39 Ml Deployment Jobs - Page 2

What you will do: Successful candidates will demonstrate excellent skill and maturity, be self-motivated as well as team-oriented, and have the ability to support the development and implementation of end-to-end ML-enabled software solutions to meet the needs of internal stakeholders. Those who will excel in this role will be those who listen with an ear to the overarching goal, not just the immediate concern that started the query. They will be able to show their recommendations are contextually grounded in an understanding of the practical problem, the data, and theory as well as what product and software solutions are feasible and desirable. The core responsibilities of this role are: Leading a team of machine learning engineers to build, automate, and deploy ML models and pipelines in production. Enact ML best practices for the team to follow. Developing ML model pipelines from proof of concept to production. Documenting the work process, including pipeline set up, experiment execution, and model deployment. Monitoring performance of models in production. Collaborate with stakeholders to help meet their objectives and provide them with periodic status updates. What you will need: Graduate education in a computationally intensive domain. 5+ years of prior relevant work or lab experience in ML projects, and 2+ years as tech lead or team management experience. Experience with building data pipelines (Kubeflow, sklearn pipelines, etc.). Experience with building and deploying REST APIs (Flask, FastAPI) Advanced proficiency with Python and SQL (BigQuery/MySQL). Extensive knowledge of ML frameworks and libraries. (Ray, Feast, ClearML, etc.) Experience with cloud services (AWS / GCP), Docker, Kubernetes, and CI/CD pipelines. Monitor models performance and microservices health status in Production.

? Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Collaborate with sales, pre-sales &consulting team to assist in creating solutions and propositions for proactive demand generation ii. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for recruitment, joint research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options c. Integrate model performance management tools into the current business infrastructure 3. Team Management a. Resourcing i. Support recruitment process to on-board right resources for the team b. Talent Management i. Support on boarding and training for the team members to enhance capability & effectiveness ii. Manage team attrition c. Performance Management i. Conduct timely performance reviews and provide constructive feedback to own direct reports ii. Be a role model to team for five habits iii. Ensure that the Performance Nxt is followed for the entire team d. Employee Satisfaction and Engagement i. Lead and drive engagement initiatives for the team

Key Responsibilities: CI/CD Pipeline Management & Automation: Design, implement, and maintain robust CI/CD pipelines for deploying machine learning models and solutions. Automate and streamline deployment processes using AWS services such as CodePipeline, CodeBuild, CodeDeploy, and CodeCommit. Ensure seamless integration of model training, testing, and deployment stages within the CI/CD pipeline. Set up and manage infrastructure as code (IaC) using tools like AWS CloudFormation or Terraform for creating scalable and reliable environments for ML applications. Automate deployment, scaling, and monitoring of machine learning models in AWS environments using AWS Lambda, ECS, EKS, and SageMaker. AWS Cloud Services Management & Security: Manage and configure AWS cloud services such as EC2, S3, SageMaker, Lambda, and others to support machine learning pipelines and production environments. Use AWS SageMaker for managing the ML lifecycle, including data preparation, training, tuning, and model deployment. Set up automated workflows for model retraining and versioning based on new data inputs and performance metrics. Ensure compliance with industry standards and internal policies regarding data privacy, security, and governance for machine learning solutions. Implement best practices in DevOps, including version control, code quality checks, and deployment automation using AWS services. Continuously improve infrastructure by staying up-to-date with new AWS features, best practices, and emerging technologies. Monitoring & Optimization: Monitor the performance of deployed ML models and pipelines using AWS CloudWatch, CloudTrail, and other monitoring tools. Implement automated testing, validation, and monitoring processes to ensure models perform as expected in production environments. Optimize costs and performance by automating resource scaling, ensuring high availability, and improving pipeline efficiency. Collaboration & Support: Collaborate with data scientists, machine learning engineers, and DevOps teams to integrate ML models into production systems. Provide support and troubleshooting expertise for pipeline issues, including model failures, deployment bottlenecks, and scaling problems. Work closely with security teams to implement best practices for security and compliance, ensuring that data and models are protected within AWS. Key Skills & Qualifications: Education: Bachelors degree in Computer Science, Information Technology, or a related field. Experience : 5+ years of experience as an AWS Engineer, DevOps Engineer, or Cloud Engineer, with a focus on CI/CD pipelines and machine learning solutions. Strong expertise in AWS cloud services (S3, EC2, SageMaker, Lambda, CodePipeline, etc.). Experience with CI/CD tools like AWS CodePipeline, GitLab CI, or similar platforms. Proficiency with containerization tools such as Docker and Kubernetes for managing microservices architecture. Knowledge of infrastructure as code (IaC) tools such as AWS CloudFormation, Terraform etc. Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and deployment in production environments. Experience in setting up and managing CI/CD pipelines for machine learning or data science solutions. Familiarity with version control systems like Git and deployment automation practices. Strong knowledge of monitoring and logging tools (e.g., AWS CloudWatch) for real-time performance tracking. Ability to work collaboratively with cross-functional teams, including data scientists, ML engineers, and DevOps teams. Strong verbal and written communication skills for documentation and knowledge sharing.

