169 Kubeflow Jobs - Page 6

Strong knowledge of Python, R, and ML frameworks such as scikit-learn, TensorFlow, PyTorch Experience with cloud ML platforms: SageMaker, Azure ML, Vertex AI LLM Experience such as GPT Hands-on experience with data wrangling, feature engineering, and model optimization Also experienced in developing model wrapers Deep understanding of algorithms including regression, classification, clustering, NLP, and deep learning Familiarity with MLOps tools like MLflow, Kubeflow, or Airflow

Strong knowledge of Python, R, and ML frameworks such as scikit-learn, TensorFlow, PyTorch Experience with cloud ML platforms: SageMaker, Azure ML, Vertex AI LLM Experience such as GPT Hands-on experience with data wrangling, feature engineering, and model optimization Also experienced in developing model wrapers Deep understanding of algorithms including regression, classification, clustering, NLP, and deep learning Familiarity with MLOps tools like MLflow, Kubeflow, or Airflow

Product Engineer (Onsite, Hyderabad) Experience: 5 - 8 Years Exp Salary : INR 30-32 Lacs per annum Preferred Notice Period : Within 30 Days Shift : 9:00AM to 6:00PM IST Opportunity Type: Onsite (Hyderabad) Placement Type: Permanent (*Note: This is a requirement for one of Uplers' Clients) Must have skills required : Python, FastAPI, Django, MLFlow, feast, Kubeflow, Numpy, Pandas, Big Data Good to have skills : Banking, Fintech, Product Engineering background IF (One of Uplers' Clients) is Looking for: Product Engineer (Onsite, Hyderabad) 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 Product Engineer Location: Narsingi, Hyderabad 5 days of work from the Office Client is a Payment gateway processing company Interview Process: Screening round with InfraCloud, followed by a second round with our Director of Engineering. We share the profile with the client, and they take one/two interviews About the Project We are building a high-performance machine learning engineering platform that powers scalable, data-driven solutions for enterprise environments. Your expertise in Python, performance optimization, and ML tooling will play a key role in shaping intelligent systems for data science and analytics use cases. Experience with MLOps, SaaS products, or big data environments will be a strong plus. Role and Responsibilities Design, build, and optimize components of the ML engineering pipeline for scalability and performance. Work closely with data scientists and platform engineers to enable seamless deployment and monitoring of ML models. Implement robust workflows using modern ML tooling such as Feast, Kubeflow, and MLflow. Collaborate with cross-functional teams to design and scale end-to-end ML services across a cloud-native infrastructure. Leverage frameworks like NumPy, Pandas, and distributed compute environments to manage large-scale data transformations. Continuously improve model deployment pipelines for reliability, monitoring, and automation. Requirements 5+ years of hands-on experience in Python programming with a strong focus on performance tuning and optimization. Solid knowledge of ML engineering principles and deployment best practices. Experience with Feast, Kubeflow, MLflow, or similar tools. Deep understanding of NumPy, Pandas, and data processing workflows. Exposure to big data environments and a good grasp of data science model workflows. Strong analytical and problem-solving skills with attention to detail. Comfortable working in fast-paced, agile environments with frequent cross-functional collaboration. Excellent communication and collaboration skills. Nice to Have Experience deploying ML workloads in public cloud environments (AWS, GCP, or Azure). Familiarity with containerization technologies like Docker and orchestration using Kubernetes. Exposure to CI/CD pipelines, serverless frameworks, and modern cloud-native stacks. Understanding of data protection, governance, or security aspects in ML pipelines. Experience Required: 5+ years 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: We foster business expansion through our innovative products and services, facilitating the seamless adoption of cloud-native technologies by companies. Our expertise lies in the revitalization of applications and infrastructure, harnessing the power of cloud-native solutions for enhanced resilience and scalability. As pioneering Kubernetes partners, we have been dedicated contributors to the open-source cloud-native community, consistently achieving nearly 100% growth over the past few years. We take pride in spearheading local chapters of Serverless & Kubernetes Meetup, actively participating in the development of a vibrant community dedicated to cutting-edge technologies within the Cloud and DevOps domains. About Uplers: Uplers is the #1 hiring platform for SaaS companies, designed to help you hire top product and engineering talent quickly and efficiently. Our end-to-end AI-powered platform combines artificial intelligence with human expertise to connect you with the best engineering talent from India. With over 1M deeply vetted professionals, Uplers streamlines the hiring process, reducing lengthy screening times and ensuring you find the perfect fit. Companies like GitLab, Twilio, TripAdvisor, and AirBnB trust Uplers to scale their tech and digital teams effectively and cost-efficiently. Experience a simpler, faster, and more reliable hiring process with Uplers today.

