226 Mlops Jobs - Page 6

A bachelor's degree in Computer Science, Information Systems, Engineering, or other related discipline. Proficient in Azure ML, MLops, ML Flow Good in Data Science, Machine Learning, Deep Learning, Proficient in Python, Pandas, Numpy

Design, implement, and maintain end-to-end ML pipelines in Azure using Azure Machine Learning, Azure DevOps Collaborate with data scientists, software engineers, and other stakeholders to deploy and monitor machine learning models in production environments. Develop CI/CD pipelines for model training, validation, and deployment using Azure DevOps Implement best practices for model versioning, testing, and monitoring to ensure the reliability and performance of deployed models, leveraging Prometheus for monitoring and alerting. Configure and manage Prometheus for monitoring Kubernetes clusters, applications, and custom metrics, ensuring high availability and scalability. Troubleshoot and resolve issues related to model performance, scalability, and reliability in production environments. Stay up-to-date with the latest developments in Azure ML services, Helm, Prometheus, and best practices for ML Ops. Technology with heavy specialization in Veeam and Backup technologies. Extensive experience on Veeam Backup and recovery, Veeam one, Veeam Enterprise manager Veeam backup failure, Veeam back up configuration on multiple environment such as on-prem, Azure, AWS etc., Iron-mountain veeam backup and Tape library. Extensive experience and knowledge in the design, administration, tuning and troubleshooting of: Veeam Backup and Replication Veeam Enterprise Manager Veeam ONE Veeam Cloud Connect Veeam Service Provider Infrastructure Veeam Iron Mountain backup management Veeam Tape Backup management Azure Native backup Extensive experiences with Backup reporting and inventories.

A bachelor's degree in Computer Science, Information Systems, Engineering, or other related discipline. Proficient in Azure ML, MLops, ML Flow Good in Data Science, Machine Learning, Deep Learning, Proficient in Python, Pandas, Numpy

A bachelor's degree in Computer Science, Information Systems, Engineering, or other related discipline. Proficient in Azure ML, MLops, ML Flow Good in Data Science, Machine Learning, Deep Learning, Proficient in Python, Pandas, Numpy

A bachelor's degree in Computer Science, Information Systems, Engineering, or other related discipline. Proficient in Azure ML, MLops, ML Flow Good in Data Science, Machine Learning, Deep Learning, Proficient in Python, Pandas, Numpy

A bachelor's degree in Computer Science, Information Systems, Engineering, or other related discipline. Proficient in Azure ML, MLops, ML Flow Good in Data Science, Machine Learning, Deep Learning, Proficient in Python, Pandas, Numpy

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.

Design and optimize data pipelines, ETL processes, and big data solutions using Azure Data Factory, Synapse, and Databricks. Develop and deploy ML models with TensorFlow/PyTorch, automate workflows, manage CI/CD for data, and ensure security Required Candidate profile 5-15 years in data engineering or ML. Expertise in Azure Data Services, Big Data (Spark, Kafka), ML frameworks (TensorFlow, PyTorch), Python/SQL, CI/CD, and data security best practices.

