531 Mlops Jobs - Page 21

Role : Principal ML Ops Architect Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker

Role : Principal ML Ops Architect Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker

Airflow-We are seeking a highly skilled and motivated GenAI Developer with 36 years of experience to work on cutting-edge Generative AI platforms. The ideal candidate will have strong programming expertise, experience working in multi-cloud environments, and hands-on exposure to MLOps tools and modern data engineering frameworks. Key Responsibilities: Design, develop, and deploy solutions using GenAI platforms Build and maintain scalable applications using Java, Python, and SpringAI Work in a poly-cloud environment across AWS, GCP, and Azure Collaborate with cross-functional teams to integrate AI solutions into existing workflows

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker

Role : Principal ML Ops ArchitectResponsibilities :1. Strategic Leadership :a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes.c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture :a. Design and implement scalable, reliable, and efficient ML Ops architectures.b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle.c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management :a. Lead and mentor a team of ML Ops engineers and architects.b. Foster collaboration and knowledge sharing among team members.c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research :a. Stay up-to-date with emerging ML Ops trends and technologies.b. Research and evaluate new tools and techniques to enhance ML Ops capabilities.c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills :- 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure.- Experience with distributed computing frameworks (Spark, Hadoop)- Knowledge of graph databases and auto ML libraries- Bachelor's / Master's degree in computer science, analytics, mathematics, statistics- Strong experience in Python, SQL.- Solid understanding and knowledge of containerization technologies (Docker, Kubernetes).- Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow)- Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch).- Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).- Strong problem-solving and analytical skills.- Ability to plan, execute and take ownership of task.Keywords :- ML Ops / MLOps Architect- Azure DevOps- Docker- Kubernetes- TensorFlow- MLFlow- Pipeline- Machine Learning Platform Engineer- Data Science Platform Engineer- DevOps Engineer (with ML focus)- AI Engineer- Data Engineer- Cloud Engineer (with ML focus)- Software Engineer (with ML focus)- Model Deployment Specialist- MLOps Architect- CI/CD- PyTorch- Scikit-learn- Cloud Computing- Big Data- Azure- Azure Machine Learning- GCP- Vertex AI- AWS- Amazon SageMaker

Role : Principal ML Ops Architect Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker

Role : Principal ML Ops Architect Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker

Role: MLOps Engineer Experience : 8 yr to 15 yr Location: PAN India Key words -Skillset AWS SageMaker, Azure ML Studio, GCP Vertex AI PySpark, Azure Databricks MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline Kubernetes, AKS, Terraform, Fast API Responsibilities: Model Deployment, Model Monitoring, Model Retraining Deployment pipeline, Inference pipeline, Monitoring pipeline, Retraining pipeline Drift Detection, Data Drift, Model Drift Experiment Tracking MLOps Architecture REST API publishing Job Responsibilities: Research and implement MLOps tools, frameworks and platforms for our Data Science projects. Work on a backlog of activities to raise MLOps maturity in the organization. Proactively introduce a modern, agile and automated approach to Data Science. Conduct internal training and presentations about MLOps tools benefits and usage. Required experience and qualifications: Wide experience with Kubernetes. Experience in operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g. Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube). Good understanding of ML and AI concepts. Hands-on experience in ML model development. Proficiency in Python used both for ML and automation tasks. Good knowledge of Bash and Unix command line toolkit. Experience in CI/CD/CT pipelines implementation. Experience with cloud platforms - preferably AWS - would be an advantage.

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker

Responsibilities : 1. Strategic Leadership :a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes.c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture :a. Design and implement scalable, reliable, and efficient ML Ops architectures.b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle.c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management :a. Lead and mentor a team of ML Ops engineers and architects.b. Foster collaboration and knowledge sharing among team members.c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research :a. Stay up-to-date with emerging ML Ops trends and technologies.b. Research and evaluate new tools and techniques to enhance ML Ops capabilities.c. Contribute to the development of innovative ML Ops solutions.Minimum Required Skills :- 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure.- Experience with distributed computing frameworks (Spark, Hadoop)- Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics- Strong experience in Python, SQL.- Solid understanding and knowledge of containerization technologies (Docker, Kubernetes).- Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow)- Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch).- Certifications in cloud platforms or ML technologies can be a plus.- Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).- Strong problem-solving and analytical skills.- Ability to plan, execute and take ownership of task. Keywords :- ML Ops / MLOps Architect- Azure DevOps- Docker- Kubernetes- TensorFlow- MLFlow- Pipeline- Machine Learning Platform Engineer- Data Science Platform Engineer- DevOps Engineer (with ML focus)- AI Engineer- Data Engineer- Cloud Engineer (with ML focus)- Software Engineer (with ML focus)- Model Deployment Specialist- MLOps Architect- CI/CD- PyTorch- Scikit-learn- Cloud Computing- Big Data- Azure- Azure Machine Learning- GCP- Vertex AI- AWS- Amazon SageMaker