Job Summary: We are seeking a highly skilled MLOps Engineer to design, deploy, and manage machine learning pipelines in Google Cloud Platform (GCP). In this role, you will be responsible for automating ML workflows, optimizing model deployment, ensuring model reliability, and implementing CI/CD pipelines for ML systems. You will work with Vertex AI, Kubernetes (GKE), BigQuery, and Terraform to build scalable and cost-efficient ML infrastructure. The ideal candidate must have a good understanding of ML algorithms, experience in model monitoring, performance optimization, Looker dashboards and infrastructure as code (IaC), ensuring ML models are production-ready, reliable, and continuously improving. You will be interacting with multiple technical teams, including architects and business stakeholders to develop state of the art machine learning systems that create value for the business. Responsibilities: Managing the deployment and maintenance of machine learning models in production environments and ensuring seamless integration with existing systems. Monitoring model performance using metrics such as accuracy, precision, recall, and F1 score, and addressing issues like performance degradation, drift, or bias. Troubleshoot and resolve problems, maintain documentation, and manage model versions for audit and rollback. Analyzing monitoring data to preemptively identify potential issues and providing regular performance reports to stakeholders. Optimization of the queries and pipelines. Modernization of the applications whenever required Qualifications: Expertise in programming languages like Python, SQL Solid understanding of best MLOps practices and concepts for deploying enterprise level ML systems. Understanding of Machine Learning concepts, models and algorithms including traditional regression, clustering models and neural networks (including deep learning, transformers, etc.) Understanding of model evaluation metrics, model monitoring tools and practices. Experienced with GCP tools like BigQueryML, MLOPS, Vertex AI Pipelines (Kubeflow Pipelines on GCP), Model Versioning & Registry, Cloud Monitoring, Kubernetes, etc. Solid oral and written communication skills and ability to prepare detailed technical documentation of new and existing applications. Strong ownership and collaborative qualities in their domain. Takes initiative to identify and drive opportunities for improvement and process streamlining. Bachelors Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience. Bonus Qualifications: Experience in Azure MLOPS, Familiarity with Cloud Billing. Experience in setting up or supporting NLP, Gen AI, LLM applications with MLOps features. Experience working in an Agile environment, understanding of Lean Agile principles.

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

Strong knowledge of Python, R, and ML frameworks such as scikit-learn, TensorFlow, PyTorch. Experience with cloud ML platforms: SageMaker, Azure ML, Vertex AI.LLM Experience such as GPT Hands-on experience with data wrangling, feature engineering, and model optimization. Also experienced in developing model wrapers. Deep understanding of algorithms including regression, classification, clustering, NLP, and deep learning. Familiarity with MLOps tools like MLflow, Kubeflow, or Airflow.

ZS s Beyond Healthcare Analytics (BHCA) Team is shaping one of the key growth vector area for ZS, Beyond Healthcare engagement, comprising of clients from industries like Quick service restaurants, Technology, Food & Beverage, Hospitality, Travel, Insurance, Consumer Products Goods & other such industries across North America, Europe & South East Asia region. BHCA India team currently has presence across New Delhi, Pune and Bengaluru offices and is continuously expanding further at a great pace. BHCA India team works with colleagues across clients and geographies to create and deliver real world pragmatic solutions leveraging AI SaaS products & platforms, Generative AI applications, and other Advanced analytics solutions at scale. What You ll Do: Build, Refine and Use ML Engineering platforms and components. Scaling machine learning algorithms to work on massive data sets and strict SLAs. Build and orchestrate model pipelines including feature engineering, inferencing and continuous model training. Implement ML Ops including model KPI measurements, tracking, model drift & model feedback loop. Collaborate with client facing teams to understand business context at a high level and contribute in technical requirement gathering. Implement basic features aligning with technical requirements. Write production-ready code that is easily testable, understood by other developers and accounts for edge cases and errors. Ensure highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews. Write unit tests as well as higher level tests to handle expected edge cases and errors gracefully, as well as happy paths. Uses bug tracking, code review, version control and other tools to organize and deliver work. Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues and dependencies. Consistently contribute in researching & evaluating latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions. What You ll Bring A master's or bachelor s degree in Computer Science or related field from a top university. 4+ years hands-on experience in ML development. Good understanding of the fundamentals of machine learning Strong programming expertise in Python, PySpark/Scala. Expertise in crafting ML Models for high performance and scalability. Experience in implementing feature engineering, inferencing pipelines, and real time model predictions. Experience in ML Ops to measure and track model performance, experience working with MLFlow Experience with Spark or other distributed computing frameworks. Experience in ML platforms like Sage maker, Kubeflow. Experience with pipeline orchestration tools such Airflow. Experience in deploying models to cloud services like AWS, Azure, GCP, Azure ML. Expertise in SQL, SQL DB's. Knowledgeable of core CS concepts such as common data structures and algorithms. Collaborate well with teams with different backgrounds / expertise / functions