Pyramid overview A role with Target Data Science & Engineering means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Statistics, Optimization or Machine Learning teams, youll be challenged to harness Targets impressive data breadth to build the algorithms that power solutions our partners in in Marketing, Supply Chain Optimization, Network Security and Personalization rely on About The role As Senior Engineer, you will join a Target Tech team responsible for Promotion forecasting and optimization. You will play crucial role in designing, implementing, and optimizing the machine learning solutions in production. Additionally, youll apply best practices in software design, participate in code reviews, create a maintainable well-tested codebase with relevant documentation. At an organizational level, you will conduct training sessions, present work to technical and non-technical peers/leaders, build knowledge on business priorities/strategic goals and leverage this knowledge while building requirements and solutions for each business need. Core responsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs. About you: 4-year degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) or equivalent experience MS in Computer Science, Applied Mathematics, Statistics, Physics or equivalent work or industry experience 4 plus years of experience in end-to-end application development, data exploration, data pipelining, API design, optimization of model latency Expertise in MLOps frameworks and hands on experience in MLOps tools preferably Google Vertex ai 2 plus years of experience deploying Machine Learning algorithms into production environments - including model and system monitoring and troubleshooting Highly proficient programming in Scala and/ or Python/ Pyspark Good understanding of Big Data tech - specifically Hadoop, Kafka, Spark Experience in handling streaming data and real-time forecasting solutions deployment Solid understanding of data analysis techniques, including data cleaning, preprocessing, and visualization Demonstrated ability collaborating with data scientists, software engineers and product managers to understand the business requirements and translate to machine learning solutions at scale Excellent communication skills with the ability to clearly tell data driven stories through appropriate visualizations, graphs, and narratives Self-driven and results oriented - able to meet tight timelines Motivated, team player with ability to collaborate effectively across global team Understanding of retail industry and pricing concepts is added advantage Bonus Points: Extensive experience with Deep Learning frameworks TensorFlow, Pytorch or Keras PhD in Computer Science, Applied Mathematics, Statistics, Physics or related quantitative field Extensive experience developing highly distributed ML systems at scale Familiarity with Vertex AI and Cloud ML ecosystem will be desirable Experience in mentoring the junior team members ML skillset and career development .

Role & responsibilities : Job Title : AI Engineer (AI-Powered Agents, Knowledge Graphs, & MLOps) Location: Hyderabad Job Type : Full-time Hands-on Gen AI development in GCP and Azure stack Job Summary : We seek an AI Engineer with deep expertise in building AI-powered agents, designing and implementing knowledge graphs, and optimizing business processes through AI-driven solutions. The role also requires hands-on experience in AI Operations (AI Ops), including continuous integration/deployment (CI/CD), model monitoring, and retraining. The ideal candidate will have experience working with open-source or commercial large language models (LLMs) and be proficient in using platforms like Azure Machine Learning Studio or Google Vertex AI to scale AI solutions effectively. Key Responsibilities : AI Agent Development : Design, build, and deploy AI-powered agents for applications such as virtual assistants, customer service bots, and task automation systems using LLMs and other AI models. Knowledge Graph Implementation : Develop and implement knowledge graphs for enterprise data integration, enhancing the retrieval, structuring, and management of large datasets to support decision-making. AI-Driven Process Optimization : Collaborate with business units to optimize workflows using AI-driven solutions, automating decision-making processes and improving operational efficiency. AI Ops (MLOps) : Implement robust AI/ML pipelines that follow CI/CD best practices to ensure continuous integration and deployment of AI models across different environments. Model Monitoring and Maintenance : Establish processes for real-time model monitoring, including tracking performance, drift detection, and accuracy of models in production environments. Model Retraining and Optimization : Develop automated or semi-automated pipelines for model retraining based on changes in data patterns or model performance. Implement processes to ensure continuous improvement and accuracy of AI solutions. Cloud and ML Platforms : Utilize platforms such as Azure Machine Learning Studio, Google Vertex AI, and open-source frameworks for end-to-end model development, deployment, and monitoring. Collaboration : Work closely with data scientists, software engineers, and business stakeholders to deploy scalable AI solutions that deliver business impact. MLOps Tools : Leverage MLOps tools for version control, model deployment, monitoring, and automated retraining processes to ensure operational stability and scalability of AI systems. Performance Optimization : Continuously optimize models for scalability and performance, identifying bottlenecks and improving efficiencies. Qualifications : Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 3+ years of experience as an AI Engineer, focusing on AI-powered agent development, knowledge graphs, AI-driven process optimization, and MLOps practices. Proficiency in working with large language models (LLMs) such as GPT-3/4, GPT-J, BLOOM, or similar, including both open-source and commercial variants. Experience with knowledge graph technologies, including ontology design and graph databases (e.g., Neo4j, AWS Neptune). AI Ops/MLOps Expertise : Hands-on experience with AI/ML CI/CD pipelines, automated model deployment, and continuous model monitoring in production environments. Familiarity with tools and frameworks for model lifecycle management, such as MLflow, Kubeflow, or similar. Strong skills in Python, Java, or similar languages, and proficiency in building, deploying, and monitoring AI models. Solid experience in natural language processing (NLP) techniques, including building conversational AI, entity recognition, and text generation models. Model Monitoring & Retraining : Expertise in setting up automated pipelines for model retraining, monitoring for drift, and ensuring the continuous performance of deployed models. Experience in using cloud platforms like Azure Machine Learning Studio, Google Vertex AI, or similar cloud-based AI/ML tools. Preferred Skills : Experience with building or integrating conversational AI agents using platforms like Microsoft Bot Framework, Rasa, or Dialogflow. Familiarity with AI-driven business process automation and RPA integration using AI/ML models. Knowledge of advanced AI-driven process optimization tools and techniques, including AI orchestration for enterprise workflows. Experience with containerization technologies (e.g., Docker, Kubernetes) to support scalable AI/ML model deployment. Certification in Azure AI Engineer Associate, Google Professional Machine Learning Engineer, or relevant MLOps-related certifications is a plus. Preferred candidate profile Perks and benefits