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker

Senior Data Scientist Experience: 5 - 7 Years Exp Salary : Upto INR 35 Lacs per annum Preferred Notice Period : Within 30 Days Shift : 10:00AM to 7:00PM IST Opportunity Type: Onsite (Bengaluru) Placement Type: Permanent (*Note: This is a requirement for one of Uplers' Clients) Must have skills required : Azure Devops Or GitHub, Azure SQL DB or Cosmo DB, EKS, ETL, MLOps, supply chain, Machine Learning Good to have skills : CI/CD, Databricks, Demand Forecasting, Spark Aioneers (One of Uplers' Clients) is Looking for: Senior Data Scientist 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 As a Senior Data Scientist, you will be a pivotal member of the data science team within aioneers. You will be leading the solution implementation of data science work streams within our supply chain analytics projects. Often the projects involve solving complex supply chain problems like demand forecasting, multi-echelon inventory optimization, production scheduling, intelligent order fulfillment, rough cut capacity planning (RCCP) etc. You will also provide technical guidance and mentorship junior data scientists in the team. You will own development and implementation of end-to-end life cycle of machine learning solutions from data modelling, feature engineering, ML solution structuring to MLOps process implementation of automated model serving You will play the role of a technology architect to design efficient MLOps process for large scale model deployments and high frequency servings using Azure data and ML services You will provide thought leadership to solution architects and project managers to come up with effective solution architecture for clients problems You will be building heuristics, Operations research techniques based (like linear programming and discrete optimization) solutions to solve optimization problems in supply chain space You will also lead the data engineering work activities within the projects to create required data models with features stores for ML implementations and post processing activities to make the outputs consumable for business use cases YOUR PROFILE We are looking for someone with 5+ years of relevant data science and machine learning experience in solving supply chain problems Data Science And Machine Learning Skills Understanding of statistical methods (e.g., regression, hypothesis testing) and optimization techniques like linear programming and mixed-integer programming for supply chain problems Proficiency in methods like ARIMA, SARIMAX, Prophet, or advanced techniques using neural networks (e.g., LSTMs, Temporal Fusion Transformer) Familiarity with supervised and unsupervised learning for classification (e.g., demand segmentation) and clustering (e.g., supplier categorization) Knowledge of CI/CD pipelines for ML, including retraining, deployment, and monitoring models using Azure DevOps or GitHub Actions Supply Chain Domain Knowledge: Seasoned expertise in demand forecasting using ML. Understanding the nuances of intermittent, erratic and lumpy demand patterns and how to solve them using ML techniques Knowledge of EOQ, reorder point models, safety stock modelling and inventory simulation techniques would be a plus Programming and Technical Skills Expertise in setting up end to end MLOps processes - model training, deployment, and tracking experiments Expertise in creating data pipelines for ETL processes and connecting supply chain data sources Proficiency in integrating ERP data from systems like SAP into Azure via connectors or APIs Deploying scalable ML models as APIs using AKS (Kubernetes) Expertise in handling large-scale supply chain datasets using Spark, Databricks, or Azure Synapse Advanced query skills in Azure SQL Database or Cosmos DB for real-time analytics Advanced proficiency in Python for ML modelling, data analysis, and libraries like Scikit-learn, PyTorch, TensorFlow Version control and automating deployments using Azure DevOps or GitHub Actions Ability to think through automation, pipeline design and other MLOps processes Conceptual and pragmatic knowledge of the concepts of data modelling, feature engineering, fine tuning machine learning models, statistical model validation Educational Background and Experience Engineering degree in computer science, informatics, data analytics and other relevant branches Affinity for new technologies and a drive for independent learning Affinity for an open feedback culture with flat hierarchies WHY AIONEERS? At aioneers, we are building the next generation innovative solutions on supply chain technologies. What we can offer is a wonderful team culture, flexible work hours, respect for your ideas, open discussions / open door policies and attractive remuneration. Your results count and not the hours. You will have the chance to actively participate in the development and execution of innovative business strategies on an international scale. 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: aioneers is a software and consulting company headquartered in Mannheim, Germany. We help businesses optimize their supply chain using our best-in-class supply chain expertise and our AI-powered technology, the AIO Platform. 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!

