.. ensuring effective communication and managing expectations throughout the recruitment process. Data Management: Maintain accurate and up-to-date candidate and client data in the recruitment management system, generate reports, and analyze recruitment metrics to drive continuous improvement. Market Research: .. read more
Job Type: Contract Location: India (Remote) ️ Work Timings: US Timings - 4 PM to 1 AM IST ️ Experience Required: 4+ Years of relevant experience Hiring Timeline: Immediate - 30 Days Hiring Process: Resume review - Initial screen - Technical Interviews (2) - Leadership & Behavior Interviews About the company Incommon is hiring on behalf of a US-based B2B software development company. Their services include software development, mobile application and development, data mining, devops, marketing services, etc. Position Summary We are seeking a versatile and adaptable Data Scientist with expertise in a range of technology domains, including Network Operations, Infrastructure Management, Cloud Computing, MLOps, Deep Learning, NLP, DevOps, LLM infrastructure. This role encompasses a wide range of responsibilities, including designing and implementing cloud solutions, building MLOps pipelines on cloud platforms (AWS, Azure), orchestrating CI/CD pipelines using tools like GitLab CI and GitHub Actions, and taking ownership of data pipeline and engineering infrastructure design to support enterprise machine learning systems at scale. Key Responsibilities: Infra: Manage cloud-based infrastructure on AWS and Azure, focusing on scalability and efficiency. Utilize containerization technologies like Docker and Kubernetes for application deployment. NetOps Monitor and maintain network infrastructure, ensuring optimal performance and security. Implement load balancing solutions for efficient traffic distribution. Infrastructure and Systems Management: Cloud Computing: Design and implement cloud solutions, including the development of MLOps pipelines. Ensure proper provisioning, resource management, and cost optimization in a cloud environment. MLOps and DevOps: Orchestrate CI/CD pipelines using GitLab CI and GitHub Actions for streamlined software delivery. Collaborate with data scientists and engineers to operationalize and optimize data science models. Apply software engineering rigor, including CI/CD and automation, to machine learning projects. Data Pipelines and Engineering Infrastructure: Design and develop data pipelines and engineering infrastructure to support enterprise machine learning systems. Transform offline models created by data scientists into production-ready systems. Build scalable tools and services for machine learning training and inference. Technology Evaluation and Integration: Identify and evaluate new technologies to enhance the performance, maintainability, and reliability of machine learning systems. Develop custom integrations between cloud-based systems using APIs. Proof-of-Concept Development: Facilitate the development and deployment of proof-of-concept machine learning systems. Emphasize auditability, versioning, and data security during development. Requirements : Strong software engineering skills in complex, multi-language systems. Proficiency in Python and comfort with Linux administration. Experience working with cloud computing and database systems. Expertise in building custom integrations between cloud-based systems using APIs. Experience with containerization (Docker) and Kubernetes in cloud computing environments. Familiarity with data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.). Ability to translate business needs into technical requirements. Strong understanding of software testing, benchmarking, and continuous integration. Exposure to machine learning methodology and best practices. Exposure to deep learning approaches and modeling frameworks (PyTorch, TensorFlow, Keras, etc.).