Role & responsibilities Develop MLOps pipelines for AI/ML model training, validation, and deployment. Automate data ingestion, preprocessing, and feature engineering workflows. Optimize model training and inference performance for scalability and efficiency. Manage CI/CD pipelines for ML models using tools like Jenkins, CI/CD. Deploy models using cloud-based (AWS, Azure, GCP) and on-prem solutions (Docker, Kubernetes). Monitor ML models in production to ensure performance, reliability, and drift detection. Implement robust logging, version control, and rollback strategies for ML models. Work closely with data scientists, AI engineers, and Dev Ops teams to improve AI system integration. Ensure compliance with data security, governance, and model explain-ability best practices. Preferred candidate profile Perks and benefits

Role Overview: Lead AI & Data Science initiatives, driving AI research, Agentic AI, MLOps, and Azure AI for a service-based company. Oversee AI/ML model deployment, innovation, and client solutions , ensuring business impact and scalability. Key Responsibilities: Develop AI strategy, focusing on Agentic AI, Generative AI, and ML Operations . Lead AI research, innovation, and Azure-based AI solutions . Manage AI/ML model development, deployment, and optimization . Mentor teams and collaborate with business, product, and engineering leaders . Engage with clients, delivering AI-powered solutions and consulting . Skills & Qualifications: 10-15 years in AI, Data Science, ML Operations, and Azure AI . Expertise in Agentic AI, AI Research, Generative AI, and ML lifecycle . Proficiency in Python, TensorFlow, PyTorch, MLOps tools, and Kubernetes . Strong leadership, stakeholder management, and problem-solving skills. Preferred: Masters/PhD in AI, Data Science, or related field .

Skills : C++, Python, ROS, ROS2, ROS Navigation stack & path planningOS : Windows, Linux, Ubuntu Other Skills : Configuration Management, Software Testing, Basics of QT, Basics of image processing, Object Detection and Basics of machine learning.Job details: Proficiency in robotics middleware such as ROS (Robot Operating System) Development of customized Nodes in ROS according to Robotic/Sensor Application Familiarity with Linux. Ability to write robust code in python and C++ Knowledge of math, probability and algorithms Familiarity with machine learning frameworks and libraries. Experience developing software systems for localization & mapping. Object detection & tracking and control of the robot. Ability to select hardware to run ML model with required latency. Experience with Integrating Robotic Sub Systems, Experience working on Unmanned Ground Vehicles and Unmanned Aerial Vehicles. Troubleshooting robotic systems and applications. Proven experience as an Artificial Intelligence /Machine Learning developer

