35 Ml Pipelines Jobs - Page 2

Location: Bangalore / Hybrid Department: Data & AI Company: Resolve Tech Solutions / Juno Labs About Juno Labs: Juno Labs is at the forefront of AI-driven cloud solutions, helping businesses unlock the power of data with scalable, intelligent, and high-performance architectures. We specialize in building next-gen data platforms, leveraging cloud technologies, AI/ML, vector databases, and advanced frameworks to drive real-time insights and intelligent decision-making. Job Description: We are looking for an experienced MLOps Engineer to join our Data & AI team. This role will focus on building, deploying, and optimizing end-to-end machine learning systems with an emphasis on LLMOps (Large Language Models operationalization). The ideal candidate will have strong expertise in MLOps , LLMOps , and DevOps , with hands-on experience managing and deploying large-scale models, particularly LLMs , in both cloud and on-premise environments. The role involves not only building robust MLOps pipelines but also self-hosting models, optimizing GPU usage, and performing quantization to reduce the cost of deployment. Key Responsibilities: Design and implement scalable MLOps pipelines to deploy, monitor, and manage machine learning models, with a particular focus on LLMOps . Integrate, fine-tune, and optimize Hugging Face models (e.g., Transformers , BART , GPT-2/3 ) for diverse NLP tasks such as text generation , text classification , and NER , and deploy them for production-scale systems. Use LangChain to build sophisticated LLM-driven applications , enabling seamless model workflows for NLP and decision-making tasks. Optimize and manage LLMOps pipelines for large-scale models using technologies such as OpenAI API , Amazon Bedrock , DeepSpeed , and Hugging Face Hub . Develop and scale self-hosted LLM solutions (e.g., fine-tuning and serving models on-premises or in a hybrid cloud environment) to meet performance, reliability, and cost-effectiveness goals. Leverage cloud-native tools such as Amazon SageMaker , Vertex AI , GCP , AWS for scaling large language models, and ensure their optimization in distributed cloud environments. Use GPU-based optimization for large-scale model training and deployment, ensuring high performance and efficient resource allocation in the cloud or on- premises environments. Deploy models via containerized solutions using Docker , Kubernetes , and Helm , allowing for seamless scaling and management in both cloud and on- premise infrastructures. Implement model quantization and pruning techniques to reduce the resource footprint of deployed models while maintaining high performance. Monitor model performance in production using Prometheus , Grafana , ELK Stack , and other observability tools to track metrics such as inference latency, accuracy, and throughput. Automate the end-to-end workflow of model development and deployment via CI/CD pipelines with tools like GitLab CI , Jenkins , and CircleCI . Integrate vector databases (e.g., Pinecone , FAISS , Milvus ) for efficient storage, retrieval, and querying of model-generated embeddings. Stay up to date with the latest advancements in MLOps , LLMOps , and machine learning technologies, ensuring the adoption of best practices in model development, deployment, and optimization. Required Skills & Qualifications: Bachelors or Masters degree in Computer Science, Engineering, or a related field. 5+ years of experience in MLOps , LLMOps , DevOps , or related roles, with a focus on deploying and managing machine learning models in production environments. Experience with cloud platforms such as AWS , GCP , Azure , and services like Amazon SageMaker , Vertex AI , TensorFlow Serving , DeepSpeed , and Amazon Bedrock . Expertise in Hugging Face models and the Transformers library, including model fine-tuning , deployment , and optimizing NLP models for large-scale production. Experience with LangChain for building and deploying LLM-based applications that handle dynamic and real-time tasks. Strong experience with self-hosting LLMs in cloud or on-premises environments using GPU-based infrastructure for training and inference (e.g., NVIDIA GPUs , CUDA ). Expertise in GPU utilization and optimization for large-scale model training, inference, and cost-effective deployment. Hands-on experience in model quantization techniques to reduce the memory footprint and inference time, such as TensorFlow Lite , ONNX , or DeepSpeed . Familiarity with distributed ML frameworks like Kubeflow , Ray , Dask , MLflow , for managing end-to-end ML workflows and large-scale model training and evaluation. Proficiency with containerization and orchestration tools such as Kubernetes , Docker , Helm , and Terraform for infrastructure automation. Knowledge of vector databases like Pinecone , Milvus , or FAISS to facilitate fast and scalable retrieval of model-generated embeddings. Expertise in setting up and managing CI/CD pipelines for model training, validation, testing, and deployment with tools like Jenkins , GitLab CI , and CircleCI . Strong programming skills in Python , Bash , and Shell scripting . Solid understanding of monitoring and logging tools such as Prometheus , Grafana , and ELK Stack to ensure high system performance, error detection, and model health tracking. Preferred Qualifications: Proven experience in deploying and managing large-scale LLMs like GPT-3 , BERT , T5 , and BLOOM in production environments using cloud-native solutions and on-premises hosting. Deep expertise in quantization , model compression , and pruning to optimize deployed models for lower latency and reduced resource consumption. Strong understanding of NLP tasks and deep learning concepts such as transformers, attention mechanisms, and pretrained model fine-tuning. Experience with Kedro for building reproducible ML pipelines with a focus on data engineering, workflow orchestration, and modularity. Familiarity with Apache Spark and Hadoop for handling big data processing needs, especially in real-time AI workloads . Familiarity with advanced data engineering pipelines and data lakes for the effective management of large datasets required for training LLMs. Why Join Us: Work with cutting-edge technologies in AI , MLOps , and LLMOps , including self- hosting and optimizing large-scale language models. Be part of an innovative, fast-growing team working on the future of AI-driven cloud solutions. Flexibility in work style with a hybrid work environment that promotes work-life balance. Competitive salary and benefits package, with opportunities for personal and professional growth.