Total AI Systems, Inc., headquartered in Lansing, Michigan, specializes in developing commercial software solutions that utilize advanced artificial intelligence to improve business efficiency. We are a profitable, product-based company with offices in the United States, India, and Canada. Our team is made up of rockstars who know that delivering a rockstar performance for our customers means going above and beyond. Whether its early mornings, late nights, or continuous practice and refinement, we are dedicated to providing our very best and having a lot of fun along the way. We take care of our employees, and in turn, they take care of us. We're seeking individuals who are ready to go the extra mile and join us on this journey! We are currently seeking AL/ML Engineers to join our dynamic team. The ideal candidate will possess deep technical expertise in machine learning and artificial intelligence, with a proven track record of developing scalable AI solutions. Your role will involve everything from data analysis and model building to integration and deployment, ensuring our AI initiatives drive substantial business impact. The following are some of the responsibilities for this position: Develop end-to-end AI/ML solutions with a strong focus on GenAI, NLP and predictive analytics. Identifying problems in the current product and working in tandem with AI and backend engineers or the implementation of the solutions to these problems and train/optimize/validate models for all the identified ML/AI/NLP/CV/GenAI problems. Experience delivering and maintaining productionized end-to-end Machine Learning solutions, form data preparation, experimentation, model training, model serving and model update. Design, train, and deploy ML models to production environments. Collaborate with developers, designers and product managers to build scalable and effective solutions. Write clean, maintainable and efficient code in Python and Node.js. Work with various databases (SQL, NoSQL, RDB, etc.) and ensure data integrity. Design and develop backend logic to support ML applications. Leverage cloud platforms (preferably AWS) for deploying and managing AI solutions. Use Docker to containerize applications for streamlined deployment. Continuously explore and integrate new ML tools, frameworks and best practices. This is an exciting position with a growing company and offers a lot of upsides. This is a challenging position that requires dedication and determination. Some requirements of the position include: 1-3 years of experience in machine learning engineering or AI, preferably in a SaaS environment. Hands on experience in NLP and generative AI models, with a solid foundation in Python, node js and exposure in model fine-tuning and training. Experience working in a small, product-based company with a hands-on approach. Proven track record of building and delivering software products in a small team environment. Solid understanding of databases (SQL, NoSQL, Relational Database, Low-code, No-code) and data modeling. Experience deploying ML models and services in production environments. Should have experience with NLP, GenAI technologies and cloud platforms (AWS preferred). Working knowledge of Docker for application deployment. Strong software engineering skills with an interest in end-to-end development. Passion for learning and exploring new technologies. Good to have familiarity with PHP.