We are looking for an AI & Machine Learning Lead Engineer to lead the development and deployment of advanced AI and machine learning models. The ideal candidate will have a strong background in precision learning, large language models (LLMs), retrieval-augmented generation (RAG), natural language processing (NLP), and statistical methods for output analysis. This role involves leading a team of engineers and data scientists, setting strategic directions for AI/ML initiatives, and ensuring the delivery of impactful solutions that align with our business objectives. Key Responsibilities: Leadership and Strategy: Lead and manage a team of AI and machine learning engineers, providing technical guidance and mentorship. Oversee the entire machine learning lifecycle, from data collection and preprocessing to model training, evaluation, and deployment. Develop and drive the AI/ML strategy, aligning it with overall business goals. Collaborate with cross-functional teams, including data engineering, product management, and software development, to integrate ML models into production environments. Precision Learning and Large Language Models (LLMs): Design, develop, and optimize precision learning algorithms for specific business applications. Lead efforts in developing, fine-tuning, and deploying large language models (LLMs) to address various use cases such as text generation, summarization, translation, and conversational AI. Retrieval-Augmented Generation (RAG): Implement and optimize RAG systems to improve the performance and accuracy of AI solutions. Develop retrieval strategies that effectively integrate large-scale knowledge bases with LLMs to generate more accurate and contextually relevant outputs. Natural Language Processing (NLP): Develop and implement NLP models for tasks such as text classification, sentiment analysis, named entity recognition, summarization, and question-answering. Stay current with NLP research trends and advancements, implementing best practices to enhance model efficiency and performance. Output Analysis through Statistical Methods: Analyse model outputs using advanced statistical methods to ensure reliability, accuracy, and explainability. Implement A/B testing, hypothesis testing, and other statistical techniques to validate model performance and derive actionable insights. Research and Development: Stay abreast of the latest research in machine learning, deep learning, and AI. Propose and implement novel approaches to solve challenging problems. Collaborate with academic and research institutions to contribute to the machine learning community through publications, open-source projects, and conferences MLOps and Model Deployment: Collaborate with DevOps and data engineering teams to implement MLOps practices for CI/CD pipelines, model monitoring, and governance. Ensure scalable deployment of AI/ML models on cloud platforms (AWS, GCP, Azure) or on-premises environments. Mentorship and Team Leadership: Mentor and guide a team of machine learning engineers and data scientists, fostering a culture of continuous learning and innovation. Conduct regular code reviews, provide constructive feedback, and ensure adherence to best practices in machine learning. Required Qualifications: 8-10 years Experience with Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field; PhD is a plus. 5+ years of experience in machine learning and AI, with a strong focus on NLP, LLMs, RAG, and precision learning. Proven track record of leading AI/ML teams and projects from conception to deployment. Expertise in Python and relevant ML libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers. Strong understanding of NLP techniques, including transformer architectures (e.g., BERT, GPT). Experience in RAG techniques, knowledge retrieval systems, and integrating LLMs with external data sources. Proficiency in statistical analysis and hypothesis testing, with a strong foundation in experimental design. Excellent problem-solving skills, with the ability to articulate complex technical concepts to non-technical stakeholders. Preferred Qualifications: Experience with cloud services (AWS, GCP, Azure) for model deployment and scaling. Familiarity with MLOps practices, including model versioning, monitoring, and CI/CD pipelines for ML. Knowledge of advanced AI techniques such as reinforcement learning, meta-learning, and unsupervised learning. Strong publication record or contributions to the AI/ML community.

