Machine Learning Engineer/MLOps Engineer

About Nasuni :Nasuni is a profitable, growing SaaS data infrastructure company reinventing enterprise file storage and data management in an AI-driven world. We power the data infrastructure of the worlds most innovative enterprises. Backed by Vista Equity Partners, our engineers are building whats next with AI. Our platform lets businesses seamlessly store, access, protect, and unlock AI-driven insights from exploding volumes of unstructured file data. As an engineer here, youll help build AI-powered infrastructure trusted by 900+ global customers, including Dow, Mattel, and Autodesk.Nasuni is headquartered in Boston, USA with offices in Cork-Ireland, London-UK and we are growing an India Innovation Center in Hyderabad India to leverage exuberant IT talent available in India. We have a hybrid work culture. 3 days a week working from the Hyderabad office during core working hours and 2 days working from home.Summary Of Role :The Machine Learning Engineer will own the end-to-end AI and ML stack, from data science and traditional modeling to agentic generative AI. This role will design and maintain data pipelines, build and evaluate models, and productionize systems that solve real-world problems at scale. Youll work across experimentation, deployment, and monitoring, building intelligent agents, automation, and decision-support tools, while partnering with cross-functional teams to drive measurable impact through data and AI.Nasunis Cloud Services team is responsible for the design and development of UniFS-as-a-Service and a collection of product offerings that run on that platform. These services can be deployed in a customers AWS or Azure account or as a Nasuni-hosted SaaS service, all via infrastructure as code, and leverage a variety of serverless technologies in order to interact with the customers data stored in Nasuni.Design, build, and maintain end-to-end ML pipelines, including data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment using the best practices for MLOps and engineering.- Develop and integrate agentic generative AI solutions, such as LLM-based assistants, RAG workflows, and tool-using agents, and embed these into internal platforms or products in collaboration with the broader engineering team.- Support and improve existing ML and AI systems in production, by debugging issues, monitoring performance and drift, tuning models, and helping balance quality, latency, and infrastructure cost.- Collaborate closely with global product, data, and engineering partners, translating business or functional requirements into technical designs, and ensuring ML/AI features are practical, robust, and aligned with stakeholder needs.- Run experiments and communicate results effectively, including designing evaluations and A/B tests, analyzing outcomes, and documenting and presenting findings, trade-offs, and recommendations to both technical and non-technical audiences.Other Duties :- Perform hands-on data work as needed, including data cleaning, labeling, preprocessing, and exploratory data analysis to improve data quality and model readiness.- Contribute to research and development by exploring new ML/AI techniques, evaluating libraries and tools, and rapidly prototyping ideas that could enhance existing solutions.- Create and maintain internal documentation and provide occasional support for adjacent engineering, analytics, and operations tasks as required.- Additional responsibilities and tasks may be assigned as needed on a project or interim basisBasic Qualifications :- Bachelors or Masters degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.- 3- 6 years of hands-on experience in applied machine learning or data science, preferably in a product or platform environment.- Strong programming skills in Python and solid working knowledge of SQL; comfortable writing production-quality code and collaborating via Git.- Practical experience building and deploying traditional ML models (e.g., classification, regression, ranking, time series), including data preprocessing, feature engineering, and model evaluation.- Experience working with at least one major cloud platform (AWS, Azure, or GCP) and common ML/AI tooling (e.g., Good understanding of modern deep learning concepts and exposure to LLMs or generative AI (e.g., using foundation models via APIs, fine-tuning or prompt engineering).- Preferred Qualifications :- Hands-on experience with LLM-based systems such as RAG pipelines, chatbots, or tool-using/agentic workflows.- model versioning, monitoring, pipelines, orchestration frameworks) such as SageMaker AI, MLFlow, DVC, Bedrock.- Competitive compensation programs- Flexible time off and leave policies- Comprehensive health and wellness coverage- Hybrid and flexible work arrangements- Employee referral and recognition programs- Professional development and learning support- Inclusive, collaborative team culture- Modern office spaces with team events and perks- Retirement and statutory benefits as per Indian regulations (ref:hirist.