About The Role : Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Collaborate with sales, pre-sales &consulting team to assist in creating solutions and propositions for proactive demand generation ii. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for recruitment, joint research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options c. Integrate model performance management tools into the current business infrastructure 3. Team Management a. Resourcing i. Support recruitment process to on-board right resources for the team b. Talent Management i. Support on boarding and training for the team members to enhance capability & effectiveness ii. Manage team attrition c. Performance Management i. Conduct timely performance reviews and provide constructive feedback to own direct reports ii. Be a role model to team for five habits iii. Ensure that the Performance Nxt is followed for the entire team d. Employee Satisfaction and Engagement i. Lead and drive engagement initiatives for the team Deliver No. Performance Parameter Measure 1. Demand generation Order booking 2. Revenue generation through delivery Timeliness, customer success stories, customer use cases 3. Capability Building & Team Management % trained on new skills, Team attrition %

Looking for 5 to 10 years experience of ML Engineer with strong Azure Cloud DevOps with even stronger DABs DevOps skills with even stronger DABs Databricks Asset Bundles implementation knowledge Translate business requirement into technical solution Implementation of MLOPS Scalable solution using AIML and reduce the risk of Fraud and other fiscal crisis Creating MLOPS Architecture and implementing it for multiple models in a scalable and automated way Designing and implementing end to end ML solutions Operationalize and monitor machine learning models using high end tools and technologies Design implementation of DevOps principles in Machine Learning Data Science quality assurance and testing Collaborate with data scientists engineers and other key stakeholders Key Responsibilities Azure Cloud Engineering Design implement and manage scalable cloud infrastructure on Microsoft Azure Ensure high availability performance and security of cloud based applications Collaborate with cross functional teams to define and implement cloud solutions Develop and maintain CICD pipelines using Azure DevOps Automate deployment processes to ensure efficient and reliable software delivery Monitor and troubleshoot CICD pipelines to ensure smooth operation DABs Databricks Asset Bundles Implementation Lead the implementation and management of Databricks Asset Bundles Optimize data workflows and ensure seamless integration with existing systems Provide expertise in DABs to enhance data processing and analytics capabilities Machine Learning Deployment Deploy machine learning models into production environments Monitor and maintain ML models to ensure optimal performance Collaborate with data scientists and engineers to integrate ML solutions into applications

frameworksThe Opportunity: (Brief Overview of the Role) We are seeking a talented ML Ops engineer to join our team and play a key role in deploying, managing and optimizing machine learning models in production. The ideal candidate will have a strong background in machine learning and software engineering, with experience in DevOps practices and cloud computing. Roles & Responsibilities: Collaborate with data scientists and software engineers to deploy machine learning models into production environments. Design and implement scalable and reliable ML pipelines for model training, evaluation, and deployment. Develop and maintain infrastructure automation scripts for provisioning, configuration, and orchestration using platforms such as Resource Manager (ARM) templates, Automation, PowerShell, or other relevant tools. Optimize ML pipelines for efficiency, scalability, and cost-effectiveness Monitor, optimize, and troubleshoot Azure/AWS resources and services to ensure high availability, reliability, and performance of cloud-based applications and systems. Implement security policies, access controls, and encryption protocols to safeguard Azure/AWS environments and data, adhering to compliance and governance requirements. Collaborate with development teams to streamline the continuous integration and continuous deployment (CI/CD) process in Azure/AWS DevOps or similar tools for efficient application delivery. Provide expertise and support in areas such as Azure/AWS cost management, capacity planning, scalability, and disaster recovery strategies. Participate in the evaluation of new Azure/AWS offerings, technologies, and services to drive innovation and improve the overall cloud environment. Qualifications What you will need to succeed in the role: (Minimum Qualification and Skills Required) Bachelor's degree in Computer Science, Information Technology, or a related field; relevant certifications such as Microsoft Certified: Azure Administrator Associate or Microsoft Certified: Azure Solutions Architect Expert preferred. Strong proficiency in Python programming and familiarity with machine learning frameworks such as TensorFlow or Pytorch. Proficiency in implementing and managing security, identity, and access management solutions, leveraging Active Directory, Security Center, and other related tools. Hands-on experience with scripting and automation using tools such as Azure Resource Manager templates, PowerShell, Ansible, or other relevant technologies. Strong understanding of networking concepts and experience in configuring and troubleshooting Azure/AWS Virtual Networks, VPN gateways, and ExpressRoute. Familiarity with cloud-native monitoring, logging, and alerting tools such as Monitor, Log Analytics, Application Insights, and their integration with respective cloud services and applications. Knowledge of best practices in Azure/AWS DevOps, CI/CD pipelines, and experience working in an agile development environment. Excellent troubleshooting skills, with the ability to analyze complex issues in cloud environments and provide scalable solutions. Strong communication and collaboration skills, with the ability to work effectively in a team- oriented, fast-paced environment.