Gen AI + DS + ML Ops Job Title: Generative AI and Data Science Engineer with MLOps Expertise Location: Gurgaon, India Employment Type: Full-time About the Role: We are seeking a versatile and highly skilled Generative AI and Data Science Engineer with strong MLOps expertise. This role combines deep technical knowledge in data science and machine learning with a focus on designing and deploying scalable, production-level AI solutions. You will work with cross-functional teams to drive AI/ML projects from research and prototyping through to deployment and maintenance, ensuring model robustness, scalability, and efficiency. Responsibilities: Generative AI Development and Data Science: Design, develop, and fine-tune generative AI models for various applications such as natural language processing, image synthesis, and data augmentation. Perform exploratory data analysis (EDA) and statistical modeling to identify trends, patterns, and actionable insights. Collaborate with data engineering and product teams to create data pipelines for model training, testing, and deployment. Apply data science techniques to optimize model performance and address real-world business challenges. Machine Learning Operations (MLOps): Implement MLOps best practices for managing and automating the end-to-end machine learning lifecycle, including model versioning, monitoring, and retraining. Build, maintain, and optimize CI/CD pipelines for ML models to streamline development and deployment processes. Ensure scalability, robustness, and security of AI/ML systems in production environments. Develop tools and frameworks for monitoring model performance and detecting anomalies post-deployment. Research and Innovation: Stay current with advancements in generative AI, machine learning, and MLOps technologies and frameworks. Identify new methodologies, tools, and technologies that could enhance our AI and data science capabilities. Engage in R&D initiatives and collaborate with team members on innovative projects. Requirements: Educational Background: Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field. PhD is a plus. Technical Skills: Proficiency in Python and familiarity with machine learning libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn). Strong understanding of generative AI models (e.g., GANs, VAEs, transformers) and deep learning techniques. Experience with MLOps frameworks and tools such as MLflow, Kubeflow, Docker, and CI/CD platforms. Knowledge of data science techniques for EDA, feature engineering, statistical modeling, and model evaluation. Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying and scaling AI/ML models. Soft Skills: Ability to collaborate effectively across teams and communicate complex technical concepts to non-technical stakeholders. Strong problem-solving skills and the ability to innovate in a fast-paced environment. Preferred Qualifications: Prior experience in designing and deploying large-scale generative AI models. Proficiency in SQL and data visualization tools (e.g., Tableau, Power BI). Experience with model interpretability and explainability frameworks.

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

We are seeking a highly experienced and technically adept Lead AI/ML Engineer to spearhead the development and deployment of cutting-edge AI solutions, with a focus on Generative AI and Natural Language Processing (NLP). The ideal candidate will be responsible for leading a high-performing team, architecting scalable ML systems, and driving innovation across AI/ML projects using modern toolchains and cloud-native technologies. Key Responsibilities Team Leadership: Lead, mentor, and manage a team of data scientists and ML engineers; drive technical excellence and foster a culture of innovation. AI/ML Solution Development: Design and deploy end-to-end machine learning and AI solutions, including Generative AI and NLP applications. Conversational AI: Build LLM-based chatbots and document intelligence tools using frameworks like LangChain , Azure OpenAI , and Hugging Face . MLOps Execution: Implement and manage the full ML lifecycle using tools such as MLFlow , DVC , and Kubeflow to ensure reproducibility, scalability, and efficient CI/CD of ML models. Cross-functional Collaboration: Partner with business and engineering stakeholders to translate requirements into impactful AI solutions. Visualization & Insights: Develop interactive dashboards and data visualizations using Streamlit , Tableau , or Power BI for presenting model results and insights. Project Management: Own delivery of projects with clear milestones, timelines, and communication of progress and risks to stakeholders. Required Skills & Qualifications Languages & Frameworks: Proficient in Python and frameworks like TensorFlow , PyTorch , Keras , FastAPI , Django NLP & Generative AI: Hands-on experience with BERT , LLaMA , Spacy , LangChain , Hugging Face , and other LLM-based technologies MLOps Tools: Experience with MLFlow , Kubeflow , DVC , ClearML for managing ML pipelines and experiment tracking Visualization: Strong in building visualizations and apps using Power BI , Tableau , Streamlit Cloud & DevOps: Expertise with Azure ML , Azure OpenAI , Docker , Jenkins , GitHub Actions Databases & Data Engineering: Proficient with SQL/NoSQL databases and handling large-scale datasets efficiently Preferred Qualifications Masters or PhD in Computer Science, AI/ML, Data Science, or related field Experience working in agile product development environments Strong communication and presentation skills with technical and non-technical stakeholders