Overview We are seeking a skilled Associate Manager AIOps & MLOps Operations to support and enhance the automation, scalability, and reliability of AI/ML operations across the enterprise. This role requires a solid understanding of AI-driven observability, machine learning pipeline automation, cloud-based AI/ML platforms, and operational excellence. The ideal candidate will assist in deploying AI/ML models, ensuring continuous monitoring, and implementing self-healing automation to improve system performance, minimize downtime, and enhance decision-making with real-time AI-driven insights. Support and maintain AIOps and MLOps programs, ensuring alignment with business objectives, data governance standards, and enterprise data strategy. Assist in implementing real-time data observability, monitoring, and automation frameworks to enhance data reliability, quality, and operational efficiency. Contribute to developing governance models and execution roadmaps to drive efficiency across data platforms, including Azure, AWS, GCP, and on-prem environments. Ensure seamless integration of CI/CD pipelines, data pipeline automation, and self-healing capabilities across the enterprise. Collaborate with cross-functional teams to support the development and enhancement of next-generation Data & Analytics (D&A) platforms. Assist in managing the people, processes, and technology involved in sustaining Data & Analytics platforms, driving operational excellence and continuous improvement. Support Data & Analytics Technology Transformations by ensuring proactive issue identification and the automation of self-healing capabilities across the PepsiCo Data Estate. Responsibilities Support the implementation of AIOps strategies for automating IT operations using Azure Monitor, Azure Log Analytics, and AI-driven alerting. Assist in deploying Azure-based observability solutions (Azure Monitor, Application Insights, Azure Synapse for log analytics, and Azure Data Explorer) to enhance real-time system performance monitoring. Enable AI-driven anomaly detection and root cause analysis (RCA) by collaborating with data science teams using Azure Machine Learning (Azure ML) and AI-powered log analytics. Contribute to developing self-healing and auto-remediation mechanisms using Azure Logic Apps, Azure Functions, and Power Automate to proactively resolve system issues. Support ML lifecycle automation using Azure ML, Azure DevOps, and Azure Pipelines for CI/CD of ML models. Assist in deploying scalable ML models with Azure Kubernetes Service (AKS), Azure Machine Learning Compute, and Azure Container Instances. Automate feature engineering, model versioning, and drift detection using Azure ML Pipelines and MLflow. Optimize ML workflows with Azure Data Factory, Azure Databricks, and Azure Synapse Analytics for data preparation and ETL/ELT automation. Implement basic monitoring and explainability for ML models using Azure Responsible AI Dashboard and InterpretML. Collaborate with Data Science, DevOps, CloudOps, and SRE teams to align AIOps/MLOps strategies with enterprise IT goals. Work closely with business stakeholders and IT leadership to implement AI-driven insights and automation to enhance operational decision-making. Track and report AI/ML operational KPIs, such as model accuracy, latency, and infrastructure efficiency. Assist in coordinating with cross-functional teams to maintain system performance and ensure operational resilience. Support the implementation of AI ethics, bias mitigation, and responsible AI practices using Azure Responsible AI Toolkits. Ensure adherence to Azure Information Protection (AIP), Role-Based Access Control (RBAC), and data security policies. Assist in developing risk management strategies for AI-driven operational automation in Azure environments. Prepare and present program updates, risk assessments, and AIOps/MLOps maturity progress to stakeholders as needed. Support efforts to attract and build a diverse, high-performing team to meet current and future business objectives. Help remove barriers to agility and enable the team to adapt quickly to shifting priorities without losing productivity. Contribute to developing the appropriate organizational structure, resource plans, and culture to support business goals. Leverage technical and operational expertise in cloud and high-performance computing to understand business requirements and earn trust with stakeholders. Qualifications 5+ years of technology work experience in a global organization, preferably in CPG or a similar industry. 5+ years of experience in the Data & Analytics field, with exposure to AI/ML operations and cloud-based platforms. 5+ years of experience working within cross-functional IT or data operations teams. 2+ years of experience in a leadership or team coordination role within an operational or support environment. Experience in AI/ML pipeline operations, observability, and automation across platforms such as Azure, AWS, and GCP. Excellent Communication: Ability to convey technical concepts to diverse audiences and empathize with stakeholders while maintaining confidence. Customer-Centric Approach: Strong focus on delivering the right customer experience by advocating for customer needs and ensuring issue resolution. Problem Ownership & Accountability: Proactive mindset to take ownership, drive outcomes, and ensure customer satisfaction. Growth Mindset: Willingness and ability to adapt and learn new technologies and methodologies in a fast-paced, evolving environment. Operational Excellence: Experience in managing and improving large-scale operational services with a focus on scalability and reliability. Site Reliability & Automation: Understanding of SRE principles, automated remediation, and operational efficiencies. Cross-Functional Collaboration: Ability to build strong relationships with internal and external stakeholders through trust and collaboration. Familiarity with CI/CD processes, data pipeline management, and self-healing automation frameworks. Strong understanding of data acquisition, data catalogs, data standards, and data management tools. Knowledge of master data management concepts, data governance, and analytics.

