Senior Software Engineer

RESPONSIBILITIES:

• Design, develop, and maintain cloud-based infrastructure to support the deployment and scaling of machine learning models. Implement automated pipelines for continuous integration and continuous deployment (CI/CD) of ML models, ensuring seamless transitions from development to production environments.
• Collaborate closely with Data Scientists and AI teams to understand model requirements and facilitate the transition from prototype to production. 
• Develop APIs, microservices, and other components necessary to integrate ML models into existing systems, enabling real-time inference and decision-making.
• Leverage cloud services to optimize the deployment and performance of machine learning models and associated infrastructure. Utilize services such as AWS SageMaker, Lambda, and ECS to build scalable, cost-effective solutions that support real-time ML/AI workloads.
• Monitor and optimize the performance of ML models in production, addressing issues related to latency, scalability, and resource utilization.
• Act as a key technical partner to Data Scientists, providing guidance on best practices for model deployment, versioning, and infrastructure design.
• Support AI teams by troubleshooting and resolving technical challenges related to model deployment and performance in production.
• Stay up-to-date with the latest advancements in ML infrastructure, cloud computing, and AI deployment strategies. Proactively suggest and implement improvements to enhance the efficiency, reliability, and scalability of ML operations within the organization.

QUALIFICATIONS:

• Bachelor’s Degree: A degree in Computer Science, Software Engineering, or a related field; or equivalent practical experience.
• 5+ years of experience in software engineering, with a significant focus on building and maintaining ML infrastructure in cloud environments.
• Deep expertise in AWS services, including but not limited to SageMaker, Lambda, ECS, S3, and IAM, with the ability to design and optimize cloud-based ML infrastructure.
• Strong programming skills in languages such as Python or Java, with a focus on building robust, maintainable code.
• Proven experience in productionalizing ML models, including building APIs and services that enable real-time inference.
• Expertise in designing scalable, resilient cloud architectures that support large-scale ML operations.
• Strong understanding of microservices, distributed systems, and the challenges of deploying and maintaining ML models in production environments.
• Excellent collaboration skills, with the ability to work closely with Data Scientists, AI and Software teams, and other cross-functional stakeholders.
• Agile Methodologies: Experience working in Agile/Scrum environments, with a focus on rapid iteration and continuous improvement.