About the role : We are looking for a Machine Learning Engineer who will work on a broad range of cutting-edge data analytics and machine learning problems across a variety of industries. More specifically, you will Collaborate with data scientists to transition machine learning models from development to production. Design and implement MLOps pipelines for model training, deployment, and monitoring on GCP. Utilize GCP services for version control, continuous integration, and continuous deployment (CI/CD) of machine learning models. Implement monitoring and logging solutions to track the performance and health of deployed models. Optimize and scale machine learning workflows on GCP to handle production workloads efficiently. Stay updated on the latest developments in MLOps practices and GCP services to ensure best practices are followed. Desired Skills and Experience : 3+ years of experience with at least 3+ years of relevant DS experience. Good working knowledge on GCP Proficient in a structured Python Follows good software engineering practices and has an interest in building reliable and robust software. Good knowledge of DS concepts and professional experience in developing and enhancing algorithms and models to solve business problem Conducting quantitative analyses and interpreting results Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/PySpark is desirable. Excellent written and verbal communication skills. B.Tech from Tier-1 college M.S or M. Tech is preferred.

About the role : We are looking for a Machine Learning Engineer who will work on a broad range of cutting-edge data analytics and machine learning problems across a variety of industries. More specifically, you will Collaborate with data scientists to transition machine learning models from development to production. Design and implement MLOps pipelines for model training, deployment, and monitoring on GCP. Utilize GCP services for version control, continuous integration, and continuous deployment (CI/CD) of machine learning models. Implement monitoring and logging solutions to track the performance and health of deployed models. Optimize and scale machine learning workflows on GCP to handle production workloads efficiently. Stay updated on the latest developments in MLOps practices and GCP services to ensure best practices are followed. Desired Skills and Experience : 3+ years of experience with at least 3+ years of relevant DS experience. Good working knowledge on GCP Proficient in a structured Python Follows good software engineering practices and has an interest in building reliable and robust software. Good knowledge of DS concepts and professional experience in developing and enhancing algorithms and models to solve business problem Conducting quantitative analyses and interpreting results Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/PySpark is desirable. Excellent written and verbal communication skills. B.Tech from Tier-1 college M.S or M. Tech is preferred.

About the role : We are looking for a Machine Learning Engineer who will work on a broad range of cutting-edge data analytics and machine learning problems across a variety of industries. More specifically, you will Collaborate with data scientists to transition machine learning models from development to production. Design and implement MLOps pipelines for model training, deployment, and monitoring on GCP. Utilize GCP services for version control, continuous integration, and continuous deployment (CI/CD) of machine learning models. Implement monitoring and logging solutions to track the performance and health of deployed models. Optimize and scale machine learning workflows on GCP to handle production workloads efficiently. Stay updated on the latest developments in MLOps practices and GCP services to ensure best practices are followed. Desired Skills and Experience : 3+ years of experience with at least 3+ years of relevant DS experience. Good working knowledge on GCP Proficient in a structured Python Follows good software engineering practices and has an interest in building reliable and robust software. Good knowledge of DS concepts and professional experience in developing and enhancing algorithms and models to solve business problem Conducting quantitative analyses and interpreting results Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/PySpark is desirable. Excellent written and verbal communication skills. B.Tech from Tier-1 college M.S or M. Tech is preferred.

Skills : C++, Python, ROS, ROS2, ROS Navigation stack & path planningOS : Windows, Linux, Ubuntu Other Skills : Configuration Management, Software Testing, Basics of QT, Basics of image processing, Object Detection and Basics of machine learning. Job details: - Proficiency in robotics middleware such as ROS (Robot Operating System) - Development of customized Nodes in ROS according to Robotic/Sensor Application - Familiarity with Linux. - Ability to write robust code in python and C++ - Knowledge of math, probability and algorithms- Familiarity with machine learning frameworks and libraries. - Experience developing software systems for localization & mapping. - Object detection & tracking and control of the robot. - Ability to select hardware to run ML model with required latency. - Experience with Integrating Robotic Sub-Systems, - Experience working on Unmanned Ground Vehicles and Unmanned Aerial Vehicles. - Troubleshooting robotic systems and applications. - Proven experience as an Artificial Intelligence /Machine Learning developer