Overview We are seeking a skilled Associate Manager AIOps & MLOps Operations to support and enhance the automation, scalability, and reliability of AI/ML operations across the enterprise. This role requires a solid understanding of AI-driven observability, machine learning pipeline automation, cloud-based AI/ML platforms, and operational excellence. The ideal candidate will assist in deploying AI/ML models, ensuring continuous monitoring, and implementing self-healing automation to improve system performance, minimize downtime, and enhance decision-making with real-time AI-driven insights. Support and maintain AIOps and MLOps programs, ensuring alignment with business objectives, data governance standards, and enterprise data strategy. Assist in implementing real-time data observability, monitoring, and automation frameworks to enhance data reliability, quality, and operational efficiency. Contribute to developing governance models and execution roadmaps to drive efficiency across data platforms, including Azure, AWS, GCP, and on-prem environments. Ensure seamless integration of CI/CD pipelines, data pipeline automation, and self-healing capabilities across the enterprise. Collaborate with cross-functional teams to support the development and enhancement of next-generation Data & Analytics (D&A) platforms. Assist in managing the people, processes, and technology involved in sustaining Data & Analytics platforms, driving operational excellence and continuous improvement. Support Data & Analytics Technology Transformations by ensuring proactive issue identification and the automation of self-healing capabilities across the PepsiCo Data Estate. Responsibilities Support the implementation of AIOps strategies for automating IT operations using Azure Monitor, Azure Log Analytics, and AI-driven alerting. Assist in deploying Azure-based observability solutions (Azure Monitor, Application Insights, Azure Synapse for log analytics, and Azure Data Explorer) to enhance real-time system performance monitoring. Enable AI-driven anomaly detection and root cause analysis (RCA) by collaborating with data science teams using Azure Machine Learning (Azure ML) and AI-powered log analytics. Contribute to developing self-healing and auto-remediation mechanisms using Azure Logic Apps, Azure Functions, and Power Automate to proactively resolve system issues. Support ML lifecycle automation using Azure ML, Azure DevOps, and Azure Pipelines for CI/CD of ML models. Assist in deploying scalable ML models with Azure Kubernetes Service (AKS), Azure Machine Learning Compute, and Azure Container Instances. Automate feature engineering, model versioning, and drift detection using Azure ML Pipelines and MLflow. Optimize ML workflows with Azure Data Factory, Azure Databricks, and Azure Synapse Analytics for data preparation and ETL/ELT automation. Implement basic monitoring and explainability for ML models using Azure Responsible AI Dashboard and InterpretML. Collaborate with Data Science, DevOps, CloudOps, and SRE teams to align AIOps/MLOps strategies with enterprise IT goals. Work closely with business stakeholders and IT leadership to implement AI-driven insights and automation to enhance operational decision-making. Track and report AI/ML operational KPIs, such as model accuracy, latency, and infrastructure efficiency. Assist in coordinating with cross-functional teams to maintain system performance and ensure operational resilience. Support the implementation of AI ethics, bias mitigation, and responsible AI practices using Azure Responsible AI Toolkits. Ensure adherence to Azure Information Protection (AIP), Role-Based Access Control (RBAC), and data security policies. Assist in developing risk management strategies for AI-driven operational automation in Azure environments. Prepare and present program updates, risk assessments, and AIOps/MLOps maturity progress to stakeholders as needed. Support efforts to attract and build a diverse, high-performing team to meet current and future business objectives. Help remove barriers to agility and enable the team to adapt quickly to shifting priorities without losing productivity. Contribute to developing the appropriate organizational structure, resource plans, and culture to support business goals. Leverage technical and operational expertise in cloud and high-performance computing to understand business requirements and earn trust with stakeholders. Qualifications 5+ years of technology work experience in a global organization, preferably in CPG or a similar industry. 5+ years of experience in the Data & Analytics field, with exposure to AI/ML operations and cloud-based platforms. 5+ years of experience working within cross-functional IT or data operations teams. 2+ years of experience in a leadership or team coordination role within an operational or support environment. Experience in AI/ML pipeline operations, observability, and automation across platforms such as Azure, AWS, and GCP. Excellent Communication: Ability to convey technical concepts to diverse audiences and empathize with stakeholders while maintaining confidence. Customer-Centric Approach: Strong focus on delivering the right customer experience by advocating for customer needs and ensuring issue resolution. Problem Ownership & Accountability: Proactive mindset to take ownership, drive outcomes, and ensure customer satisfaction. Growth Mindset: Willingness and ability to adapt and learn new technologies and methodologies in a fast-paced, evolving environment. Operational Excellence: Experience in managing and improving large-scale operational services with a focus on scalability and reliability. Site Reliability & Automation: Understanding of SRE principles, automated remediation, and operational efficiencies. Cross-Functional Collaboration: Ability to build strong relationships with internal and external stakeholders through trust and collaboration. Familiarity with CI/CD processes, data pipeline management, and self-healing automation frameworks. Strong understanding of data acquisition, data catalogs, data standards, and data management tools. Knowledge of master data management concepts, data governance, and analytics.

Responsibilities: Design and implement cloud solutions using AWS and Azure. Develop and maintain Infrastructure as Code (IAC) with Terraform. Create and manage CI/CD pipelines using GitHub Actions and Azure DevOps. Automate deployment processes and provisioning of compute instances and storage. Orchestrate container deployments with Kubernetes. Develop automation scripts in Python, PowerShell, and Bash. Monitor and optimize cloud resources for performance and cost-efficiency using tools like Datadog and Splunk. Configure Security Groups, IAM policies, and roles in AWS\Azure. Troubleshoot production issues and ensure system reliability. Collaborate with development teams to integrate DevOps and MLOps practices. Create comprehensive documentation and provide technical guidance. Continuously evaluate and integrate new AWS services and technologies Cloud engineering certifications (AWS, Terraform) are a plus. Excellent communication and problem-solving skills. Minimum Qualifications: Bachelors Degree in Computer Science or equivalent experience. Minimum of five years in cloud engineering, DevOps, or Site Reliability Engineering (SRE). Hands-on experience with AWS and Azure cloud services, including IAM, Compute, Storage, ELB, RDS, VPC, TGW, Route 53, ACM, Serverless computing, Containerization, CloudWatch, CloudTrail, SQS, and SNS. Experience with configuration management tools like Ansible, Chef, or Puppet. Proficiency in Infrastructure as Code (IAC) using Terraform. Strong background in CI/CD pipelines using GitHub Actions and Azure DevOps. Knowledge of MLOps or LLMops practices. Proficient in scripting languages: Python, PowerShell, Bash. Ability to work collaboratively in a fast-paced environment. Preferred Qualifications: Advanced degree in a technical field. Extensive experience with ReactJS and modern web technologies. Proven leadership in agile and project management. Advanced knowledge of CI/CD and industry best practices in software development.

