524 Mlops Jobs - Page 16

Looking for immediate to 30days 4years to 6years Location - Bangalore, Hyderabad & Chennai We are looking for a good Python Developer responsible for managing DEVOPS and infrastructure for AIML space Your primary focus will be setting the tech-stack and dependency software deployment, GPU infra configuration and commissioning, build container applications Build and Automate MDLC lifecycle using ADO Manage the BAU and operational issues and queries to ensure the component deployment is stable and operational in lower and production environment Managed and setup alerting across environments Monitor application performance and look for opportunities to improve by infra tuning and scaling. Investigate and close security issues and observation during pipeline promotion Good knowledge in Jenkins Azure Dev ops or CI/CD Vx-Pipeline In-depth knowledge in configuration and automation tools like RunDeck and Ansible Good scripting knowledge using Python, Groovy, Shell

Seeking an experienced AI Architect to lead the development of our AI and Machine Learning infrastructure and specialized language models. This role will establish and lead our MLOps practices and drive the creation of scalable, production-ready AI/ML systems. Key Responsibilities Discuss the feasibility of AI/ML use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings Design and implement robust ML infrastructure and deployment pipelines Establish comprehensive MLOps practices for model training, versioning, and deployment Lead the development of HR-specialized language models (SLMs) Implement model monitoring, observability, and performance optimization frameworks Develop and execute fine-tuning strategies for large language models Create and maintain data quality assessment and validation processes Design model versioning systems and A/B testing frameworks Define technical standards and best practices for AI development Optimize infrastructure for cost, performance, and scalability Required Qualifications 7+ years of experience in ML/AI engineering or related technical roles 3+ years of hands-on experience with MLOps and production ML systems Demonstrated expertise in fine-tuning and adapting foundation models Strong knowledge of model serving infrastructure and orchestration Proficiency with MLOps tools (MLflow, Kubeflow, Weights & Biases, etc.) Experience implementing model versioning and A/B testing frameworks Strong background in data quality methodologies for ML training Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience with cloud-based ML platforms (AWS, Azure, Google Cloud) Proven track record of deploying ML models at scale Preferred Qualifications Experience developing AI applications for enterprise software domains Knowledge of distributed training techniques and infrastructure Experience with retrieval-augmented generation (RAG) systems Familiarity with vector databases (Pinecone, Weaviate, Milvus) Understanding of responsible AI practices and bias mitigation Bachelor's or Master's degree in Computer Science, Machine Learning, or related field

Greetings from Futurea4! We are looking for a passionate and experienced Senior Data Scientist to drive innovation in the world of Gen AI, NLP, LLMs , and MLOps . Position : Senior Data Scientist Gen AI | NLP | LLM | MLOps Locations : Bangalore | Pune | Noida | Gurgaon | Hyderabad Experience : 6 to15 Years Joiners Preferred : Immediate or Short Notice Key Responsibilities Design and implement advanced NLP and Gen AI models using cutting-edge LLMs (e.g., GPT, LLaMA, Mistral). Fine-tune and customize LLMs through LoRA , PEFT , and prompt engineering techniques. Develop scalable ML pipelines and ensure smooth deployment via MLOps best practices. Translate business challenges into impactful AI-powered solutions . Monitor and improve model performance for accuracy and scalability. Create documentation and foster cross-functional knowledge sharing. Required Skills Strong hands-on experience in Generative AI , NLP , and LLM development (using frameworks like Hugging Face, spaCy, LangChain). Expert knowledge of Python , TensorFlow/PyTorch , and the ML lifecycle. Deep MLOps know-how: CI/CD, model monitoring, versioning, and deployment workflows. Cloud-native development experience (AWS, Azure, or GCP) and container tools ( Docker , Kubernetes ). Experience with large-scale data handling and data engineering principles. Nice to Have Familiarity with RAG pipelines , vector databases , and advanced prompt tuning. Exposure to multi-modal architectures , transformer models , and open-source LLMs . Publications or community involvement in AI research or development. Apply Today If you're eager to build the future of AI and ready to take on exciting challenges, we want to hear from you.