As a AI/ML Engineer, you will be responsible for designing, developing, and implementing machine learning algorithms and AI solutions that address complex business challenges. You will lead a team of engineers collaborating with cross-functional teams to drive the successful deployment of AI initiatives. Your expertise will be crucial in shaping our AI strategy and ensuring the delivery of high-quality, scalable solutions. Key Responsibilities: Lead the design and development of machine learning models and AI solutions to solve business problems. Select appropriate machine learning or deep learning models based on problem context and data availability. Develop, train, test, and validate models using state-of-the-art methodologies and frameworks. Collaborate with analytics, developers and domain experts to acquire, clean, and preprocess large datasets. Engineer features and performs exploratory data analysis to ensure data quality and model readiness. Containerize models (Docker/Kubernetes), implement monitoring (Prometheus/Grafana), and automate pipelines (MLflow/Kubeflow). Implement models into production environments, ensuring robustness, scalability, and maintainability. Develop and maintain CI/CD pipelines for seamless model integration and deployment. Monitor and evaluate model performance post-deployment and iterate based on feedback and performance metrics. Document model architecture, development processes, and performance evaluations thoroughly. Share insights and technical know-how with team members to foster a culture of continuous learning and improvement. Research & Innovation: Stay ahead of AI trends (LLMs, generative AI) and advocate for ethical AI practices. Analyze large datasets to extract insights and improve model performance. Ensure compliance with data privacy and security regulations in all AI/ML initiatives. Qualifications: Bachelor's or master's degree in computer science, Data Science, Machine Learning, or a related field. Proven experience (3+ years) in AI/ML engineering, with a strong portfolio of successful projects. Proficiency in programming languages such as Python, R, or Java, and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Strong understanding of machine learning algorithms, statistical modeling, and data analysis techniques. Experience with cloud platforms (e.g., AWS, Azure, Google Cloud)

Your Day-to-Day Provide technical leadership and guidance to teams of software engineers, fostering a culture of collaboration, innovation, and continuous improvement. Establish outcomes and key results (OKRs) and successfully deliver them. Drive improvements in key performance indicators (KPIs). Increase the productivity and velocity of delivery teams. Develop, plan, and execute engineering roadmaps that bring value and quality to our customers. Collaborate and coordinate across teams and functions to ensure technical, product, and business objectives are met. Instill end-to-end ownership of products, projects, features, modules, and services that you and your team deliver in all phases of the software development lifecycle. What do you need to bring 10+ years of experience in the software industry, with 3+ years of professional experience leading software development teams. Strong critical thinking and problem-solving skills with the ability to address complex technical and non-technical challenges. Experience building and developing engineering teams that exhibit strong ownership, user empathy, and engineering excellence. Proven track record of delivering high-quality systems and software in Big Data Technologies including Spark, Airflow, Hive, etc., with practical exposure to integrating machine learning workflows into data pipelines. Proven track record of delivering high-quality systems and software in Java/J2EE technologies and distributed systems, with experience deploying ML models into production at scale using REST APIs, streaming platforms, or batch inference. Excellent communication skills with the ability to collaborate effectively with cross-functional teams (including data scientists and ML engineers) and manage stakeholders expectations. Ability to coach and mentor talent to reach their full potential, including guiding teams in adopting MLOps best practices and understanding AI model lifecycle management. Experience in building large scale, high throughput, low latency systems, including real-time data processing systems that support personalization, anomaly detection, or predictive analytics. Strong understanding of software development methodologies, modern technology topics and frameworks, and developer operations best practices. Experience with ML platforms (e.g., Kubeflow, MLflow) and familiarity with model monitoring, feature engineering, and data versioning tools is a plus. Provide leadership to others, particularly junior engineers who work on the same team or related product features. Proven experience delivering complex software projects and solutions effectively through Agile methodologies on a regular release cadence. Strong verbal and written communication skills. Strong customer focus, ownership, urgency and drive.

