Software Engineer (MLOps)

Soroco is on a mission to redefine how work is performed globally. With multiple patents supporting its primary product, the Scout AI model, it constructs a work graph – a reliable tool to pinpoint unseen difficulties teams encounter in their work environment and how it affects business performance. This consequential graph is presently enhancing productivity for hundreds of organizations around the world, including some Fortune 500 companies. Media coverage about Soroco has appeared in the Harvard Business Review, Forbes, Fortune, and was also listed on Bloomberg's collection of ideas that defined 2022. With operations in Boston, London, and Bangalore, Soroco boasts of a strong alumni network from Harvard, MIT, and Carnegie Mellon. Scout, our dynamic transformer model, identifies unnoticed workplace issues and their consequence on business results. By autonomously finding solutions and empathizing with teams, Scout aids companies in fostering happier and more productive teams as of today. For more information on how we assist teams to discover their work graph, please visit www.soroco.com. What we are looking for: We are in search of a Software Engineer (SE) enthusiastic about working with a team to build the infrastructure in Soroco that handles deployment, updates, training, and running interference on our machine learning models in our product, Scout. The role purpose and scope: The Software Engineer (SE) will offer technical expertise and facilitate the company's technical evolution and delivery by collaborating with the engineering team. An SE typically works on defined problems, devising solutions in collaboration with others on the team. Responsibilities would include understanding the architectural designs and implementing features accordingly, addressing technical queries related to our ML systems, troubleshooting complex technical complications, project management, design, development, testing, deployment, and maintenance of systems. The role also includes interaction and collaboration with our proficient technical team across India and the US. Experience and skills: - We prefer 1-3 years of industry exposure in ML Lifecycle management. - Working experience in designing and implementing REST endpoints to support model inference. - Familiarity in executing cloud solutions, and building MLOps on the cloud (AWS, Azure, or GCP) - Baseline understanding of CI/CD pipelines orchestrated by Azure DevOps. - Knowledge in model optimization techniques like Quantization, Pruning etc. - Familiar with Data science model review, code refactoring, optimization, containerization, deployment, versioning, and monitoring of its quality. - Acquaintance with common model evaluation matrices for validating the quality of pre-trained models and finely-tuned tasks. - Experience in Data science models testing, validation and automation of tests. Bonus factors: - Quick learning ability and understanding of any problem. - Confidence in addressing unstructured problems. - Appreciation of good design and architecture. - Aspiration to design and build extensive, enterprise-grade software systems from scratch.