Project Description: Grid Dynamics aims building enterprise generative AI framework to deliver innovative, scalable and efficient AI-driven solutions across business functions.Due to constant scaling of digital capabilities the platform requires enhancements to incorporate cutting-edge generative AI features and meet emerging business demands. Platform should onboard brand new capabilities like Similarity Search (image,video and voice);Ontology and entity managment;voice and file mgmt [text to speech & vice-versa, metadata tagging, multi-media file support];Advanced RAG ; Multi-Modal capabilities Responsibilities: As an LLMOps Engineer, you will be responsible for providing expertise on overseeing the complete lifecycle management of large language models (LLM). This includes the development of strategies for deployment, continuous integration and delivery (CI/CD) processes, performance tuning, and ensuring high availability of our LLM services. You will collaborate closely with data scientists, AI/ML engineers, and IT teams to define and align LLM operations with business goals, ensuring a seamless and efficient operating model. In this role, you will: Define and disseminate LLMOps best practices. Evaluate and compare different LLMOps tools to incorporate the best practices. Stay updated on industry trends and advancements in LLM technologies and operational methodologies. Participate in architecture design/validation sessions for the Generative AI use cases with entities. Contribute to the development and expansion of GenAI use cases, including standard processes, framework, templates, libraries, and best practices around GenAI. Design, implement, and oversee the infrastructure required for the efficient operation of large language models in collaboration with client entities. Provide expertise and guidance to client entities in the development and scaling of GenAI use cases, including standard processes, framework, templates, libraries, and best practices around GenAI Serve as the expert and representative on LLMops Practices, including: (1) Developing and maintaining CI/CD pipelines for LLM deployment and updates. (2) Monitoring LLM performance, identifying and resolving bottlenecks, and implementing optimizations. (3) Ensuring the security of LLM operations through comprehensive risk assessments and the implementation of robust security measures. Collaborate with data and IT teams to facilitate data collection, preparation, and model training processes. Practical experience with training, tuning, utilizing LLMs/SLMs. Strong experience with GenAI/LLM frameworks and techniques, like guardrails, Langchain, etc. Knowledge of LLM security and observability principles. Experience of using Azure cloud services for ML Experience of using Azure cloud services for ML Min requirements: Programming languages: Python Public Cloud: Azure Frameworks: K8s, Terraform, Arize or any other ML/LLM observability tool Experience: Experience with public services like Open AI, Anthropic and similar, experience deploying open source LLMs will be a plus Tools: LangSmith/LangChain,guardrails Would be a plus: Knowledge of LLMOps best practices. Experience with monitoring/logging for production models (e.g. Prometheus, Grafana, ELK stack) We offer: Opportunity to work on bleeding-edge projects Work with a highly motivated and dedicated team Competitive salary Flexible schedule Benefits package - medical insurance, sports Corporate social events Professional development opportunities Well-equipped office