Role Responsibilities Design and develop GenAI applications utilizing Python. Implement front-end solutions using Angular or React frameworks. Apply NLP techniques to enhance application functionalities. Optimize machine learning models for performance and scalability. Establish MLOps practices to streamline model deployment. Create and integrate APIs for seamless functionality between applications. Collaborate with cross-functional teams to define project requirements. Conduct code reviews to ensure adherence to best practices. Debug and troubleshoot applications to enhance user experience. Stay updated with industry trends and advancements in GenAI. Document project specifications and technological processes. Participate in regular team meetings to align on goals and challenges. Assist in training team members on GenAI technologies. Maintain version control for project codes and documentation. Provide technical support during product launches. Qualifications Bachelor's or Master's degree in Computer Science or related field. Proven experience in GenAI development. Strong proficiency in Python programming language. Experience with front-end frameworks such as Angular and React. Hands-on expertise in Natural Language Processing (NLP). Familiarity with machine learning algorithms and models. Understanding of MLOps tools and practices. Experience in API development and integration. Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. Ability to work independently as well as in a team environment. Attention to detail with a focus on quality outcomes. Familiarity with database systems and management. Experience with version control systems like Git. Knowledge of agile methodologies is a plus.

Responsibilities : Manipulate and preprocess structured and unstructured data to prepare datasets for analysis and model training. Utilize Python libraries like PyTorch, Pandas, and NumPy for data analysis, model development, and implementation. Fine-tune large language models (LLMs) to meet specific use cases and enterprise requirements. Collaborate with cross-functional teams to experiment with AI/ML models and iterate quickly on prototypes. Optimize workflows to ensure fast experimentation and deployment of models to production environments. Implement containerization and basic Docker workflows to streamline deployment processes. Write clean, efficient, and production-ready Python code for scalable AI solutions. Good to Have: Exposure to cloud platforms like AWS, Azure, or GCP. Knowledge of MLOps principles and tools. Basic understanding of enterprise Knowledge Management Systems. Ability to work against tight deadlines. Ability to work on unstructured projects independently. Strong initiative and self-motivated Strong Communication & Collaboration acumen. Required Skills: Proficiency in Python with strong skills in libraries like PyTorch, Pandas, and NumPy. Experience in handling both structured and unstructured datasets. Familiarity with fine-tuning LLMs and understanding of modern NLP techniques. Basics of Docker and containerization principles. Demonstrated ability to experiment, iterate, and deploy code rapidly in a production setting. Strong problem-solving mindset with attention to detail. Ability to learn and adapt quickly in a fast-paced, dynamic environment.

Technical Staff, Software Engineering From applied research to advanced engineering, the CTO Storage team has the expertise to shape ground-breaking Storage products, technologies, and innovations. Its a fascinating field of work. Were involved in assessing the competition, developing Storage technology and product strategies and generating IP. We lead technology investigations, analyze industry capabilities and recommend potential acquisitions or vendor partner opportunities. Our insights influence product architecture and definitions. And we work with colleagues across the business to ensure our products always lead the way. Join us to do the best work of your career and make a profound social impact as a Technical Staff Software Engineer on our CTO Storage Team in Bangalore, India. What youll achieve As a Technical Staff Software Engineering you will be responsible for Storage architecture for data path technologies across our portfolio. You will work with product teams and product management to craft and deliver the best strategy for Dell. You will: Present innovative technical storage ideas at the executive level and develop innovative ideas for Dells storage solutions by keeping abreast of the latest advancements in AI infrastructure, workload orchestration, MLOps platforms, and GenAI applications Architect detailed product designs, specifying functionality, performance, integrations, and hardware requirements Lead prototyping and testing to validate design and functionality, focusing on performance and scalability Refine designs based on feedback, testing, and evolving requirements Work with cross-functional teams, partners, and customers to create POCs and MVPs. Ensure seamless integration of components and technologies Essential Requirements 15+ years of related experience with a bachelors degree; or 12+ years with a masters degree; or 8+ years with a PhD; or equivalent experience Proficiency in AI-driven operations, telemetry systems, and data integration, transformation, and storage connectivity and protocols. Strong experience in designing, developing, and maintaining resilient software infrastructure, and SaaS platforms and Strong experience in development of software systems using C/JAVA/Python Expertise in data migration, replication, synchronization, and familiarity with backup software solutions In-depth knowledge of storage management systems and APIs , including configuration, monitoring, and optimization. Desirable Requirements Possess advanced certifications or have published work in Storage Software Solutions, AI, deep learning, or related fields, with a track record of leading high-impact projects or initiatives in innovative tech domains. Actively contribute to open-source projects or participate in tech communities. Demonstrate a proven ability to mentor and uplift junior team members or peers, with a history of using unconventional thinking to achieve breakthrough solutions. Application closing date: 30 June 2025