We are looking for a highly motivated Senior DevOps/Mlops engineer specializing in AWS and Kubernetes to join our team Required Candidate profile Exp. in AI OPERATIONS, MLops & Python (MUST). Exp. with deploying secure infrastructure & services in one or more cloud environments such as AWS (must), Azure or GCP Exp. in Kubernetes

Hi, We are hiring for ML Developer. Please find the attached JD. Key Responsibilities Design, develop, and optimize machine learning models for various business applications. Build and maintain scalable AI feature pipelines for efficient data processing and model training. Develop robust data ingestion, transformation, and storage solutions for big data. Implement and optimize ML workflows, ensuring scalability and efficiency. Monitor and maintain deployed models, ensuring performance, reliability, and retraining when necessary Preferred candidate profile Qualifications and Experience Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 3.5 to 5 years of experience in machine learning, deep learning, or data science roles. Proficiency in Python and ML frameworks/tools such as PyTorch, Langchain Experience with data processing frameworks like Spark, Dask, Airflow and Dagster Hands-on experience with cloud platforms (AWS, GCP, Azure) and ML services. Experience with MLOps tools like ML flow, Kubeflow Familiarity with containerisation and orchestration tools like Docker and Kubernetes. Excellent problem-solving skills and ability to work in a fast-paced environment. Strong communication and collaboration skills.

Job Title: Machine Learning Engineer (Computer Vision + GenAI) Location: Bangalore, India (Hybrid) Company: Factspan Analytics Client Industry: Retail / E-commerce About the Role We are looking for a highly skilled Machine Learning Engineer with a strong foundation in Computer Vision , NLP , and GenAI to join our team at Factspan. You'll work closely with a leading retail client to address a critical business challenge: identifying and resolving duplicate product listings across Marketplace and traditional sellers using AI-driven solutions. This role is central to helping the client clean up and scale their product catalog across 30+ categories, each with 10,000+ images. You'll be expected to build and productionize models that can intelligently compare product images and text descriptions to detect duplicates and inconsistencies, leveraging both traditional ML and the latest GenAI techniques. Key Responsibilities Build and deploy scalable Computer Vision pipelines to identify near-duplicate product images across millions of listings. Use Natural Language Processing to match product descriptions and titles with associated images. Leverage LLMs and multimodal GenAI models (e.g., BLIP, CLIP, LLaVA, Gemini, GPT-4V) to solve alignment problems between images and text. Design and implement MLOps and LLMOps pipelines for deploying both traditional ML models and GenAI solutions in production. Perform large-scale image embedding generation , storage, and similarity search using tools like FAISS , Milvus , or pgvector . Use BigQuery and write optimized SQL queries for data preprocessing and analytics. Collaborate with product managers, data engineers, and cloud architects to scale the solution across new product categories. Required Skills 6+ years of hands-on experience in Machine Learning or Data Science roles. Deep expertise in Computer Vision (image deduplication, embedding models, similarity search). Strong understanding of NLP with experience aligning image and text modalities. Exposure to Generative AI/LLMs and how they can be used in multimodal tasks. Experience deploying ML/GenAI models using MLOps/LLMOps best practices (CI/CD, monitoring, versioning). Proficient in Python , TensorFlow/PyTorch , OpenCV , and transformers libraries (e.g., Hugging Face). Familiarity with GCP ecosystem especially BigQuery , Cloud Storage , Vertex AI , Cloud Functions . Strong SQL skills and understanding of data pipelines. Previous experience growing from a Data Scientist role is highly desirable. Good to Have Hands-on with Vector Databases like Milvus, Pinecone, or pgvector. Knowledge of retrieval-augmented generation (RAG) pipelines for GenAI solutions. Familiarity with tools like LangChain , Unstructured.io , or Haystack . Understanding of image compression, image hashing, and perceptual similarity techniques. Why Join Us Work on a high-impact retail AI problem at enterprise scale. Collaborate with a forward-thinking client open to innovation. Join a team where your work directly translates to business value. Flexible hybrid working model in Bangalore with a passionate AI team. Ready to create innovative solutions with computer vision, NLP, and GenAI in retail? Apply now by sending your resume and cover letter to sathishkumar.arumugam@factspan.com.

