DevOps Engineer

About FriskaAi

FriskaAi is an intelligent health management platform revolutionizing preventive care, chronic disease management, and population health through AI-driven insights. We are part of HFWL Company, committed to transforming healthcare through innovation, compassion, and cutting-edge technology. As we continue to expand, we are building a team of forward-thinking engineers passionate about the future of autonomous, agent-driven AI.

operationalizing large language models (LLMs) at scale, ensuring their reliability, and integrating AI systems seamlessly into production healthcare environments

Role Overview

DevOps Engineer with LLMOps experience

Key Responsibilities

• Design and manage

  cloud infrastructure (Azure, AWS, or GCP)

  for scalable LLM and AI agent deployment.
• Build and maintain

  CI/CD pipelines

  for model training, fine-tuning, and versioned deployment.
• Implement

  LLMOps best practices

  , including model monitoring, performance tracking, drift detection, and rollback mechanisms.
• Collaborate with AI/ML engineers to optimize training and inference workflows (e.g., GPUs/TPUs, distributed training).
• Ensure

  compliance with healthcare regulations (HIPAA, HL7, FHIR)

  and data security protocols.
• Automate infrastructure provisioning using tools like

  Terraform, Ansible, or Pulumi

  .
• Develop monitoring and alerting systems for

  model health, latency, and cost optimization

  .
• Contribute to the

  observability and reproducibility

  of AI models in production environments.

Requirements

• Bachelor’s or Master’s degree in Computer Science, DevOps, Cloud Engineering, or related field.
• 3+ years of experience in

  DevOps/SRE roles

  , with at least 1 year working in

  LLMOps or MLOps environments

  .
• Strong expertise in

  Kubernetes, Docker, and container orchestration

  .
• Experience with

  CI/CD tools

  (e.g., GitHub Actions, Azure DevOps, Jenkins, ArgoCD).
• Familiarity with

  LLM deployment frameworks

  (e.g., vLLM, Hugging Face Inference, Ray Serve, Triton).
• Proficiency in

  Python, Bash, or Go

  for automation and scripting.
• Knowledge of

  observability stacks (Prometheus, Grafana, ELK)

  .
• Strong problem-solving skills with a focus on scalability, reliability, and cost efficiency.

Nice to Have

• Experience with

  RLHF pipelines or model fine-tuning at scale

  .
• Familiarity with

  multi-cloud or hybrid cloud setups

  .
• Understanding of

  secure AI/ML data pipelines

  and privacy-enhancing technologies.
• Contributions to

  open-source LLMOps or MLOps projects

  .

What You’ll Gain

• Opportunity to

  shape the future of LLM infrastructure in healthcare AI

  .
• Hands-on experience in

  agentic AI deployment and optimization at scale

  .
• Work closely with

  AI researchers, engineers, and product teams

  .
• Be part of a mission-driven company transforming healthcare with safe and scalable AI.