Key Responsibilities Data Pipeline Development: Design, develop, and optimize robust data pipelines to efficiently collect, process, and store large-scale datasets for AI/ML applications. ETL Processes: Develop and maintain Extract, Transform, and Load (ETL) processes to ensure accurate and timely data delivery for machine learning models. Data Integration: Integrate diverse data sources (structured, unstructured, and semi-structured data) into a unified and scalable data architecture. Data Warehousing & Management: Design and manage data warehouses to store processed and raw data in a highly structured, accessible format for analytics and AI/ML models. AI/ML Model Development: Collaborate with Data Scientists to build, fine-tune, and deploy machine learning models into production environments. Focus on model optimization, scalability, and operationalization. Automation: Implement automation techniques to support model retraining, monitoring, and reporting. Cloud & Distributed Systems: Work with cloud platforms (AWS, Azure, GCP) and distributed systems to store and process data efficiently, ensuring that AI/ML models are scalable and maintainable in the cloud environment. Data Quality & Governance: Implement data quality checks, monitoring, and governance frameworks to ensure the integrity and security of the data being used for AI/ML models. Collaboration: Work cross-functionally with Data Science, Business Intelligence, and other engineering teams to meet organizational data needs and ensure seamless integration with analytics platforms. Required Skills and Qualifications Bachelor's or Masters Degree in Computer Science, Engineering, Data Science, or a related field. Strong proficiency in Python for AI/ML and data engineering tasks. Experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Keras. Proficient in SQL and working with relational databases (e.g., MySQL, PostgreSQL, SQL Server). Strong experience with ETL pipelines and data wrangling in large datasets. Familiarity with cloud-based data engineering tools and services (e.g., AWS (S3, Lambda, Redshift), Azure, GCP). Solid understanding of big data technologies like Hadoop, Spark, and Kafka for data processing at scale. Experience in managing and processing both structured and unstructured data. Knowledge of version control systems (e.g., Git) and agile development methodologies. Experience with data containers and orchestration tools such as Docker and Kubernetes. Strong communication skills to collaborate effectively with cross-functional teams. Preferred Skills Experience with Data Warehouses (e.g., Amazon Redshift, Google BigQuery, Snowflake). Familiarity with CI/CD pipelines for ML model deployment and automation. Familiarity with machine learning model monitoring and performance optimization. Experience with data visualization tools like Tableau, Power BI, or Plotly. Knowledge of deep learning models and frameworks. DevOps or MLOps experience for automating deployment of models. Advanced statistics or math background for improving model performance and accuracy.

Locations We are located in Austin (USA), Singapore, and Hyderabad (India). Job Locations: Hyderabad, Ahmedabad, Indore Role Overview We are looking for a highly skilled Senior Data Scientist with a passion for deploying and maintaining machine learning models, leading teams, and driving innovation. Summary: Years of Experience: Minimum of 8+ years Job Location: Hyderabad, Ahmedabad, Indore Notice Period: Immediate to 30 days Work Shift: General Shift What Youll Do Key Responsibilities: Lead the strategic direction of machine learning infrastructure with a focus on performance, scalability, and reliability. Drive innovation by developing advanced machine learning solutions. Collaborate with the Data Science team to deploy, maintain, and optimize ML models in production environments. Architect and oversee the development of real-time and batch inference systems. Mentor mid-level engineers, fostering growth and technical excellence. Conduct comprehensive code reviews and encourage a culture of continuous learning. Communicate insights and translate complex technical concepts into actionable business solutions. Stay ahead of industry trends, integrating new technologies and tools. Technical Skills Extensive experience with machine learning methodologies and infrastructure. Expertise in Python, TensorFlow, PyTorch, and other ML frameworks. Strong knowledge of real-time data processing and pipeline development. Proficiency in cloud platforms (AWS, GCP, Azure) for ML model deployment. Hands-on experience with Kubernetes, Docker, and container orchestration. Deep understanding of MLOps/DevOps best practices, including CI/CD pipelines, testing, and version control. Soft Skills Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. Proven leadership experience, mentoring engineering teams to success. Ability to effectively translate technical concepts into actionable business solutions. Passion for continuous learning and staying updated with industry advancements. What Youll Bring Education: Bachelors or Masters degree in Computer Science, Data Science, or a related field. Proven experience in large-scale machine learning projects. Deep knowledge of deploying scalable, robust systems for real-time and batch inference use cases. Location: Jaipur

