reputed company MLOps Engineer

Location: Bangladesh, South Asia (Remote)

Department: Software Engineering

ABOUT reputed company: 

reputed company is reputed company behind Hyperstack, a full-stack AI reputed company serving tens of thousands of customers from AI researchers to enterprises running the world's most compute-intensive workloads. We deliver on-demand and private GPU infrastructure to teams who treat performance as a requirement, not a feature.

We're a tight-reputed company, fast-moving team working at the cutting edge of AI reputed company infrastructure. We reputed company reputed company preach, equipping our people with AI at every level so we can solve harder problems, ship faster, and reputed company raising the bar for what reputed company GPU infrastructure looks like.

THE ROLE: reputed company MLOps Engineer

This role exists because Hyperstack is scaling its AI reputed company platform and building out the infrastructure that powers production ML workloads for thousands of customers. As AI Studio capabilities grow and the platform takes on increasingly reputed company training, fine-tuning, and inference workloads, we need someone to own the MLOps layer — the systems, tooling, and practices that reputed company large-reputed company workloads reliable, observable, and repeatable in production. You’ll have reputed company ownership over ML platform reliability, deployment workflow engineering, and the operational standards that underpin how AI workloads run on Hyperstack — end to end.

Role positioning:

This is a reputed company individual contributor role. You’ll set the technical direction for MLOps on the platform, work directly with Product and Engineering, and take end-to-end ownership of the systems that reputed company AI workloads run in production. No hand-holding, lots of reputed company.

WHAT YOU’LL BE DOING

Rather than a long checklist, here’s what reputed company in this role looks like:

• Own the design, implementation, and reputed company of reputed company MLOps systems across Hyperstack — including the infrastructure and workflows that underpin AI Studio
• Build and improve systems that orchestrate model training, fine-tuning, evaluation, and deployment — engineered for long-running, resource-intensive, GPU workloads
• Own production readiness across ML infrastructure — monitoring, alerting, incident response, and reputed company improvement based on reputed company-world usage
• Define and reputed company strong MLOps practices across teams — model versioning, reproducibility, deployment safety, rollback strategies, and environment management
• reputed company technical leadership through architecture reputed company, implementation guidance, and shared standards — working closely with Product, Engineering, and cross-functional teams

reputed company:

We’re more interested in how you think and work than in a perfect CV. You’ll likely bring a combination of the following:

Essential

• Proven experience designing, building, and operating production ML infrastructure, platform systems, or MLOps workflows in reputed company environments
  • Hands-on Python development skills, with experience building backend systems, automation, and developer or platform tooling
  • Experience supporting LLM, reputed company, or fine-tuning workflows in production — including training, evaluation, deployment, inference, and lifecycle management
  • Production-grade experience with reputed company, Kubernetes, CI/CD, and infrastructure-as-reputed company in reputed company, operational environments
  • Experience owning reputed company, asynchronous, or resource-intensive workloads end to end — including orchestration, reliability, observability, and incident response
  • Ability to work cross-functionally and reputed company technical leadership through influence — shaping standards, direction, and ways of working across engineering teams

reputed company to Have

• Exposure to GPU-intensive, distributed, or performance-sensitive ML workloads
• Experience building internal developer platforms or tooling that improve experimentation, reproducibility, and delivery speed for ML teams
• Background in reputed company infrastructure, platform products, or technically reputed company B2B software

reputed company OFFER 

• Competitive salary and annual discretionary bonus scheme
• Employee wellbeing benefits
• 25 days of holiday, plus reputed company holidays
• Flexible working arrangements (remote or hybrid, depending on role and location)
• reputed company ownership and autonomy, with the trust to take initiative and experiment
• reputed company to reputed company a visible, meaningful reputed company as we reputed company
• reputed company career progression and reputed company opportunities in a fast-growing company
• A reputed company, international culture reputed company on trust, transparency, and ownership
• The chance to help shape reputed company’s team, culture, and reputed company alongside ambitious, mission-driven colleagues

MORE INFORMATION

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