Project Description: Grid Dynamics wants to build a centralized, observable and secure platform for their ML, Computer Vision, LLM and SLM models. Grid Dynamics wants to onboard a vast number of AI agents, able to cover multiple required skills, ensuring a certain level of control and security in regards to their usage and availability. The observable platform must be vendor-agnostic, easy to extend to multiple type of AI applications and flexible in terms of technologies, frameworks and data types. This project is focused on establishing a centralized LLMOps capability where every ML, CV, AI-enabled application is monitored, observed, secured and provides logs of every activity. The solution consists of key building blocks such monitor every step in a RAG, Multimodal RAG or Agentic Platform, track performances and provide curated datasets for potential fine-tuning. Alignment with business scenarios, PepVigil provides also certain guardrails that allow or block interactions user-to-agent, agent-to-agent or agent-to-user. Also, Guardrails will enable predefined workflows, aimed to give more control over the series of LLM chains. Details on Tech Stack Job Qualifications and Skill Sets Advanced degree in Data Science, Computer Science, Statistics, or a related field Setting up Agent Mesh (LangSmith) Setting up Agent communication protocols (JSON/XML etc) Setting up message queues, CI/CD pipelines (Azure Queue Storage, Azure DevOps) Setting up integrations Langgrah, LangFuse Knowledge on Observability tool Arize-Phoenix tools Managing Agent Registry, Integrating with AgentAuth framework like Composio Setting up AgentCompute (Sandpack, E2BDev, Assistant APIs) Integration with IAM (Azure IAM, OKTA) Performing/Configuring Dynamic Orchestration and agent permissions Tech Stack Required: ML MLOPs Agent (Agent / Agent Mesh) LangFuse, LanChain, LangGraph Deployments (Docker, Jenkins, Kubernetes) Cloud Platforms: Azure/AWS/GCP We offer: Opportunity to work on bleeding-edge projects Work with a highly motivated and dedicated team Competitive salary Flexible schedule Benefits package - medical insurance, sports Corporate social events Professional development opportunities Well-equipped office

Role & responsibilities Job Title: Power Apps, Power Automate, and Machine Learning Consultant Job Type: Contract Job Overview: We are seeking a skilled and motivated Power Apps, Power Automate, and Machine Learning Consultant to join our dynamic team. In this role, you will be responsible for developing custom applications and workflows using Microsoft Power Apps and Power Automate, along with building intelligent solutions, including a chatbot powered by Machine Learning (ML). This role requires a strong blend of Power Platform expertise and AI/ML knowledge to provide innovative solutions that automate business processes and improve user interaction. Key Responsibilities: Power Apps Development: Design, develop, and customize applications using Microsoft Power Apps to meet business requirements, ensuring user-friendly interfaces and optimized functionality. Power Automate Workflow Development: Create and maintain automated workflows using Microsoft Power Automate to streamline business processes, including approvals, notifications, and data processing tasks. Machine Learning & Chatbot Development: Develop and deploy chatbots powered by AI and machine learning models, integrating them with Power Apps and Power Automate for automated communication and self-service. Requirements Gathering: Work closely with business users to understand their needs, document requirements, and propose solutions based on Power Platform and machine learning capabilities. Data Integration: Build integrations between Power Apps, Power Automate, and external data sources (e.g., SharePoint, SQL Server, Microsoft Dataverse) and machine learning models or services. Solution Architecture: Design scalable, secure, and robust solutions for business challenges, considering user experience, security, and long-term maintenance. Machine Learning Model Development: Utilize ML algorithms to develop chatbots, sentiment analysis, predictive models, and other AI-based solutions that enhance the automation and intelligence of business processes. Training & Deployment: Train ML models on relevant data, deploy them for real-world use, and continuously monitor and improve model performance. Testing & Deployment: Conduct thorough testing of developed solutions, including functional, performance, and security testing. Deploy applications, workflows, and ML models into production environments. Support & Maintenance: Provide post-deployment support and troubleshooting for Power Apps, Power Automate, and ML-based solutions. Maintain and optimize workflows, apps, and models. User Training: Provide training and support to end-users, helping them understand how to use the developed Power Apps, workflows, and chatbots effectively. Documentation: Create comprehensive documentation for custom apps, workflows, chatbots, and ML models, ensuring proper knowledge transfer. Required Skills & Qualifications: Experience with Power Apps and Power Automate: Proven experience in designing, developing, and deploying applications and workflows using Microsoft Power Platform tools (Power Apps and Power Automate). Machine Learning Knowledge: Strong background in machine learning, including developing and deploying AI models, preferably for chatbot creation and natural language processing (NLP). Chatbot Development: Experience in developing intelligent chatbots using ML frameworks (e.g., Microsoft Bot Framework, Azure Cognitive Services, or similar tools). Data Integration: Experience with integrating Power Apps and Power Automate with Microsoft Dataverse, SharePoint, SQL Server, and third-party systems, as well as integrating ML models and APIs. Business Process Automation: Strong understanding of business process automation and ability to translate business requirements into technical solutions. Problem Solving: Excellent analytical and troubleshooting skills for identifying and solving complex problems in Power Platform and machine learning applications. Collaboration: Ability to work effectively with both technical and non-technical teams. Communication Skills: Strong verbal and written communication skills for interacting with stakeholders, creating documentation, and training users. Salary and Perks best in the industry