Why Ryan? Global Award-Winning Culture Flexible Work Environment Generous Paid Time Off World-Class Benefits and Compensation Rapid Growth Opportunities Company Sponsored Two-Way Transportation Exponential Career Growth The Data Engineer, Data Engineering is responsible for the identifying, developing, and maintaining the technologies that enable the efficient flow of data throughout the organization. This role requires an enterprise mindset to build out robust, high-performance technology. Duties and Responsibilities, aligned with Key Results: People Use a variety of programming languages and tools to develop, test, and maintain data pipelines within the Platform Reference Architecture. Working directly with management, product teams and practice personnel to understand their platform data requirements Maintaining a positive work atmosphere by behaving and communicating in a manner that encourages productive interactions with customers, co-workers and supervisors Developing and engaging with team members by creating a motivating work environment that recognizes, holds team members accountable, and rewards strong performance Fostering an innovative, inclusive and diverse team environment, promoting positive team culture, encouraging collaboration and self-organization while delivering high quality solutions Client Collaborating on an Agile team to design, develop, test, implement and support highly scalable data solutions Collaborating with product teams and clients to deliver robust cloud-based data solutions that drive tax decisions and provide powerful experiences Analyzing user feedback and activity and iterate to improve the services and user experience Value Securing data in alignment with internal information and data security policies, best practices and client requirements Creating and implementing robust cloud-based data solutions that scale effectively, and provide powerful experiences for both internal teams and clients Performing unit tests and conducting reviews with other team members to make sure solutions and code are rigorously designed, elegantly coded and effectively tuned for performance Staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities and mentoring other members of the engineering community Perform other duties as assigned Education and Experience: Bachelor’s and/or master’s degree in a related field 3+ years of experience developing data technologies. 3+ years of experience deploying ETL solutions in production environments. 3+ years of experience with cloud-based data services, preferably in AWS or Azure. 3+ years of experience developing Python, Scala, Java, .Net or similar solutions in a backend or data wrangling capacity. 3+ years of experience in mixed Windows/Linux environments. Additional Required Skills and Experience: Results-proven track record of exceeding goals and evidence of the ability to consistently make good decisions through a combination of analysis, experience and judgment Fluency in one or more databases, preferably relational and NoSQL is a plus. Experience with distributed data platforms is a plus. Exposure to AI/ML pipelines is preferred. Experience deploying, monitoring, and maintaining data pipelines in production environments Commitment to diversity, accountability, transparency, and ethics. Computer Skills: To perform this job successfully, an individual must have intermediate knowledge of Microsoft Project, Word, Excel, Access, PowerPoint, Outlook, and Internet navigation and research. Supervisory Responsibilities: None Work Environment: Standard indoor working environment. Occasional long periods of sitting while working at computer. Must be able to lift, carry, push or pull up to 30 lbs. Position requires regular interaction with employees at all levels of the Firm and interface with external vendors as necessary. Independent travel requirement: As Needed Equal Opportunity Employer: disability/veteran