Role & Responsibilities: Design, develop, and maintain robust AWS cloud infrastructure to support large-scale applications. Architect and implement scalable solutions using AWS services such as EC2, S3, Lambda, RDS, IAM, VPC, CloudFormation, and more. MLOps: Implement and manage MLOps pipelines to streamline the deployment and monitoring of machine learning models. Automate model training, validation, deployment, and monitoring processes to ensure efficiency and reliability. Large Language Models (LLM) Utilize expertise in LLM to enhance our AI-driven solutions, including fine-tuning and optimizing pre-trained models. DevOps Capabilities: Develop and maintain CI/CD pipelines using tools such as Jenkins, GitLab CI, CircleCI, or AWS CodePipeline. Implement infrastructure as code (IaC) practices using Terraform, CloudFormation, or AWS CDK. Ensure the highest levels of security, scalability, and performance in all cloud deployments. Monitor and manage the health, performance, and security of cloud environments using AWS CloudWatch, ELK stack (Elasticsearch, Logstash, Kibana), or Prometheus & Grafana. Mentor and guide junior engineers, fostering a culture of continuous learning and improvement. Required Skills: AWS services such as EC2, S3, Lambda, RDS, IAM, VPC, CloudFormation Implement and manage MLOps pipelines Large Language Models (LLM) CI/CD pipelines Terraform

Strong understating of Python, ML concepts and frameworks, Fast API, Graph QL Experience in developing scalable APIs. Knowledge of AWS, preferred services are storage, EC2, Kubernetes Exposure of ML best practices, documentation and unit testing. ML Flow, AirFlow, ML pipeline creation, drift monitoring and control Experience in developing and deploying machine learning models in a production environment using CI/CD. Communicate with clients to understand requirements and ask right questions. Knowledge of Django and database design will be added advantage. Strong analytical and problem-solving skills. Standards : Model Deployment Standards Use standardized APIs (e.g., RESTful) to interface with models Implement model versioning and proper naming conventions Monitoring and Maintenance Schedule routine model retraining and monitoring Code Quality Standards Follow style guides (e.g., PEP 8 in Python) Write comprehensive debugging and tests