Role Description As an AI/ML Engineer at MySGS & Co., you will play a key role in designing, developing, and maintaining machine learning (ML) systems and AI-driven automation solutions. You will collaborate closely with product managers, data scientists, data engineers, architects, and other cross-functional teams to build scalable and production-ready ML models. Your role will focus on both ML development and MLOps, ensuring seamless deployment, monitoring, and automation of ML models in real-world applications. You will be responsible for optimizing end-to-end ML pipelines, automating workflows, managing model lifecycle operations (MLOps), and ensuring AI systems are scalable and cost-efficient. You will embrace a build-measure-learn approach, continuously iterating and improving models for performance and reliability. Responsibilities Design, develop, and maintain ML models to solve business challenges and drive automation. Implement and optimize ML algorithms for efficiency, scalability, and AI-powered insights. Conduct experiments, A/B testing, and model evaluations to improve performance. Develop, containerize, and deploy AI/ML systems in production environments using best practices. Automate and streamline ML pipelines, ensuring smooth transitions from development to production. Monitor and troubleshoot the performance, accuracy, and drift of ML models in production. Execute and automate model validation tests, ensuring robustness and reliability. Optimize training and inference workflows, enhancing model efficiency and speed. Manage model versioning, deployment strategies, and rollback mechanisms. Implement and maintain CI/CD pipelines for ML models, ensuring smooth integration with engineering workflows. Review code changes, pull requests, and pipeline configurations to uphold quality standards. Stay updated with emerging AI/ML technologies, MLOps best practices, and cloud-based ML platforms. Skills and Qualifications Strong programming skills in Python or R, with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn) Experience deploying and maintaining ML models using Docker, Kubernetes, and cloud-based AI services (AWS Sagemaker, GCP Vertex AI, Azure ML). Solid understanding of MLOps principles, including CI/CD for ML models, model monitoring, and automated retraining. Knowledge of data engineering principles, data preprocessing, and feature engineering for ML pipelines. Familiarity with workflow orchestration tools. Experience with real-time model serving and API deployment. Strong analytical and problem-solving skills with a keen attention to detail. Ability to collaborate cross-functionally and work in a fast-paced AI-driven

