Principal Software Engineer - MLOps Platform

e software systems for the AI/ML Platform and orchestrate the full ML development lifecycle for the partner teams

• Mentoring: Spreading your knowledge, sharing best practices and doing design reviews to step up the expertise at the team level

• Multi-cloud architecture: Define components which leverages strengths from multiple cloud platforms (e.g., AWS, Azure) to optimize performance, cost, and scalability

• AI/ML observability: You will build systems for monitoring performance of AI/ML models and find insights on the underlying data such as drift detection, data fairness/bias and anomalies

• ML Solution Deployment: You will develop tools for building and deploying ML artefacts in production environments and facilitating a smooth transition from development to deployment

• Big Data Management: Automate and orchestrate tasks related to managing big data transformation and processing and build large-scale data stores for ML artifacts

• Scalable Services: Design and implement low-latency, scalable prediction, and inference services to support the diverse needs of our users

• Cross-Functional Collaboration: Collaborate across diverse teams, including machine learning researchers, developers, product managers, software architects, and operations, fostering a collaborative and cohesive work environment

• End-to-end ownership: You will take the end-to-end ownership of the components and work with other engineers in the team including design, architecture, implementation, rollout and onboarding support to partner teams, production on-call support, testing/verification, investigations etc

Minimum Qualifications

• Educational Background: Bachelor's degree in Computer Science or equivalent practical experience

• Experience: Over 8 years of experience in software development and engineering, delivering production systems and services

• Prior experience of working with MLOps team at the intersection of the expertise across ML model deployments, DevOps and data engineering

• Hands-on skills: Ability to fluently translate the design into high quality code in golang, python, Java

• Knowledge of DevOps practices, containerization, orchestration tools such as CI/CD, Terraform, Docker, Kubernetes, Gitops

• Demonstrate knowledge of distributed data processing frameworks, orchestrators, and data lake architectures using technologies such as Spark, Airflow, iceberg/ parquet formats

• Prior collaborations with Data science teams to deploy their models, setting up ML observability for inference level monitoring

• Exposure for building RAG based applications by collaborating with other product teams, Data scientists/AI engineers

• Demonstrate creative problem-solving skills with the ability to break down problems into manageable components

• Knowledge of Amazon AWS and/or Azure cloud for solutioning large scale application deployments

• Excellent communication and collaboration skills, fostering teamwork and effective information exchange

Preferred Qualifications

• Experience in integrating with third party vendors

• Experience in latency optimization with the ability to diagnose, tune, and enhance the efficiency of serving systems

• Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard)

• Familiarity with distributed model training/inference pipelines using (KubeRay or equivalent)

• Exposure to leveraging GPU computing for AI/ML workloads, including experience with CUDA, OpenCL, or other GPU programming tools, to significantly enhance model training and inference performance

• Exposure to ML libraries such as PyTorch, TensorFlow, XGBoost, Pandas, and ScikitLearn

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