AI/ML Architect Experience 10+ years in total, 8+ years in AI/ML development 3+ years in AI/ML architecture Education Bachelors/Masters in CS, AI/ML, Engineering, or similar Title: AI/ML Architect Location: Onsite Bangalore Experience: 10+ years Position Summary: We are seeking an experienced AI/ML Architect to lead the design and deployment of scalable AI solutions. This role requires a strong blend of technical depth, systems thinking, and leadership in machine learning , computer vision , and real-time analytics . You will drive the architecture for edge, on-prem, and cloud-based AI systems, integrating 3rd party data sources, sensor and vision data to enable predictive, prescriptive, and autonomous operations across industrial environments. Key Responsibilities: Architecture & Strategy Define the end-to-end architecture for AI/ML systems including time series forecasting , computer vision , and real-time classification . Design scalable ML pipelines (training, validation, deployment, retraining) using MLOps best practices. Architect hybrid deployment models supporting both cloud and edge inference for low-latency processing. Model Integration Guide the integration of ML models into the IIoT platform for real-time insights, alerting, and decision support. Support model fusion strategies combining disparate data sources, sensor streams with visual data (e.g., object detection + telemetry + 3rd party data ingestion). MLOps & Engineering Define and implement ML lifecycle tooling, including version control, CI/CD, experiment tracking, and drift detection. Ensure compliance, security, and auditability of deployed ML models. Collaboration & Leadership Collaborate with Data Scientists, ML Engineers, DevOps, Platform, and Product teams to align AI efforts with business goals. Mentor engineering and data teams in AI system design, optimization, and deployment strategies. Stay ahead of AI research and industrial best practices; evaluate and recommend emerging technologies (e.g., LLMs, vision transformers, foundation models). Must-Have Qualifications: Bachelors or Master’s degree in Computer Science, AI/ML, Engineering, or a related technical field. 8+ years of experience in AI/ML development, with 3+ years in architecting AI solutions at scale. Deep understanding of ML frameworks (TensorFlow, PyTorch), time series modeling, and computer vision. Proven experience with object detection, facial recognition, intrusion detection , and anomaly detection in video or sensor environments. Experience in MLOps (MLflow, TFX, Kubeflow, SageMaker, etc.) and model deployment on Kubernetes/Docker . Proficiency in edge AI (Jetson, Coral TPU, OpenVINO) and cloud platforms (AWS, Azure, GCP). Nice-to-Have Skills: Knowledge of stream processing (Kafka, Spark Streaming, Flink). Familiarity with OT systems and IIoT protocols (MQTT, OPC-UA). Understanding of regulatory and safety compliance in AI/vision for industrial settings. Experience with charts, dashboards, and integrating AI with front-end systems (e.g., alerts, maps, command center UIs). Role Impact: As AI/ML Architect, you will shape the intelligence layer of our IIoT platform — enabling smarter, safer, and more efficient industrial operations through AI. You will bridge research and real-world impact , ensuring our AI stack is scalable, explainable, and production-grade from day one.

5+ years of experience in Python programing and performance tuning and optimization. Experience on ML engineering, Knowledge on Feast, Kubeflow and MLFlow. Deep understanding on NumPy , Pandas, data frames etc. Working knowledge on bigdata environment and data science model added advantage. Strong analytical and problem-solving skills, with attention to detail and ability to work in a fast-paced environment Excellent communication and collaboration skills, with ability to work with cross-functional teams Knowledge of machine learning and data science concepts

Role Summary: The AI/ML Platform Engineering Lead is a pivotal leadership role responsible for managing the day-to-day operations and development of the AI/ML platform team. In this role, you will guide the team in designing, building, and maintaining scalable platforms, while collaborating with other engineering and data science teams to ensure successful model deployment and lifecycle management. You can apply directly using the link below: https://jobs.lever.co/welocalize/628f87a7-4edd-4b4f-b440-3b602aa4dadc Key Responsibilities: Lead and manage a team of platform engineers in developing and maintaining robust AI/ML platforms. Define and implement best practices for machine learning infrastructure, ensuring scalability, performance, and security. Collaborate closely with data scientists and DevOps teams to optimize the ML lifecycle from model training to deployment. Establish and enforce standards for platform automation, monitoring, and operational efficiency. Serve as the primary liaison between engineering teams, product teams, and leadership. Mentor and develop junior engineers, providing technical guidance and performance feedback. Stay abreast of the latest advancements in AI/ML infrastructure and integrate new technologies where applicable. Qualifications: Bachelors or Masters degree in Computer Science, Engineering, or a related field. 8+ years of experience in AI/ML platform development and infrastructure. Proven experience in leading engineering teams and driving large-scale projects. Extensive expertise in cloud infrastructure (AWS, GCP, Azure), MLOps tools (e.g., Kubeflow, MLflow), and infrastructure as code (Terraform) Strong programming skills in Python and Node.js , with a proven track record of building scalable and maintainable systems that support AI/ML workflows. Hands-on experience with monitoring and observability tools, such as Datadog, to ensure platform reliability and performance. Strong leadership and communication skills with the ability to influence cross-functional teams. Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.

Position overview: We are seeking a ML Engineer with a strong background in Machine Learning, Natural Language Processing (NLP), Generative AI, and Retrieval-Augmented Generation (RAG). The ideal candidate will possess 1+ years of hands-on experience in developing and deploying advanced data-driven solutions. You will play a key role in our AI-CoE team, contributing to cutting-edge projects that drive innovation and business value. A special focus area for this role would be to build AI enabled products that would result in the creation of monetizable product differentiators for Tata Communications products and services. Detailed job description & Key Responsibilities: Design, develop, and deploy machine learning systems, including Generative AI models and LLMs. Research and implement state-of-the-art ML algorithms and tools. Conduct data preprocessing, feature engineering, and statistical analysis. Train, fine-tune, and optimize machine learning models for performance and accuracy. Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts. Extend existing ML frameworks and libraries to meet project requirements. Stay updated with the latest advancements in machine learning and AI. Skills: Working knowledge of machine learning and deep learning skills. Strong knowledge of programming knowledge Python, SQL and commonly used frameworks & tools PyTorch, Sci-kit, NumPy, Gen AI tools like langchain/llamaIndex Working knowledge of MLOPs principles and implementing projects with Big Data in batch and streaming mode. - must have Experience in handling databases (SQL and NoSQL) Exposure to MLFlow, KubeFlow, Git CI/CD Experience with containerization tools like Docker, and orchestration tools like Kubernetes Excellent problem-solving skills and a proactive attitude. Strong communication and teamwork abilities. Ability to manage multiple projects and meet deadlines Interview will involve coding test