Job Summary: We are seeking a talented and driven Machine Learning Engineer with 2-5 years of experience to join our dynamic team in Chennai. The ideal candidate will have a strong foundation in machine learning principles and extensive hands-on experience in building, deploying, and managing ML models in production environments. A key focus of this role will be on MLOps practices and orchestration, ensuring our ML pipelines are robust, scalable, and automated. Key Responsibilities: ML Model Deployment & Management: Design, develop, and implement end-to-end MLOps pipelines for deploying, monitoring, and managing machine learning models in production. Orchestration: Utilize orchestration tools (e.g., Apache Airflow, Kubeflow, AWS Step Functions, Azure Data Factory) to automate ML workflows, including data ingestion, feature engineering, model training, validation, and deployment. CI/CD for ML: Implement Continuous Integration/Continuous Deployment (CI/CD) practices for ML code, models, and infrastructure, ensuring rapid and reliable releases. Monitoring & Alerting: Establish comprehensive monitoring and alerting systems for deployed ML models to track performance, detect data drift, model drift, and ensure operational health. Infrastructure as Code (IaC): Work with IaC tools (e.g., Terraform, CloudFormation) to manage and provision cloud resources required for ML workflows. Containerization: Leverage containerization technologies (Docker, Kubernetes) for packaging and deploying ML models and their dependencies. Collaboration: Collaborate closely with Data Scientists, Data Engineers, and Software Developers to translate research prototypes into production-ready ML solutions. Performance Optimization: Optimize ML model inference and training performance, focusing on efficiency, scalability, and cost-effectiveness. Troubleshooting & Debugging: Troubleshoot and debug issues across the entire ML lifecycle, from data pipelines to model serving. Documentation: Create and maintain clear technical documentation for MLOps processes, pipelines, and infrastructure. Required Skills & Qualifications: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field. 2-5 years of professional experience as a Machine Learning Engineer, MLOps Engineer, or a similar role. Strong proficiency in Python and its ML ecosystem (e.g., scikit-learn, TensorFlow, PyTorch, Pandas, NumPy). Hands-on experience with at least one major cloud platform (AWS, Azure, GCP) and their relevant ML/MLOps services (e.g., AWS SageMaker, Azure ML, GCP Vertex AI). Proven experience with orchestration tools like Apache Airflow, Kubeflow, or similar. Solid understanding and practical experience with MLOps principles and best practices. Experience with containerization technologies (Docker, Kubernetes). Familiarity with CI/CD pipelines and tools (e.g., GitLab CI/CD, Jenkins, Azure DevOps, AWS CodePipeline). Knowledge of database systems (SQL and NoSQL). Excellent problem-solving, analytical, and debugging skills. Strong communication and collaboration abilities, with a capacity to work effectively in an Agile environment.

