Senior Machine Learning Engineer

2 weeks ago

Lahore, Punjab, Pakistan MedCare MSO Global Full time $90,000 - $120,000 per year

Medcare MSO is one of the largest USA-based Healthcare IT companies operating in Pakistan, with 800+ professionals on board. We are committed to delivering cutting-edge software solutions for the healthcare and medical billing industry.

As the Senior ML Ops Engineer, you will be responsible for designing and operating Medcare's infrastructure for deploying, monitoring, and improving real-world AI solutions. Working closely with the ML, Data Engineering, and Product teams, you will own the lifecycle of deployed models—ensuring they are scalable, secure, explainable, and auditable. Your work will play a critical role in taking high-impact AI models from prototype to enterprise-grade performance in operational workflows. This role demands deep technical acumen and the ability to deliver robust solutions in regulated environments.

Position: Senior ML Ops Engineer

Shift timings: 2:00 pm - 11:00 pm

Location: Lahore (on-site)

Key Responsibilities

  • Build, configure, and manage the lifecycle of deployed AI models including training, inference, and retraining workflows
  • Design and maintain a containerized, modular infrastructure that enables experimentation, deployment, rollback, and scaling
  • Implement audit-friendly logging, performance monitoring, and human-in-the-loop feedback mechanisms
  • Support model explainability, traceability, and QA tagging for post-deployment governance
  • Collaborate with ML Engineers and Data Engineers to enable real-time and batch predictions from structured and unstructured inputs
  • Ensure alignment with HIPAA compliance, PHI-safe practices, and secure credential management
  • Establish reusable pipelines for multiple use cases including document classification, summarization, and structured code prediction
  • Translate prototype ML scripts into reliable, scalable production environments

Qualifications

  • Bachelor's or Master's in Computer Science, Machine Learning, or related discipline
  • 4–6 years of experience in ML Ops, AI infrastructure, or ML Engineering roles
  • Strong foundation in containerization, deployment, and orchestration (e.g., Docker, FastAPI, Kubernetes, Azure DevOps)
  • Hands-on experience with MLOps stacks like MLFlow, Weights & Biases, or similar
  • Familiarity with LLMs, prompt engineering, vector databases, and feedback tuning pipelines is a strong advantage
  • Prior work deploying or managing models in healthcare or regulated enterprise environments is preferred
  • Strong cross-functional communication and documentation skills

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