4 Ml Operations Jobs

Location: Bangalore/Noida/Pune/Gurgaon Education: B.E. / B. Tech / M.E. / M. Tech / MCA Job Responsibilities: Model Deployment and Management: Drive ML prototypes into production ensuring seamless deployment and management on cloud at scale. Monitor real-time performance of deployed models, analyze data, and proactively address performance issues. Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability. Collaboration and Integration: Collaborate with DevOps engineers to manage cloud compute resources for ML model deployment and performance optimization. Work closely with ML scientists, software engineers, data engineers, and other stakeholders to implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, and automated deployment. Innovation and Continuous Improvement: Stay updated with the latest advancements in MLOps technologies and recommend new tools and techniques. Contribute to the continuous improvement of team processes and workflows. Share knowledge and expertise to promote a collaborative learning environment. Development and Documentation: Build software to run and support machine-learning models. Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes. Participate in fast iteration cycles and adapt to evolving project requirements. Business Solutions and Strategy: Propose solutions and strategies to business challenges. Collaborate with Data Science team, Front End Developers, DBA, and DevOps teams to shape architecture and detailed designs. Mentorship: Conduct code reviews and mentor junior team members. Foster strong interpersonal skills, excellent communication skills, and collaboration skills within the team. Mandatory Skills: Programming Languages: Proficiency in Python (3.x) and SQL. ML Frameworks and Libraries: Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture. Databases: Proficiency in SQL and NoSQL databases. Mathematics and Algorithms: In-depth knowledge of mathematics, statistics, and algorithms. ML Modules and REST API: Proficient with ML modules and REST API. Version Control: Hands-on experience with version control applications (GIT). Model Deployment and Monitoring: Experience with model deployment and monitoring. Data Processing: Ability to turn unstructured data into useful information (e.g., auto-tagging images, text-to-speech conversions). Problem-Solving: Analytically agile with strong problem-solving capabilities. Learning Agility: Quick to learn new concepts and eager to explore and build new features. Qualifications: Education: Bachelors or Master’s degree in Computer Science, Data Science, or a related field. Experience: Minimum of 6 years of hands-on experience in MLOps, deploying and managing machine learning models in production environments, preferably in cloud-based environments. Role & responsibilities Preferred candidate profile

**Key Responsibilities** People management - Lead a team of software engineers, DS, DE, MLE, in the design, development, and delivery of software solutions. Program management - Strong program leader that has run program management functions to efficiently deliver ML projects to production and manage its operations. Work with Business stakeholders & customers in the Retail Business domain to execute the product vision using the power of AI/ML. Scope out the business requirements by performing necessary data-driven statistical analysis. Set goals and, objectives using proper business metrics and constraints. Conduct exploratory analysis on large volumes of data, understand the statistical shape, and use the right visuals to drive & present the analysis. Analyse and extract relevant information from large amounts of data and derive useful insights on a big-data scale. Create labelling manuals and work with labellers to manage ground truth data and perform feature engineering as needed. Work with software engineering teams, data engineers and ML operations team (Data Labellers, Auditors) to deliver production systems with your deep learning models. Select the right model, train, validate, test, optimise neural net models and keep improving our image and text processing models. Architecturally optimize the deep learning models for efficient inference, reduce latency, improve throughput, reduce memory footprint without sacrificing model accuracy. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Create and enhance model monitoring system that could measure data distribution shifts, alert when model performance degrades in production. Streamline ML operations by envisioning human in the loop kind of workflows, collect necessary labels/audit information from these workflows/processes, that can feed into improved training and algorithm development process. Maintain multiple versions of the model and ensure the controlled release of models. Manage and mentor junior data scientists, providing guidance on best practices in data science methodologies and project execution. Lead cross-functional teams in the delivery of data-driven projects, ensuring alignment with business goals and timelines. Collaborate with stakeholders to define project objectives, deliverables, and timelines. **Skills required: ** MS/PhD from reputed institution with a delivery focus. 5+ years of experience in data science, with a proven track record of delivering impactful data-driven solutions. Delivered AI/ML products/features to production. Seen the complete cycle from Scoping & analysis, Data Ops, Modelling, MLOps, Post deployment analysis. Experts in Supervised and Semi-Supervised learning techniques. Hands-on in ML Frameworks - Pytorch or TensorFlow. Hands-on in Deep learning models. Developed and fine-tuned Transformer based models. ( Input output metric, Sampling technique) Deep understanding of Transformers, GNN models and its related math & internals. Exhibit high coding standards and create production quality code with maximum efficiency. Hands-on in Data analysis & Data engineering skills involving Sqls, PySpark etc. Exposure to ML & Data services on the cloud AWS, Azure, GCP Understanding internals of compute hardware - CPU, GPU, TPU is a plus. Can leverage the power of hardware accel to optimize the model execution — PyTorch Glow, cuDNN, is a plus.

Experience in AWS Cloud Mandatory along with ML Ops

Exp - 6- 10 Years Level - Senior Consultant Skill - ML OPS with Gen AI Location - Bengaluru Event on 9th May in Bangalore location Hybrid Mode (2 days WFO in a week) Education - B.Tech/B.E/MS/MBA ML OPs Engineer Required: 6-10 years of Consulting, Data, and Analytics experience Experience in descriptive & predictive analytics, both theoretical and practical knowledge in basic ML algorithms like linear and non-linear regression, linear and non-linear classification, dimensional reduction, anomaly detection, statistical concepts and techniques like theoretical distributions, parametric and non-parametric inference 6+ years of experience implementing & executing data science projects throughout the entire lifecycle: Developing/designing and implementing solutions E2E in production. Strong knowledge of Python or R Programming experience with Node.js, SQL, Java, JavaScript OR PERL Experience in cloud-based data platforms on AWS, GCP and Azure Understanding of multi-tier application architectures Ability to develop, test and maintain programming environments and architectural standards Foundational understanding of application development lifecycle and using tools like ANT, Maven, Gradle and Version control (SVN OR GIT OR BitBucket) Experience with working in an agile development lifecycle and continuous integration processes using tools such as Jenkins Experience in doing deployments for Java, .NET , Angular , Node.js , PHP, Python applications using Jenkins/Bamboo Experience on code quality assessment tools and integration with CI tool Strong logical structuring and problem-solving skills Strong verbal, written and presentation skills Preferred Additional Experience in using Spark either with Scala or Python Experience with different database types like RDS and NoSQL Experience in cloud deployments Knowledge of working in a Linux environment Strong understanding and experience configuring, managing and supporting applications using tools such as OpsWorks, Datadog and CloudWatch on AWS Experience in Docker /Swarm / Kubernetes Experience or exposure to Test Driven Development Experience (Junit / TestNG) Experience on Behavior Driven Development Experience (Cucumber / Selenium) Expertise in any commercial data visualization tool such as Tableau, Qlik, Power BI Experience with real time data movement solutions that use security and encryption protocols while data is in transit