Purpose Of The Position: We are looking for a highly experienced Senior Data Scientist with a deep understanding of AI/ML technologies, including Recommendation Systems, Chatbots, Generative AI, and Large Language Models (LLMs). The ideal candidate will have hands-on experience in applying these technologies to solve real-world problems, working with large datasets, and collaborating with cross-functional teams to deliver innovative data-driven solutions.. Key Responsibilities : Design, develop, and optimize recommendation systems to enhance user experience and engagement across platforms. Build and deploy chatbots with advanced NLP capabilities for automating customer interactions and improving business processes. Lead the development of Generative AI solutions, including content generation and automation. Research and apply Large Language Models (LLMs) like GPT, BERT, and others to solve business-specific problems and create innovative solutions. Collaborate with engineering teams to integrate machine learning models into production systems, ensuring scalability and reliability. Perform data exploration, analysis, and feature engineering to improve model performance. Stay updated on the latest advancements in AI and ML technologies, proposing new techniques and tools to enhance our product capabilities. Mentor junior data scientists and engineers, providing guidance on best practices in AI/ML model development and deployment. Collaborate with product managers and business stakeholders to translate business goals into AI-driven solutions. Work on model interpretability, explainability, and ensure models are built in an ethical and responsible manner. Required Skills And Qualifications: 5+ years of experience in data science or machine learning, with a focus on building and deploying AI models. Strong expertise in designing and developing recommendation systems and working with collaborative filtering, matrix factorization, and content-based filtering techniques. Hands-on experience with chatbots using Natural Language Processing (NLP) and conversational AI frameworks. In-depth understanding of Generative AI, including transformer-based models and GANs (Generative Adversarial Networks). Experience working with Large Language Models (LLMs) such as GPT, BERT, T5, etc. Proficiency in machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn. Strong programming skills in Python and libraries such as NumPy, Pandas, Hugging Face, and NLTK. Experience with cloud platforms like AWS, GCP, or Azure for deploying and scaling machine learning models. Solid understanding of data pipelines, ETL processes, and working with large datasets using SQL or NoSQL databases. Knowledge of MLOps and experience deploying models in production environments. Strong problem-solving skills and a deep understanding of statistical methods and algorithms. Preferred Qualifications: Experience with Reinforcement Learning and Recommender Systems personalization techniques. Experience of working with AWS Bedrock services. Familiarity with ethical AI and model bias mitigation techniques. Experience with A/B testing, experimentation, and performance tracking for AI models in production. Prior experience mentoring junior data scientists and leading AI/ML projects. Strong communication and collaboration skills, with the ability to convey complex technical concepts to non-technical stakeholders. Why Join Us? Opportunity to be part of a rapidly growing, innovative product-based company. Collaborate with a talented, driven team focused on building high-quality software solutions. Competitive compensation and benefits package.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Job Summary: We are looking for a talented MLOps Engineer with 4-7yrs to help design, build, and manage scalable infrastructure for deploying AI/ML and Generative AI models into production. You will be responsible for implementing and maintaining robust ML pipelines, CI/CD workfl ows, containerized deployments, and model monitoring systems. The ideal candidate will have strong experience in cloud-native MLOps practices, especially on GCP (preferred), and a solid understanding of modern machine learning workfl ows and tools. Roles & Responsibilities: ML Pipelines & Automation Develop and manage end-to-end ML pipelines for data processing, model training, testing, and deployment. Automate model lifecycle using tools like MLfl ow, Kubefl ow, Airfl ow, or Vertex AI Pipelines. Model Deployment & Infrastructure Package and deploy models using Docker, Kubernetes, and cloud-native platforms like GCP Vertex AI, Cloud Run, or SageMaker. Implement CI/CD pipelines using tools such as GitHub Actions, Cloud Build, or Jenkins for continuous model integration and delivery. Monitoring & Performance Optimization Set up monitoring systems for model drift, latency, accuracy, and resource utilization. Implement logging, alerting, and observability using tools like Prometheus, Grafana, or Cloud Logging. Collaboration & Support Work closely with AI/ML Architects, Data Scientists, and Software Engineers to ensure reproducibility, scalability, and reliability of AI solutions. Support deployment of GenAI models and components (e.g., RAG pipelines, LLMs, embedding services). Security, Governance & Compliance Ensure secure and compliant handling of data and model artifacts. Manage model versioning, lineage tracking, and audit logging in accordance with internal policies. Required Skills & Qualifications: 4-7 years of experience in MLOps, DevOps, or ML Engineering roles. Strong programming skills in Python and scripting for automation. Hands-on with MLOps tools: MLfl ow, DVC, TFX, or Kubefl ow. Experience with cloud platforms: GCP (Vertex AI, Cloud Build, Artifact Registry), AWS (SageMaker, Lambda), or Azure ML. Profi ciency with containerization (Docker) and orchestration platforms (Kubernetes, Cloud Run). Solid experience in setting up and managing CI/CD pipelines for machine learning workflows. Familiarity with data pipelines, ETL tools (Airfl ow, Datafl ow), and data validation tools.