Job Description: AI/ML Engineer Location: Hyderabad, Telangana, India Experience: 3+ Years About the Team: Join our dynamic and innovative engineering team at Qylis, where we are at the forefront of leveraging Artificial Intelligence and Machine Learning to solve complex challenges and build cutting-edge solutions. We foster a collaborative and growth-oriented environment, encouraging our engineers to explore new technologies and make a significant impact. You will be working alongside talented individuals passionate about pushing the boundaries of AI/ML and delivering impactful products on the Azure cloud platform. Role Overview: We are seeking a highly motivated and experienced AI/ML Engineer to join our team. You will be responsible for designing, developing, and deploying scalable AI/ML models and GenAI applications. Your expertise in Python, web frameworks, machine learning, deep learning, and cloud technologies will be crucial in driving our AI initiatives forward. You will work closely with data scientists, software engineers, and product managers throughout the entire lifecycle of AI/ML projects, from ideation to production deployment and monitoring. Responsibilities: Design, develop, and implement machine learning and deep learning models for various applications, including but not limited to natural language processing, computer vision, predictive analytics, and recommendation systems. Develop and deploy GenAI applications leveraging large language models (LLMs) and other generative techniques. Build and maintain robust and scalable backend systems and APIs using Python frameworks such as FastAPI or Flask to serve AI/ML models and GenAI applications. Containerize applications and services using Docker and orchestrate them using Kubernetes for efficient deployment and scaling on the Azure cloud platform. Utilize Azure cloud services (e.g., Azure Machine Learning, Azure Kubernetes Service, Azure Functions, Azure Blob Storage) to build and deploy AI/ML solutions. Collaborate with data scientists to understand data requirements, perform feature engineering, and evaluate model performance. Work closely with software engineers to integrate AI/ML models and GenAI applications into existing and new products and services. Implement and maintain CI/CD pipelines for automated building, testing, and deployment of AI/ML solutions. Monitor the performance and stability of deployed models and applications, troubleshoot issues, and implement necessary optimizations. Stay up-to-date with the latest advancements in AI/ML, GenAI, and cloud technologies and evaluate their potential application within the company. Contribute to the development of best practices and standards for AI/ML development and deployment within the team. Participate in code reviews and contribute to improving the overall code quality and maintainability. Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Minimum of 3 years of professional experience in developing and deploying AI/ML solutions. Strong proficiency in Python and relevant libraries such as TensorFlow, PyTorch, scikit-learn, and Transformers. Hands-on experience with at least one Python web framework (FastAPI or Flask) for building APIs and backend services. Solid understanding of machine learning algorithms, deep learning architectures, and GenAI concepts. Experience with containerization technologies (Docker) and orchestration platforms (Kubernetes). Proven experience working with the Azure cloud platform and its AI/ML and container services. Familiarity with data engineering concepts and tools for data processing and preparation. Experience with CI/CD pipelines and DevOps practices. Strong problem-solving and analytical skills. Excellent communication and collaboration skills. Ability to work independently and as part of a team. Preferred Qualifications: Experience with MLOps practices and tools for managing the ML lifecycle. Familiarity with other cloud platforms (e.g., AWS, GCP). Experience with specific AI/ML application domains (e.g., NLP, computer vision, time series analysis). Contributions to open-source AI/ML projects. Relevant certifications in AI/ML or cloud technologies.

MLE/Sr. MLE Chennai, Bangalore, Hyderabad Who we are Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow. Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence. We are a Great Place to Work-Certified (2022-25), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the Best and Fastest Growing analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine. Curious about the role? What your typical day would look like? We are looking for a Machine Learning Engineer/Sr MLE 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 Engage with clients to understand their business context. Translate business problems and technical constraints into technical requirements for the desired analytics solution. Collaborate with a team of data scientists and engineers to embed AI and analytics into the business decision processes. What do we expect? 6+ years of experience with at least 4+ years of relevant MLOps experience. Proficient in a structured Python (Mandate) Proficient in Azure Databricks Follows good software engineering practices and has an interest in building reliable and robust software. Good understanding of DS concepts and DS model lifecycle. Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/ PySpark is desirable. Model deployment / model monitoring experience is desirable. CI/CD pipeline creation is good to have. Excellent written and verbal communication skills. B.Tech from Tier-1 college / M.S or M. Tech is preferred. You are important to us, lets stay connected! Every individual comes with a different set of skills and qualities so even if you dont tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow. We are an equal-opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire. Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry. Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities.