Position overview: We are seeking a ML Engineer with a strong background in Machine Learning, Natural Language Processing (NLP), Generative AI, and Retrieval-Augmented Generation (RAG). The ideal candidate will possess 1+ years of hands-on experience in developing and deploying advanced data-driven solutions. You will play a key role in our AI-CoE team, contributing to cutting-edge projects that drive innovation and business value. A special focus area for this role would be to build AI enabled products that would result in the creation of monetizable product differentiators for Tata Communications products and services. Detailed job description & Key Responsibilities: Design, develop, and deploy machine learning systems, including Generative AI models and LLMs. Research and implement state-of-the-art ML algorithms and tools. Conduct data preprocessing, feature engineering, and statistical analysis. Train, fine-tune, and optimize machine learning models for performance and accuracy. Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts. Extend existing ML frameworks and libraries to meet project requirements. Stay updated with the latest advancements in machine learning and AI. Skills: Working knowledge of machine learning and deep learning skills. Strong knowledge of programming knowledge Python, SQL and commonly used frameworks & tools PyTorch, Sci-kit, NumPy, Gen AI tools like langchain/llamaIndex Working knowledge of MLOPs principles and implementing projects with Big Data in batch and streaming mode. - must have Experience in handling databases (SQL and NoSQL) Exposure to MLFlow, KubeFlow, Git CI/CD Experience with containerization tools like Docker, and orchestration tools like Kubernetes Excellent problem-solving skills and a proactive attitude. Strong communication and teamwork abilities. Ability to manage multiple projects and meet deadlines Interview will involve coding test

Position overview: We are seeking a Data Scientist with a strong background in Machine Learning, Natural Language Processing (NLP), Generative AI, and Retrieval-Augmented Generation (RAG). The ideal candidate will possess 1+ years of hands-on experience in developing and deploying advanced data-driven solutions. You will play a key role in our AI-CoE team, contributing to cutting-edge projects that drive innovation and business value. A special focus area for this role would be to build AI enabled products that would result in the creation of monetizable product differentiators. Detailed job description & Key Responsibilities: Develop, Test, and Deploy machine learning models for various business and Telco use cases. Perform data preprocessing, feature engineering and ML/DL model evaluation. Optimize and fine-tune models for performance and scalability. Good understanding of NLP concepts and projects involving entity recognition, text classification, and language modelling like GPT/Llama/Claude/Grok Build and refine RAG models to improve information retrieval and answer generation systems. Integrate RAG methods into existing applications to enhance data accessibility and user experience. Work closely with cross-functional teams including software engineers, product managers, and domain experts. Communicate technical concepts to non-technical stakeholders effectively. Document processes, methodologies, and model development for internal and external stakeholders. Skills: Strong knowledge of probability and statistics. Working knowledge of machine learning and deep learning skills. Strong knowledge of programming knowledge Python, SQL and commonly used frameworks & tools PyTorch, Sci-kit, NumPy, Gen AI tools like langchain/llamaIndex Working knowledge of MLOPs principles and implementing projects with Big Data in batch and streaming mode. Excellent problem-solving skills and a proactive attitude. Strong communication and teamwork abilities. Ability to manage multiple projects and meet deadlines Interview will involve coding tests.

Position overview: We are seeking a Data Scientist with a strong background in Machine Learning, Natural Language Processing (NLP), Generative AI, and Retrieval-Augmented Generation (RAG). The ideal candidate will possess 1+ years of hands-on experience in developing and deploying advanced data-driven solutions. You will play a key role in our AI-CoE team, contributing to cutting-edge projects that drive innovation and business value. A special focus area for this role would be to build AI enabled products that would result in the creation of monetizable product differentiators. Detailed job description & Key Responsibilities: Develop, Test, and Deploy machine learning models for various business and Telco use cases. Perform data preprocessing, feature engineering and ML/DL model evaluation. Optimize and fine-tune models for performance and scalability. Good understanding of NLP concepts and projects involving entity recognition, text classification, and language modelling like GPT/Llama/Claude/Grok Build and refine RAG models to improve information retrieval and answer generation systems. Integrate RAG methods into existing applications to enhance data accessibility and user experience. Work closely with cross-functional teams including software engineers, product managers, and domain experts. Communicate technical concepts to non-technical stakeholders effectively. Document processes, methodologies, and model development for internal and external stakeholders. Skills: Strong knowledge of probability and statistics. Working knowledge of machine learning and deep learning skills. Strong knowledge of programming knowledge Python, SQL and commonly used frameworks & tools PyTorch, Sci-kit, NumPy, Gen AI tools like langchain/llamaIndex Working knowledge of MLOPs principles and implementing projects with Big Data in batch and streaming mode. Excellent problem-solving skills and a proactive attitude. Strong communication and teamwork abilities. Ability to manage multiple projects and meet deadlines Interview will involve coding tests.

