you will diagnose and troubleshoot issues related to machine learning models, frameworks, and deployment pipelines. Your goal will be to ensure the seamless operation of ML systems and to assist our clients in leveraging these technologies effectively.

What is expected from you-

• Ownership of Issues: Take ownership of customer issues related to machine learning applications and see problems through to resolution.
• Research and Diagnosis: Research, diagnose, troubleshoot, and identify solutions for software and hardware issues specifically related to machine learning environments.
• Technical Support: Provide hands-on support for the ML frameworks and serving tools used across model deployment and inference.
• Network Configuration: Assist clients with network configuration issues that may affect ML model performance and data access.
• Remote Support: Familiarity with remote desktop applications and help desk software (e.g., Freshdesk) for efficient support delivery.
• Communication: Communicate effectively via email, chat, and phone to provide clear instructions and technical manuals to clients.
• Collaboration: Work closely with the Customer Success Manager and data science teams to ensure seamless support and integration of ML solutions.
• Escalation: Properly escalate unresolved issues to appropriate internal teams, such as data scientists or software developers, when necessary.
• Documentation: Document technical knowledge in the form of notes and manuals, particularly focusing on MLOps best practices and troubleshooting steps.
• Follow-Up: Ensure all issues are properly logged and follow up with clients to confirm their ML systems are fully functional post-troubleshooting.
• Technical Skills: Proficiency in Kubernetes troubleshooting, Linux, Python, and SQL, with working knowledge of cloud platforms (AWS, GCP, or Azure) and observability tooling such as Prometheus and Grafana. Familiarity with support and issue-tracking systems (Freshdesk, Jira) is expected. Experience in SAAS.
• Experience with Helm, Docker, NVIDIA GPUs/CUDA, and inference engines such as vLLM, TensorRT-LLM, Triton Inference Server, or PyTorch is a strong plus.

What We’re Looking For-

• Proven Experience: Demonstrated experience as a Technical Support Engineer, MLOps Engineer, or similar role with a focus on machine learning technologies.
• Hands-On Experience: Proficiency with ML frameworks (e.g., TensorFlow, PyTorch) and familiarity with cloud platforms (e.g., AWS, Azure, GCP) for ML deployment.
• Technical Troubleshooting: Ability to diagnose and troubleshoot technical issues related to ML model performance, data pipelines, and infrastructure.
• Remote Desktop Applications: Familiarity with remote desktop applications and help desk software (e.g., Freshdesk) for efficient support delivery.
• Problem-Solving Skills: Excellent problem-solving and communication skills, with the ability to explain complex technical concepts in simple terms.
• Educational Background: BS degree in Information Technology, Computer Science, Data Science, or a relevant field.
• Certifications: Additional certifications in machine learning, cloud computing, or relevant technologies (e.g., AWS Certified Machine Learning, Google Cloud Professional Data Engineer) are a plus.

Why Join Simplismart?

• Opportunity to define and lead the brand identity of a fast-growing GenAI company.
• Work closely with leadership on high-impact initiatives from global event campaigns to overall storytelling.
• Be part of a team that values design as a strategic lever, not just execution.
• Competitive compensation and growth opportunities in a high-energy startup environment.