Job Title: ML Ops Engineer Location: Bangalore / Chennai / Gurgaon / Kolkata / Pune / Hyderabad Experience: 2-4 Years Job Description: We are looking for a dynamic and experienced ML Ops Engineer to join our team in Bangalore. The ideal candidate will be responsible for building and managing ML systems in production. You will work closely with our Data Scientists, ML Engineers, and IT teams to maintain and improve our machine learning platforms. Responsibilities: 1. Develop, test, validate, and maintain robust tools using cloud-based technologies, Python, PySpark, etc. for machine learning (ML) models. 2. Work closely with data scientists and ML engineers to understand their needs and translate them into reliable, efficient, and scalable production systems. 3. Ensure the successful deployment of ML models, including logging, monitoring, and setting up automated pipelines. 4. Leverage advanced programming skills to create data structures and algorithms. 5. Develop and maintain scalable data pipelines and build out new API integrations to support continuing increases in data volume and complexity. 6. Establish robust logging and versioning systems for our ML models. 7. Use orchestration tools to automate ML pipelines. 8. Implement CI/CD pipelines for ML models. 9. Collaborate with cross-functional teams to understand the requirements and implement solutions. 10. Monitor ML models and system performance, ensuring high availability and reliability. 11. Provide technical expertise and best practices for ML productionization. 12. Proactively identify and resolve issues that may impact ML model performance. Requirements: 1. Bachelor's degree in Computer Science, Engineering, or related field. 2. 2-4 years of experience in a similar role. 3. Strong knowledge of Python and PySpark. Experience with other programming languages is a plus. 4. Extensive experience in building and maintaining production systems. 5. Experience with version control/Git, and CI/CD pipelines. 6. Strong understanding of machine learning models. 7. Proficiency in applying advanced programming algorithms. 8. Experience with orchestration tools. 9. Strong problem-solving skills and attention to detail. 10. Excellent communication and collaboration skills. 11. Ability to work in a fast-paced, dynamic environment. Required Skills MLops

Role :AWS SageMaker Developer Experience :7+ Years Location: Hyderabad Work Mode :WFO Roles &Responsibilities: Extensive experience with Amazon SageMaker, including: Model development and testing Model deployment Model monitoring Key Responsibilities: Develop and test machine learning models using SageMaker, leveraging built-in algorithms and custom frameworks. Deploy models to production using SageMaker endpoints, batch transform, and multi-model endpoints. Implement continuous monitoring of deployed models using SageMaker Model Monitor. Conduct A/B testing and shadow testing for new model versions. Optimize model performance and cost-efficiency using SageMaker features like Spot Training and automated hyperparameter tuning. Collaborate with cross-functional teams to integrate ML models into production applications. Required Skills: Proficiency in Python and popular ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) Strong understanding of ML workflows, from data preparation to model deployment Experience with AWS services related to ML pipelines (e.g., S3, CloudWatch, Lambda) Familiarity with SageMaker Studio and SageMaker notebooks Knowledge of best practices in MLOps and model governance Preferred Qualifications: Experience with SageMaker features such as Autopilot, Feature Store, and Pipelines Familiarity with containerization and Docker Understanding of distributed computing and large-scale data processing Experience with real-time and batch inference scenarios AWS certifications related to machine learning or data science Key Responsibilities : Design and architect end-to-end MDM solutions using the Reltio platform, focusing on banking or Customer 360 use cases. Collaborate with cross-functional teams to align MDM solutions with broader organizational objectives. Provide technical leadership in Reltio technologies, standard methodologies, and data governance practices. Develop and optimize customer relationship profiles using Reltio's multi-model architecture. Required Qualifications: Reltio certification as a Solution Architect. 8+ years of experience in large-scale program execution, with at least 3 years leading Customer 360 or similar enterprise-wide initiatives. Strong understanding of the banking domain or extensive Customer 360 experience. Expertise in data modeling, data quality management, and data governance. Proficiency in cloud platforms (AWS, Azure, Google Cloud) for hosting MDM solutions. Experience with ETL concepts, data integration frameworks, and workflow automation. Preferred Skills: Familiarity with other MDM platforms. Understanding of regulatory requirements in the banking sector. Key Competencies : Strong problem-solving and analytical skills. Excellent communication and stakeholder management abilities. Proven track record in delivering complex MDM projects. Ability to translate business requirements into technical solutions Best Regards, MD. Fayaz Hussain | Quant Systems Inc (A Sonata Software Company) IT Recruiter Phone: +91 8099472032 Email: mohammad.Fayaz@sonata-software.com