We are looking for experienced Senior and Principal Machine Learning Engineers across two teams: InstructLab and OpenShift AI . In this role, you will build, optimize, and scale machine learning models while contributing to innovative AI-driven solutions, and assisting users in understanding ML predictions. During the hiring process, we'll work with you to determine the best team placement based on your background and interests. While the core job requirements remain consistent, your day-to-day responsibilities will align with your chosen team's objectives. Successful applicants must reside in a country where Red Hat is registered to do business. What you will do Specific responsibilities will vary based on team placement, but may include: Design and implement machine learning systems Develop and optimize ML models for production use Create and maintain ML infrastructure and pipelines Ensure ML systems are scalable and maintainable Collaborate with data scientists to productionize models Collaborate closely with researchers, software developers, and upstream AI/ML communities Mentor and guide other team members What you will bring Experience in AI development, deep learning, machine learning libraries (e.g. pytorch, scikit-learn), prompt engineering, and/or fundamental mathematics Experience in feature engineering Experience in Go or Python development Experience in Kubernetes, OpenShift, Docker, or other cloud-native technologies Experience in agile development, Jira, and Git Ability to quickly learn and use new tools and technologies Excellent written and verbal communication skills The following skills are valued and may influence team placement: Masters or PhD in Machine Learning (ML) or Natural Language Processing (NLP) Active participation in KServe, TrustyAI, Kubeflow, or other open source communities Specialized expertise in specific AI domains (NLP, Computer Vision, MLOps, etc.)

HPE is seeking Data Engineer with strong experience in machine learning workflows to build and optimize scalable data systems. You'll work closely with data scientists and data engineers to power ML-driven solutions. Responsibilities: Collaborate closely with Machine Learning (ML) teams to deploy and monitor models in production, ensuring optimal performance and reliability. Design and implement experiments, and apply statistical analysis to validate model solutions and results. Lead efforts in ensuring high-quality data, proper governance practices, and excellent system performance in complex data architectures. Develop, maintain, and scale data pipelines, enabling machine learning and analytical models to function efficiently. Monitor and troubleshoot issues within data systems, resolving performance bottlenecks and implementing best practices. Required Skills: 56 years of data engineering experience, with a proven track record in building scalable data systems. Proficiency in SQL & NoSQL databases, Python, and distributed processing technologies such as Apache Spark. Strong understanding of data warehousing concepts, data modelling, and architecture principles. Expertise in cloud platforms (AWS, GCP, Azure) and managing cloud-based data systems would be an added advantage Hands-on experience building and maintaining machine learning pipelines and utilizing tools like MLflow, Kubeflow, or similar frameworks. Experience with search, recommendation engines, or NLP (Natural Language Processing) technologies. Solid foundation in statistics and experimental design, particularly in relation to machine learning systems. Strong problem-solving skills and ability to work independently and in a team-oriented environment.

Develop and deploy ML pipelines using MLOps tools, build FastAPI-based APIs, support LLMOps and real-time inferencing, collaborate with DS/DevOps teams, ensure performance and CI/CD compliance in AI infrastructure projects. Required Candidate profile Experienced Python developer with 4–8 years in MLOps, FastAPI, and AI/ML system deployment. Exposure to LLMOps, GenAI models, containerized environments, and strong collaboration across ML lifecycle

We are looking for Indias top 1% Machine Learning Engineers for a unique job opportunity to work with the industry leaders Who can be a part of the community? We are looking for top-tier Machine Learning Engineers with expertise in building, deploying, and optimizing AI models If you have experience in this field then this is your chance to collaborate with industry leaders Whats in it for you? Pay above market standards The role is going to be contract based with project timelines from 2-6 months, or freelancing Be a part of Elite Community of professionals who can solve complex AI challenges Responsibilities: Design, optimize, and deploy machine learning models; implement feature engineering and scaling pipelines Use deep learning frameworks (TensorFlow, PyTorch) and manage models in production (Docker, Kubernetes) Automate workflows, ensure model versioning, logging, and real-time monitoring; comply with security and regulations Work with large-scale data, develop feature stores, and implement CI/CD pipelines for model retraining and performance tracking Required Skills: Proficiency in machine learning, deep learning, and data engineering (Spark, Kafka) Expertise in MLOps, automation tools (Docker, Kubernetes, Kubeflow, MLflow, TFX), and cloud platforms (AWS, GCP, Azure) Strong knowledge of model deployment, monitoring, security, compliance, and responsible AI practices Nice to Have: Experience with A/B testing, Bayesian optimization, and hyperparameter tuning Familiarity with multi-cloud ML deployments and generative AI technologies (LLM fine-tuning, FAISS). Locations : Mumbai, Delhi / NCR, Bengaluru , Kolkata, Chennai, Hyderabad, Ahmedabad, Pune, India

