Senior Machine Learning Engineer

Key Skills

Aartificial intelligence, AWS, Azure, Continuous Integration & Continuous Delivery - CI/CD, Data pipelines, Data science techniques, Design, DevOps, Docker, Google Cloud Platform (GCP), Java Programming, Kubeflow, Kubernetes-K8s, Machine learning techniques, MLflow, MLOps tools, Python Programming, PyTorch, Scala Programming, TensorFlow

Job Description

As a Data Scientist at IBM, you will help transform our clients’ data into tangible business value by analyzing information, communicating outcomes and collaborating on product development. Work with Best in Class open source and visual tools, along with the most flexible and scalable deployment options. Whether it’s investigating patient trends or weather patterns, you will work to solve real world problems for the industries transforming how we live.

Your Role and Responsibilities

As a Senior Machine Learning Engineer, you will play a crucial role in our team, responsible for designing, implementing, and maintaining the infrastructure and workflows that enable the seamless integration of AI models into our software applications. You will be responsible for developing the systems to deploy, manage and optimize our ML and AI models in production environments, ensuring smooth integration and efficient operation. This is a key role in bridging the gap between data science, software development, and DevOps, making the AI that we develop accessible to the software that utilizes it. Your primary focus will be on building scalable and efficient systems for model development, training, deployment, and monitoring. You will leverage your expertise in machine learning, cloud computing, and software engineering to ensure smooth and reliable operations of our machine learning models in production.

Responsibilities

• Design and implement end-to-end AI pipelines, encompassing data ingestion, preprocessing, model training, deployment, and monitoring.
• Collaborate with data scientists and software engineers to streamline the development and deployment of machine learning models.
• Develop infrastructure and automation tools for scalable and reproducible model training and evaluation.
• Architect and maintain the cloud-based infrastructure required for running machine learning workloads at scale.
• Implement efficient deployment strategies for machine learning models, considering factors like model versioning, A/B testing, and canary deployments.
• Monitor and optimize model performance, ensuring scalability, reliability, and low-latency response times.
• Identify and mitigate potential bottlenecks and performance issues in the ML and MLOps pipelines.
• Stay updated with the latest advancements in machine learning frameworks, MLOps, cloud technologies, and industry best practices.
• Mentor and guide junior members of the team, providing technical leadership and fostering a culture of innovation and continuous learning.

Required Technical and Professional Expertise

• 3+ years of experience in machine learning engineering or similar role
• 5+ years programming languages such as Python, Java, or Scala, with experience in software development best practices and version control systems
• Strong proficiency in machine learning frameworks like PyTorch, Tensorflow and scikit-learn
• Experience in hybrid and cloud platforms including experience with serverless computing, containerization, and orchestration tools
• Solid understanding of DevOps/MLOps practices and tools, including CI/CD pipelines, automated testing, and infrastructure-as-code (IaC)
• Familiarity with big data technologies and distributed computing frameworks like Apache Spark.
• Experience with monitoring and logging tools, ensuring the observability of machine learning models and infrastructure.
• Experience with ML pipeline and MLOps tools such as ModelDB, Kubeflow, MLFlow, Pachyderm, or Data Version Control (DVC).
• Experience in supporting full model lifecycle processes (model builds and deployment, experiment tracking, model management, model training, hyperparameter optimization, model evaluation, and explainability.

Preferred Technical and Professional Expertise

• Experience with container technologies (Docker, Kubernetes, EKS, ECS).
• Experience with multiple cloud providers (IBM, AWS, GCP, Azure, etc).
• Experience in distributed computing.