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

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

We are seeking a talented ML Ops engineer to join our team and play a key role in deploying, managing and optimizing machine learning models in production. The ideal candidate will have a strong background in both machine learning and software engineering, with experience in DevOps practices and cloud computing. Roles Responsibilities: Collaborate with data scientists and software engineers to deploy machine learning models into production environments. Design and implement scalable and reliable ML pipelines for model training, evaluation and deployment. Develop and maintain infrastructure automation scripts for provisioning, configuration, and orchestration using platforms such as Resource Manager (ARM) templates, Automation, PowerShell, or other relevant tools. Optimize ML pipelines for efficiency, scalability and cost-effectiveness Monitor, optimize, and troubleshoot Azure/AWS resources and services to ensure high availability, reliability, and performance of cloud-based applications and systems. Implement security policies, access controls, and encryption protocols to safeguard Azure/AWS environments and data, adhering to compliance and governance requirements. Collaborate with development teams to streamline the continuous integration and continuous deployment (CI/CD) process in Azure/AWS DevOps or similar tools for efficient application delivery. Provide expertise and support in areas such as Azure/AWS cost management, capacity planning, scalability, and disaster recovery strategies. Participate in the evaluation of new Azure/AWS offerings, technologies, and services to drive innovation and improve the overall cloud environment. Qualifications External/Internal What you will need to succeed in the role: (Minimum Qualification and Skills Required) Bachelors degree in Computer Science, Information Technology, or a related field; relevant certifications such as Microsoft Certified: Azure Administrator Associate or Microsoft Certified: Azure Solutions Architect Expert preferred. INTERNAL Strong proficiency in Python programming and familiarity with machine learning frame works such as TensorFlow or Pytorch. Proficiency in implementing and managing security, identity, and access management solutions, leveraging Active Directory, Security Center, and other

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

Role & responsibilities Proficiency in Python, TensorFlow, PyTorch, and basic ML/DL concepts Experience in containerization (Docker, Kubernetes) and cloud-based deployments Familiarity with NLP, embeddings, and transformer models Strong software engineering skills, CI/CD pipelines, and API integration (Flask, FastAPI) Understanding of model evaluation and performance tuning Exposure to MLOps tools (MLflow, Kubeflow) and model observability (Prometheus, Grafana) Preferred candidate profile Assist in building and deploying ML models with best practices Containerize and ship ML models for production use Compare and evaluate model performance, ensuring optimal results Support model fine-tuning and dataset preparation Collaborate on ML workflow automation and monitoring Document processes and stay updated with ML advancements

Role & responsibilities Expertise in ML/DL, model lifecycle management, and MLOps (MLflow, Kubeflow). Proficiency in Python, TensorFlow, PyTorch, Scikit-learn, and Hugging Face models. Strong experience in NLP, fine-tuning transformer models, and dataset preparation. Hands-on with cloud platforms (AWS, GCP, Azure) and scalable ML deployment (Sagemaker, Vertex AI). Experience in containerization (Docker, Kubernetes) and CI/CD pipeline. Knowledge of distributed computing (Spark, Ray), vector databases (FAISS, Milvus), and model optimization (quantization, pruning). Familiarity with model evaluation, hyperparameter tuning, and model monitoring for drift detection. Preferred candidate profile Design and implement end-to-end ML pipelines from data ingestion to production. Develop, fine-tune, and optimize ML models, ensuring high performance and scalability. Compare and evaluate models using key metrics (F1-score, AUC-ROC, BLEU etc.). Automate model retraining, monitoring, and drift detection. Collaborate with engineering teams for seamless ML integration. Mentor junior team members and enforce best practices.