3+ years of work experience in Python programming for AI/ML, deep learning, and Generative AI model development Proficiency in TensorFlow/PyTorch, Hugging Face Transformers and Langchain libraries Hands-on experience with NLP, LLM prompt design and fine-tuning, embeddings, vector databases and agentic frameworks Strong understanding of ML algorithms, probability and optimization techniques 6+ years of experience in deploying models with Docker, Kubernetes, and cloud services (AWS Bedrock, SageMaker, GCP Vertex AI) through APIs, and using MLOps and CI/CD pipelines Familiarity with retrieval-augmented generation (RAG), cache-augmented generation (CAG), retrieval-integrated generation (RIG), low-rank adaptation (LoRA) fine-tuning Ability to write scalable, production-ready ML code and optimized model inference Experience with developing ML pipelines for text classification, summarization and chat agents Prior experience with SQL and noSQL databases, and Snowflake/Databricks

Lead AI Engineer Experience: 8 - 10 Years Exp Salary : Competitive Preferred Notice Period : Within 30 Days Shift : 10:00AM to 6:00PM IST Opportunity Type: Hybrid (Ahmedabad) Placement Type: Permanent (*Note: This is a requirement for one of Uplers' Clients) Must have skills required : LLMs, Generative AI, Natural Language Processing (NLP), AI, Python, MLOps, TensorFlow, Scalable Architecture Good to have skills : Multi-Agent Systems, Federated learning, Reinforcement Learning, AI automation Inferenz (One of Uplers' Clients) is Looking for: Lead AI Engineer who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you. Role Overview Description Position: Lead AI Engineer Location: Ahmedabad, Pune Required Experience: 8+ Years Preferred Immediate Joiner We are seeking an experienced Lead AI Engineer to spearhead AI initiatives, drive innovation, and architect scalable AI/ML solutions. This role involves leading AI-driven projects, mentoring teams, and integrating AI solutions into enterprise applications. Key Responsibilities: Architect and develop enterprise-scale AI/ML solutions, including Generative AI and LLMs. Lead research and implementation of cutting-edge AI methodologies. Collaborate with business stakeholders to define AI strategy and align it with business goals. Optimize model performance and scalability for production deployment. Establish best practices for AI development, including MLOps and model monitoring. Drive innovation by exploring new AI techniques and frameworks. Manage and mentor a team of AI engineers, fostering a culture of continuous learning. Present insights, research findings, and AI-driven solutions to leadership teams. Required Skills & Qualifications: Masters or Ph.D. in Computer Science, AI, Machine Learning, or a related discipline. 8+ years of experience in AI, ML, and deep learning, with a track record of successful AI deployments. Strong expertise in LLMs, Generative AI, NLP, and multimodal AI. Proficiency in Python, R, Java, and AI/ML frameworks like TensorFlow, PyTorch, or Hugging Face. Extensive experience with cloud-based AI solutions, MLOps, and scalable architectures. Ability to lead AI-driven initiatives and collaborate with cross-functional teams. Strong leadership skills to manage, mentor, and guide engineering teams. Excellent communication and stakeholder management abilities. Preferred Skills: Experience in multi-agent systems, federated learning, and AI ethics. Understanding of computer vision, reinforcement learning, and AI automation. Contributions to AI research, patents, or high-impact publications. How to apply for this opportunity: Easy 3-Step Process: 1. Click On Apply! And Register or log in on our portal 2. Upload updated Resume & Complete the Screening Form 3. Increase your chances to get shortlisted & meet the client for the Interview! About Our Client: At Inferenz, our team of innovative technologists and domain experts help accelerating the business growth through digital enablement and navigating the industries with data, cloud and AI services and solutions. We dedicate our resources to increase efficiency and gain a greater competitive advantage by leveraging various next generation technologies. About Uplers: Our goal is to make hiring and getting hired reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant product and engineering job opportunities and progress in their career. (Note: There are many more opportunities apart from this on the portal.) So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!

