RISETech (Research and Innovation in Sciences, Engineering and Technology) is currently located in Rawalpindi/Islamabad, Pakistan. Our focus is on products and solutions in the domain of computer and electrical engineering. We are a young, enthusiastic group of people working in cross domains. The company has built several products and solutions for national/international clients in the health sector and other general domains as well.

We are seeking a highly skilled ML Engineer with expertise in building, deploying, and managing machine learning models at scale using cloud infrastructure. This role focuses on optimizing the serving of ML models to ensure reliability, scalability, and low latency, enabling seamless integration with large-scale applications.

Key Responsibilities:

1. Model Deployment and Serving:
   
   • Design, implement, and maintain scalable ML model serving frameworks using cloud platforms (e.g., AWS, GCP, Azure).
   • Optimize model inference pipelines to achieve low latency and high throughput.
   • Build and manage RESTful or gRPC APIs to expose ML models for real-time or batch predictions.
2. Cloud Infrastructure Management:
   • Architect and manage cloud-based solutions for model deployment, leveraging services like Kubernetes, Docker, Lambda functions, or serverless frameworks.
   • Utilize Infrastructure as Code (IaC) tools (e.g., Terraform, CloudFormation) for deployment automation.
   • Monitor cloud resources and implement cost-efficient solutions for ML workloads.
3. Inference Scalability:
   • Handling inference methods of heterogeneous set of models including LLMs, VLMs and other large scale foundation models over a standardized API.
   • Develop agentic workflows on top of the deployed models for different use cases such as RAGs with LangChain.
   • Design solutions to scale model serving infrastructure to handle large volumes of concurrent requests.
   • Implement redundancy, failover strategies, and disaster recovery mechanisms to ensure high availability.
   • Use auto-scaling mechanisms and load balancers for dynamic workload management.
   • Profile and optimize ML models for inference speed using techniques like quantization, pruning, or TensorRT.
   • Optimize data pipelines for efficient batch and real-time data processing.
   • Conduct A/B testing and monitoring to evaluate model performance post-deployment.
4. Collaboration and Integration:
   • Collaborate with front-end and back-end engineers to productionize and integrate models into end-user applications.
   • Streamline CI/CD pipelines for ML models.
   • Support software engineering teams in embedding models into existing software ecosystems.
5. Monitoring and Maintenance:
   • Set up observability frameworks for serving pipelines (e.g., logging, metrics, alerting with tools like Prometheus, Grafana).
   • Continuously monitor model serving performance and implement updates or fixes as required.
   • Manage versioning of models and infrastructure components.

Required Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field.
• Experience: 3+ years of hands-on experience in ML model deployment and serving at scale.
• Strong understanding of machine learning concepts, frameworks (e.g., PyTorch, Tensorflow), and tools for model optimization.
• Proficiency in cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
• Experience with microservices architecture and API development.
• Strong programming skills in Python.

Interested candidates are encouraged to apply on form below with your latest resume and include any other details in application.