Wipro Limited (NYSE:WIT, BSE:507685, NSE:WIPRO) is a leading technology services and consulting company focused on building innovative solutions that address clients most complex digital transformation needs. Leveraging our holistic portfolio of capabilities in consulting, design, engineering, and operations, we help clients realize their boldest ambitions and build future-ready, sustainable businesses. With over 230,000 employees and business partners across 65 countries, we deliver on the promise of helping our customers, colleagues, and communities thrive in an ever-changing world. For additional information, visit us at www.wipro.com. About The Role : Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions. Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options 3. Team Management a. Talent Management i. Support on boarding and training to enhance capability & effectiveness Deliver No. Performance Parameter Measure 1. Demand generation # PoC supported 2. Revenue generation through delivery Timeliness, customer success stories, customer use cases 3. Capability Building & Team Management # Skills acquired Reinvent your world.We are building a modern Wipro. We are an end-to-end digital transformation partner with the boldest ambitions. To realize them, we need people inspired by reinvention. Of yourself, your career, and your skills. We want to see the constant evolution of our business and our industry. It has always been in our DNA - as the world around us changes, so do we. Join a business powered by purpose and a place that empowers you to design your own reinvention. Come to Wipro. Realize your ambitions. Applications from people with disabilities are explicitly welcome.

Wipro Limited (NYSE:WIT, BSE:507685, NSE:WIPRO) is a leading technology services and consulting company focused on building innovative solutions that address clients most complex digital transformation needs. Leveraging our holistic portfolio of capabilities in consulting, design, engineering, and operations, we help clients realize their boldest ambitions and build future-ready, sustainable businesses. With over 230,000 employees and business partners across 65 countries, we deliver on the promise of helping our customers, colleagues, and communities thrive in an ever-changing world. For additional information, visit us at www.wipro.com. About The Role : Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Collaborate with sales, pre-sales &consulting team to assist in creating solutions and propositions for proactive demand generation ii. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for recruitment, joint research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options c. Integrate model performance management tools into the current business infrastructure 3. Team Management a. Resourcing i. Support recruitment process to on-board right resources for the team b. Talent Management i. Support on boarding and training for the team members to enhance capability & effectiveness ii. Manage team attrition c. Performance Management i. Conduct timely performance reviews and provide constructive feedback to own direct reports ii. Be a role model to team for five habits iii. Ensure that the Performance Nxt is followed for the entire team d. Employee Satisfaction and Engagement i. Lead and drive engagement initiatives for the team Deliver No. Performance Parameter Measure 1. Demand generation Order booking 2. Revenue generation through delivery Timeliness, customer success stories, customer use cases 3. Capability Building & Team Management % trained on new skills, Team attrition % Reinvent your world.We are building a modern Wipro. We are an end-to-end digital transformation partner with the boldest ambitions. To realize them, we need people inspired by reinvention. Of yourself, your career, and your skills. We want to see the constant evolution of our business and our industry. It has always been in our DNA - as the world around us changes, so do we. Join a business powered by purpose and a place that empowers you to design your own reinvention. Come to Wipro. Realize your ambitions. Applications from people with disabilities are explicitly welcome.

Wipro Limited (NYSE:WIT, BSE:507685, NSE:WIPRO) is a leading technology services and consulting company focused on building innovative solutions that address clients most complex digital transformation needs. Leveraging our holistic portfolio of capabilities in consulting, design, engineering, and operations, we help clients realize their boldest ambitions and build future-ready, sustainable businesses. With over 230,000 employees and business partners across 65 countries, we deliver on the promise of helping our customers, colleagues, and communities thrive in an ever-changing world. For additional information, visit us at www.wipro.com. About The Role : Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions. Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options 3. Team Management a. Talent Management i. Support on boarding and training to enhance capability & effectiveness Deliver No. Performance Parameter Measure 1. Demand generation # PoC supported 2. Revenue generation through delivery Timeliness, customer success stories, customer use cases 3. Capability Building & Team Management # Skills acquired Reinvent your world.We are building a modern Wipro. We are an end-to-end digital transformation partner with the boldest ambitions. To realize them, we need people inspired by reinvention. Of yourself, your career, and your skills. We want to see the constant evolution of our business and our industry. It has always been in our DNA - as the world around us changes, so do we. Join a business powered by purpose and a place that empowers you to design your own reinvention. Come to Wipro. Realize your ambitions. Applications from people with disabilities are explicitly welcome.