Practical exp with ML frameworks such as TensorFlow, PyTorch, or scikit-learn Cloud platforms (AWS, Azure, or GCP) and their ML-specific services Python, machine learning operations, CI/CD, Docker, Kubernetes, Jenkins, GitLab CI, or Azure DevOps

About the role : Saint-Gobain is modernizing its Data practices with more and more Artificial Intelligence algorithms and is seeking a senior MLOps Engineer to reinforce Saint-Gobain center of excellence in AI & GenAI, assisting Saint-Gobain Data Science teams worldwide in building and deploying AI applications to maximize the value brought to the Business by Artificial Intelligence applications. In this role, you will: Act as an expert, assisting Data Scientists in designing AI solutions, selecting and implementing the right technologies according to their needs Move AI applications from prototyping stage to production Extend the AI & GenAI foundations (Saint-Gobain Python libraries for AI & GenAI, CI/CD pipelines, reference AI/GenAI architecture, and global AI/Gen AI tools) which will be used by all main Data Science teams in Saint-Gobain worldwide, including local AI/GenAI factories Deploy and scale AI applications developed by Data Scientists to new Business units Ensure the automation of applications & models monitoring Support end-users on the use of Saint-Gobain AI & GenAI foundations In this role, you will report to: a INDEC Data & AI manager In this role, you will interact with: Data Scientists from different BUs of Saint-Gobain worldwide to guide them on the best use of Saint-Gobain AI & GenAI technologies and tooling Local MLOps engineers in BUs assisting Data Scientists locally, to provide them expertise on AI tooling and methodology Business users to support their use of Saint-Gobain global AI/GenAI applications Qualifications: MS degree in Data Science Proven experience (5+ years) in AI Engineering in a cloud environment (Azure / AWS / GCP) Skills: Expertise in MLOps (models re-training, labelling & monitoring) Expertise in Python and ML models optimization. Expertise in Cloud AI Services, especially Azure ML Good knowledge in Generative AI architectures and solutions Good knowledge in applications industrialization (Dockers / Kubernetes) Good knowledge in DevOps (Git, CI/CD pipelines) Fluent in English and takes initiatives

Exciting Opportunity with a Leading MNC! We are hiring MLOps Engineers to join a top multinational company and work on MLOps pipelines. If you have the skills and experience, this is your chance to grow in a dynamic, fast-paced environment! Key responsibilities: Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models. Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes. Take offline models data scientists build and turn them into a real machine learning production system. Automate the training, testing and deployment processes for machine learning models. Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability. Implement best practices for version control, model reproducibility and governance. Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness. Troubleshoot and resolve issues related to model deployment and performance. Ensure compliance with security and data privacy standards in all MLOps activities. Keep up-to-date with the latest MLOps tools, technologies and trends. Provide support and guidance to other team members on MLOps practices. Communicate with a team of data scientists, data engineers and architect, document the processes. Required Qualifications: Experience in designing and implementing pipelines MLOps on AWS, Azure, or GCP. Hands on building CI/CD pipelines orchestration using TeamCity, Jenkins, Airflow or similar tools. Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes, OpenShift. Programming languages like Python, Go, Ruby or Bash, good understanding of Linux, knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc. Ability to understand tools used by data scientist and experience with software development and test automation. Fluent in English, good communication skills and ability to work in a team. Desired Qualifications: Bachelors degree in Computer Science or Software Engineering. Experience in using AWS, MS Azure or GCP services. Good to have any associate MLOps Certification. If you or someone you know is a great fit, apply now or share your profile with us Satish@coretek.io

Professional experience with new age technologies in a relevant industry (tech startups, multinational corporations, R&D labs) Hands-on experience -application of cutting-edge technologies in real-world scenarios -industry-standard tools -platforms