Job Title: Senior ML Engineer – MLOps & Productionization

Level: Senior
Employment Type: Full-time
Experience: 7+ Years
Location: Islamabad

 

Role Overview

We are seeking a Senior ML Engineer to bridge the gap between experimental AI/ML prototypes and production-ready enterprise systems. As our AI/ML initiatives evolve from research to production, you will be responsible for operationalizing ML and LLM models, ensuring they run reliably at scale, and establishing robust engineering practices within our intelligence layer.

This role requires a hands-on engineer with deep Python expertise, strong MLOps experience, and the ability to deliver high-performance, containerized services that support large-scale, streaming data environments.

Key Responsibilities

Production Transition & Microservices Development

• Refactor Python-based ML models and LLM chains from experimental notebooks into production-ready, containerized microservices

• Ensure robust versioning, logging, and monitoring of models in production

• Collaborate with AI/ML researchers to translate R&D prototypes into scalable software

MLOps & Deployment Pipelines

• Build automated CI/CD pipelines for model deployment, monitoring, and retraining

• Implement model lifecycle management, including rollback strategies and reproducibility

• Integrate ML models into high-velocity streaming pipelines with low-latency requirements

Performance, Reliability & Standards

• Optimize ML inference logic for real-time and high-throughput environments

• Define and enforce coding standards, testing frameworks, and best practices for the AI/ML engineering team

• Ensure system stability, reliability, and observability across the intelligence layer

Technical Requirements

• Advanced Python Expertise: Beyond scripting – experience with FastAPI, design patterns, testing frameworks, and clean, maintainable code

• Containerization & Orchestration: Deep experience with Docker; familiarity with Kubernetes or equivalent orchestration for ML workloads

• MLOps & Deployment: Proven experience with CI/CD, model versioning, automated retraining, and tools such as MLflow, Kubeflow, or similar

• Experience operationalizing ML/LLM models in production, with strong focus on scalability and reliability

Nice to Have

• Experience with cloud-native AI/ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)

• Familiarity with streaming data platforms (Kafka, Pulsar, or similar)

• Exposure to monitoring, logging, and alerting frameworks for ML systems

• Knowledge of enterprise-grade security and compliance practices for AI

Soft Skills

• Strong problem-solving and analytical thinking

• Ability to translate research prototypes into robust engineering solutions

• Excellent collaboration skills, capable of working across AI, data engineering, and DevOps teams

• High ownership mentality with a focus on production stability and scalability

Why Join

• Play a critical role in moving cutting-edge AI/ML into production

• Shape the MLOps strategy and best practices for a growing intelligence platform

• Work in a fast-moving, enterprise-scale AI/ML environment with real business impact