Netwoven seeks a highly skilled and innovative Senior Architect - AI/ML to lead our AI/ML initiatives and strengthen our Centre of Excellence (CoE). As a key member of the leadership team, you will drive innovation in AI/ML solutions, provide thought leadership, and support our delivery teams in executing groundbreaking projects. You will play a crucial role in developing and documenting AI/ML use cases across various industries and collaborating with our Sales team to present these solutions to prospective and existing customers. Key Responsibilities: Lead Innovation in AI/ML Solutions: Spearhead efforts to design and develop innovative AI/ML-driven solutions for our clients across various industries. Strengthen CoE: Strengthen and mentor the existing AI/ML CoE team, fostering a culture of innovation and technical excellence. Solution Design & Development: Collaborate closely with delivery teams, offering ground-level design and technical support to ensure seamless solution deployment. Thought Leadership: Provide industry thought leadership in the AI/ML space, researching new technologies, trends, and best practices. Document Use Cases: Develop and document AI/ML solution ideas for industry-specific use cases, ensuring clarity and practicality. Sales Collaboration: Work with the Sales team to present AI/ML use cases and solutions to both prospective and existing customers, helping drive business development. Stakeholder Engagement: Serve as a trusted advisor to internal and external stakeholders, ensuring alignment of AI/ML initiatives with business goals. Required Skills and Experience: 15+ years of experience in software design and development, with a minimum of 4+ years dedicated to AI/ML technologies in a senior or leadership role in solution design and architecture. Proven experience in designing, developing, and deploying AI/ML models and solutions in real-world scenarios across various industries. Strong knowledge of machine learning frameworks (TensorFlow, PyTorch, etc.), NLP, deep learning, and computer vision technologies. Expertise in Generative AI and Large Language Models (LLMs) with hands-on experience in developing and fine-tuning models for real-world applications. Experience in data analysis, data security, and ensuring privacy in AI/ML solutions, with a strong understanding of the regulatory landscape. Solid understanding of industry use cases and trends in AI/ML, with the ability to identify new opportunities for AI-driven innovation. Experience in leading innovation teams and mentoring technical talent in AI/ML domains. • Excellent communication skills with the ability to clearly articulate complex technical concepts and solutions to both technical and non-technical audiences. Proven track record of working with sales teams to develop customer-focused AI/ML solutions and present them effectively to customers. Proficient in cloud platforms (AWS, Azure, GCP) for AI/ML deployment and scaling. Strong problem-solving abilities, with a passion for using AI/ML to solve complex business challenges. Education: Bachelors degree in computer science, Data Science, Artificial Intelligence, or a related field. A master's or Ph.D. in AI/ML or related domains is highly preferred Preferred : Bachelors degree in computer science, Data Science, Artificial Intelligence, or a related field. Experience working in a consulting or customer-facing role A master's or Ph.D. in AI/ML or related domains is highly preferred Certifications in AI/ML technologies or cloud platforms (AWS/Azure/GCP). Experience with MLOps and implementing AI/ML pipelines in production environments. Experience with AI-driven innovation in verticals such as manufacturing, retail, healthcare, etc

We are seeking a talented and motivated AI Engineers to join our dynamic team and contribute to the development of next-generation AI/GenAI based products and solutions. This role will provide you with the opportunity to work on cutting-edge SaaS technologies and impactful projects that are used by enterprises and users worldwide. As a Senior Software Engineer, you will be involved in the design, development, testing, deployment, and maintenance of software solutions. You will work in a collaborative environment, contributing to the technical foundation behind our flagship products and services. Responsibilities: Software Development: Write clean, maintainable, and efficient code or various software applications and systems. GenAI Product Development: Participate in the entire AI development lifecycle, including data collection, preprocessing, model training, evaluation, and deployment.Assist in researching and experimenting with state-of-the-art generative AI techniques to improve model performance and capabilities. Design and Architecture: Participate in design reviews with peers and stakeholders Code Review: Review code developed by other developers, providing feedback adhering to industry standard best practices like coding guidelines Testing: Build testable software, define tests, participate in the testing process, automate tests using tools (e.g., Junit, Selenium) and Design Patterns leveraging the test automation pyramid as the guide. Debugging and Troubleshooting: Triage defects or customer reported issues, debug and resolve in a timely and efficient manner. Service Health and Quality: Contribute to health and quality of services and incidents, promptly identifying and escalating issues. Collaborate with the team in utilizing service health indicators and telemetry for action. Assist in conducting root cause analysis and implementing measures to prevent future recurrences. Dev Ops Model: Understanding of working in a DevOps Model. Begin to take ownership of working with product management on requirements to design, develop, test, deploy and maintain the software in production. Documentation: Properly document new features, enhancements or fixes to the product, and also contribute to training materials. Basic Qualifications: Bachelors degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. 2+ years of professional software development experience. Proficiency as a developer using Python, FastAPI, PyTest, Celery and other Python frameworks. Experience with software development practices and design patterns. Familiarity with version control systems like Git GitHub and bug/work tracking systems like JIRA. Basic understanding of cloud technologies and DevOps principles. Strong analytical and problem-solving skills, with a proven track record of building and shipping successful software products and services. Preferred Qualifications: Experience with object-oriented programming, concurrency, design patterns, and REST APIs. Experience with CI/CD tooling such as Terraform and GitHub Actions. High level familiarity with AI/ML, GenAI, and MLOps concepts. Familiarity with frameworks like LangChain and LangGraph. Experience with SQL and NoSQL databases such as MongoDB, MSSQL, or Postgres. Experience with testing tools such as PyTest, PyMock, xUnit, mocking frameworks, etc. Experience with GCP technologies such as VertexAI, BigQuery, GKE, GCS, DataFlow, and Kubeflow. Experience with Docker and Kubernetes. Experience with Java and Scala a plus.