Why Join? Above market-standard compensation Contract-based or freelance opportunities (212 months) Work with industry leaders solving real AI challenges Flexible work locations Remote | Onsite | Hyderabad/Bangalore Your Role: Architect and optimize ML infrastructure with Kubeflow, MLflow, SageMaker Pipelines Build CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI/CD) Automate ML workflows (feature engineering, retraining, deployment) Scale ML models with Docker, Kubernetes, Airflow Ensure model observability, security, and cost optimization in cloud (AWS/GCP/Azure) Must-Have Skills: Proficiency in Python, TensorFlow, PyTorch, CI/CD pipelines Hands-on experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML) Expertise in monitoring tools (MLflow, Prometheus, Grafana) Knowledge of distributed data processing (Spark, Kafka) (Bonus: Experience in A/B testing, canary deployments, serverless ML)

Roles and Responsibilities Design, Develop, and Deploy : Develop, deploy, and maintain machine learning models that are not only theoretically sound but also practical and scalable. Our team places a strong emphasis on rapid, trustworthy experimentation for validating models and features. Model Maintenance : Design and build machine learning pipelines optimized for scalability, ensuring seamless model training, evaluation, and deployment. Monitor the performance of machine learning models in real-time using statistical methods, ensuring their efficiency and effectiveness.Implement and manage real-time data systems to handle large data streams efficiently. Technical Expertise : Conduct RD in innovative techniques such as Recommender Systems, Computer Vision, NLP, Generative AI and Causal Inference, pushing the boundaries of practical machine learning applications. Software Development : Develop robust, scalable, and maintainable software solutions for seamless model deployment. CI/CD Pipelines : Set up and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for automated testing, deployment, and model integration. Collaboration : Work closely with the Product Managers, Platforms and Engineering teams to ensure smooth deployment and integration of ML models into Myntra production systems. Data Management : Utilize big data technologies and data lakes to preprocess and shape raw data for machine learning applications. Code Quality : Write clean, efficient, and maintainable code following best practices. Performance Optimization : Conduct performance testing, troubleshooting, and tuning to ensure optimal model performance. Continuous Learning : Stay up-to-date with the latest advancements in machine learning and technology, sharing insights and knowledge across the organization. Experience Industry Experience: Master's degree in a related technical field with 4+ years of relevant industry experience or Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related technical field with 6+ years of relevant industry experience OR OR Ph.D. in a related field with a thesis in a domain relevant to Myntra's needs (e.g., Recommender Systems, Natural Language Processing). Machine Learning Expertise: At least 4 years of hands-on experience as a Machine Learning Engineer or a similar role. Solid understanding of statistics, particularly as it applies to machine learning, including probability theory, hypothesis testing, and statistical inference. Production Deployment: Proven track record of implementing and scaling machine learning models and pipelines in a production environment. Programming Skills: Strong proficiency in Python or equivalent programming languages for model development. ML Frameworks: Familiarity with leading machine learning frameworks (Keras, TensorFlow, PyTorch) and libraries (scikit-learn). CI/CD Tools: Experience with CI/CD tools and practices. Communication: Excellent verbal and written communication skills. Teamwork Independence: Ability to work collaboratively in a team environment or independently as needed. Mentor team members technically on designing and deploying ML pipelines and services. Workload Management: Strong organizational skills to manage and prioritize tasks, supporting your manager effectively. Preferred Qualifications Strong emphasis on rapid, trustworthy experimentation for validating machine learning models and hypotheses. Hands-on experience with Search and Recommender Systems, Computer Vision, or Forecasting is strongly desired. We value candidates who emphasize practical implementation and scaling of machine learning solutions. Experience with real-time systems and databases like Kafka, Cassandra, Vector Databases, or Bigtable is highly valued. Prior experience with Generative AI techniques earns brownie points. Advanced understanding and experience in Causal Inference earns a lot of brownie points. Strong communication skills, especially in conveying complex technical and statistical concepts to a non-technical audience. Experience with big data technologies like Spark or other distributed computing frameworks. Exceptional candidates are encouraged to apply, even if you don't meet every listed qualification. We're open to hiring individuals who demonstrate outstanding potential. Nice to Have Research Contributions: Publications or presentations in recognized Machine Learning and Data Science journals/conferences. Cloud Services: Proficiency in cloud platforms (AWS, Google Cloud) and an understanding of distributed systems. Generative AI Exposure: Familiarity with Generative AI models. Database Management: Experience with SQL and/or NoSQL databases. ML Orchestration: Knowledge of ML orchestration tools (Airflow, Kubeflow, MLFlow).