Job Summary: We are looking for a highly capable and automation-driven MLOps Engineer with 2+ years of experience in building and managing end-to-end ML infrastructure. This role focuses on operationalizing ML pipelines using tools like DVC, MLflow, Kubeflow, and Airflow, while ensuring efficient deployment, versioning, and monitoring of machine learning and Generative AI models across GPU-based cloud infrastructure (AWS/GCP). The ideal candidate will also have experience in multi-modal orchestration, model drift detection, and CI/CD for ML systems. Key Responsibilities: Develop, automate, and maintain scalable ML pipelines using tools such as Kubeflow, MLflow, Airflow, and DVC. Set up and manage CI/CD pipelines tailored to ML workflows, ensuring reliable model training, testing, and deployment. Containerize ML services using Docker and orchestrate them using Kubernetes in both development and production environments. Manage GPU infrastructure and cloud-based deployments (AWS, GCP) for high-performance training and inference. Integrate Hugging Face models and multi-modal AI systems into robust deployment frameworks. Monitor deployed models for drift, performance degradation, and inference bottlenecks, enabling continuous feedback and retraining. Ensure proper model versioning, lineage, and reproducibility for audit and compliance. Collaborate with data scientists, ML engineers, and DevOps teams to build reliable and efficient MLOps systems. Support Generative AI model deployment with scalable architecture and automation-first practices. Qualifications: 2+ years of experience in MLOps, DevOps for ML, or Machine Learning Engineering. Hands-on experience with MLflow, DVC, Kubeflow, Airflow, and CI/CD tools for ML. Proficiency in containerization and orchestration using Docker and Kubernetes. Experience with GPU infrastructure, including setup, scaling, and cost optimization on AWS or GCP. Familiarity with model monitoring, drift detection, and production-grade deployment pipelines. Good understanding of model lifecycle management, reproducibility, and compliance. Preferred Qualifications : Experience deploying Generative AI or multi-modal models in production. Knowledge of Hugging Face Transformers, model quantization, and resource-efficient inference. Familiarity with MLOps frameworks and observability stacks. Experience with security, governance, and compliance in ML environments. Location-Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Mandatory Skills : Python, Machine Learning Models, Flask, Data Visualization Tools. Good to have : Gen AI, NLP, Cloud (AWS) etc Notice Period : Immediate - 30 days Relevant Experience : 5+ Years in Machine Learning Responsibilities : - Design, develop, and deploy machine learning models and algorithms. - Perform data analysis to extract insights and identify patterns. - Develop and implement feature engineering processes to enhance model performance. - Collaborate with data scientists and cross-functional teams to understand business requirements and translate them into technical solutions. - Build and maintain scalable data pipelines and infrastructure. - Develop and deploy machine learning applications using Flask, FastAPI, or Django. - Conduct experiments, model tuning, and evaluate model performance using appropriate metrics. Must-Have Skills : - Python and its libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch). - Experience with web frameworks such as Flask, FastAPI, or Django. - Strong understanding of machine learning algorithms and techniques. - Experience with data visualization tools (e.g., Matplotlib, Seaborn). - Strong problem-solving skills and ability to work independently as well as in a team. - Excellent communication skills to effectively convey technical concepts to non-technical stakeholders. Good-to-Have Skills : - Experience with cloud platforms, particularly AWS. - Knowledge of MLOps practices and tools. - Experience with Natural Language Processing (NLP) techniques. - Exposure to Generative AI (GenAI) technologies.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Dear Candidate, We have an urgent opening with one of Multinational Company for Below Locations. Interested candidate can share the resume on Deepaksharma@thehrsolutions.in OR WhatsApp on 8882505093 Experience : 9+ Years Profile : ML Engineer (Advanced AI/ML + Python) Locations : Hyderabad, Bangalore, Mumbai, Kolkata Notice period : Only immediate joiners OR Already serving. Job Description - ML Engineer (Advanced AI/ML + Python) This role leverages machine learning & advanced AI services to generate business insights 8+ Years of relevant experience in Machine learning & Deep learning (GenAI-optional) experience Experience in deploying NLP, CV & Deep learning algorithms Experience in leading teams and managing the end-to-end deliverables. Exposure to MLOps implementations Leadership abilities to guide and mentor junior team members. Design and implement scalable and reliable ML pipelines for model training, evaluation and deployment Advanced degree in Data Science, Statistics, Computer Science, or a related field. Proficiency in Python, Git, Github,Pyspark, Pytorch, , SQL and strong expertise of AI/ML algorithms is essential with knowledge of AWS cloud ML services. Strong analytical skills & communication skills. Ability to interpret complex data sets. Responsibilities- Lead the development and implementation of predictive models and machine learning algorithms. Design and implement scalable and reliable ML pipelines for model training, evaluation and deployment Collaborate with CMA CGM cross-functional teams to understand business needs and devise possible solutions. Present and visualize data insights to stakeholders in a comprehensible manner.