225 Mlops Jobs - Page 4

Job Summary As a Senior Machine Learning Solutions Architect at NetApp, you will be at the forefront of driving innovation by designing and implementing cutting-edge Generative AI (GenAI) and Machine Learning (ML) solutions. Your work will empower our internal sales teams, partners, and customers with actionable insights derived from advanced analytics. You will lead the design of scalable, robust, and production-ready AI/ML systems, from real-time predictive analytics to LLM-driven automation, ensuring seamless integration into our existing infrastructure. Collaborating with cross-functional teams, including senior software developers, technical directors, and business stakeholders, you will play a pivotal role in shaping the future of data-driven decision-making at NetApp. This role demands a creative, results-driven, and collaborative individual who thrives in a fast-paced environment and is passionate about leveraging AI/ML to solve complex business challenges. Job Requirements Architect and Deploy AI/ML Solutions. Design, develop, and implement scalable and robust ML systems using techniques such as classical machine learning, Generative AI models (e.g., LLMs), and AI agents. Ensure the reliability, scalability, and performance of AI/ML models in production environments. Oversee ML design reviews and establish best practices for end-to-end ML systems, including MLOps and CI/CD pipelines. Collaborate Across Teams. Partner with data engineers to build scalable data pipelines for AI/ML-driven solutions, including curated data pipelines, ML feature pipelines, and deployment services. Work closely with business stakeholders to identify and unlock new use cases, driving innovation through data intelligence. Participate in cross-functional meetings, workshops, and planning sessions to align data engineering and AI/ML initiatives with organizational goals. Technical Leadership and Mentorship. Act as a technical thought leader in the AI/ML space, providing guidance on data governance, model lifecycle management, and MLOps practices. Mentor and coach data scientists and cross-functional team members, fostering a culture of innovation, collaboration, and continuous learning. Industry Representation. Represent NetApp as a leader and ambassador in the AI/ML community, building relationships with external partners and promoting the companys capabilities at industry and academic conferences. Education Masters or Bachelors degree in Computer Science, Engineering, Applied Mathematics, Statistics, Data Science, or a related field. 10+ years of experience as a Data and Machine Learning Engineer, with a proven track record of architecting, designing, and implementing end-to-end ML solutions, building scalable data/feature pipelines, and shipping AI/ML-driven products at scale. Recent experience with experimenting and deploying Large Language Models (LLMs) to production is a strong.

Experience in leading a team as well as handling customer stakeholders Hands on experience in building scalable solutions using microservices & deployment of ML Algorithms in production Excellent programming skills in Python, a good understanding in Go is a plus. Experience in Test driven Development Expert knowledge of building and maintaining in Production Experience in cloud - AWS/Azure/Google is mandatory Frontend Exposure to Front end frameworks like React/Angular having led teams and built applications Experience is creating reusable components and libraries Experience is optimising the performance of the web applications Exposure to mobile application development is a plus Database Proficient with SQL, RDBMS such as Postgres, MySQL, SQL Server, Oracle and/or experience with NoSQL DBMSs such as Mongo DB Familiarity with some ORM (Object Relational Mapper) libraries like SQLAlchemy Experience in optimising the performance of the queries. Backend Experience in server-side/back-end full cycle product development in a production environment in Python Hands on experience in python server frameworks like Django/Flask/FastAPI Strong testing and debugging skills. Strong trouble-shooting skills that span systems (Linux), network, and application Understanding of the threading limitations of Python, and multi-process architecture. CI/CD Good knowledge of software engineering practices like version control (GIT) and Dev ops Understanding of ML/AI Pipeline & Development life cycle & tools, MLOps Knowledge

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

3+ years experience as a software engineer or equivalent designing large data-heavy distributed systems and/or high-traffic web-apps Experience in at least one programming language (Python-2 yrs strong coding is must) or java. Hands-on experience designing & managing large data models, writing performant SQL queries, and working with large datasets and related technologies Experience designing & interacting with APIs (REST/GraphQL) Experience working with cloud platforms such as GCP, Big Query Experience in DevOps processes/tooling (CI/CD, GitHub Actions), using version control systems (Git strongly preferred), and working in a remote software development environment Strong analytical, problem solving and interpersonal skills, have a hunger to learn, and the ability to operate in a self-guided manner in a fast-paced rapidly changing environment Preferred: Experience using infrastructure as code frameworks (Terraform) Preferred: Experience using big data tools such as Spark/PySpark Preferred: Experience using or deploying MLOps systems/tooling (eg. MLFlow) Must have: Experience in pipeline orchestration (eg. Airflow) Must Have Experience in Data Flow 1 yr experience atleast Preferred: Experience using infrastructure as code frameworks (Terraform) Preferred: Experience in an additional programming language (JavaScript, Java, etc) Preferred: Experience using data science/machine learning technologies.

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

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

Overview In this role, we are seeking an Associate Manager Offshore Program & Delivery Management to oversee program execution, governance, and service delivery across DataOps, BIOps, AIOps, MLOps, Data IntegrationOps, SRE, and Value Delivery programs. This role requires expertise in offshore execution, cost optimization, automation strategies, and cross-functional collaboration to enhance operational excellence. Manage and support DataOps programs, ensuring alignment with business objectives, data governance standards, and enterprise data strategy. Assist in real-time monitoring, automated alerting, and self-healing mechanisms to improve system reliability and performance. Contribute to the development and enforcement of governance models and operational frameworks to streamline service delivery and execution roadmaps. Support the standardization and automation of pipeline workflows, report generation, and dashboard refreshes to enhance efficiency. Collaborate with global teams to support Data & Analytics transformation efforts and ensure sustainable, scalable, and cost-effective operations. Assist in proactive issue identification and self-healing automation, enhancing the sustainment capabilities of the PepsiCo Data Estate. Responsibilities Support DataOps and SRE operations, assisting in offshore delivery of DataOps, BIOps, Data IntegrationOps, and related initiatives. Assist in implementing governance frameworks, tracking KPIs, and ensuring adherence to operational SLAs. Contribute to process standardization and automation efforts, improving service efficiency and scalability. Collaborate with onshore teams and business stakeholders, ensuring alignment of offshore activities with business needs. Monitor and optimize resource utilization, leveraging automation and analytics to improve productivity. Support continuous improvement efforts, identifying operational risks and ensuring compliance with security and governance policies. Assist in managing day-to-day DataOps activities, including incident resolution, SLA adherence, and stakeholder engagement. Participate in Agile work intake and management processes, contributing to strategic execution within data platform teams. Provide operational support for cloud infrastructure and data services, ensuring high availability and performance. Document and enhance operational policies and crisis management functions, supporting rapid incident response. Promote a customer-centric approach, ensuring high service quality and proactive issue resolution. Assist in team development efforts, fostering a collaborative and agile work environment. Adapt to changing priorities, supporting teams in maintaining focus on key deliverables. Qualifications 6+ years of technology experience in a global organization, preferably in the CPG industry. 4+ years of experience in Data & Analytics, with a foundational understanding of data engineering, data management, and operations. 3+ years of cross-functional IT experience, working with diverse teams and stakeholders. 12 years of leadership or coordination experience, supporting team operations and service delivery. Strong communication and collaboration skills, with the ability to convey technical concepts to non-technical audiences. Customer-focused mindset, ensuring high-quality service and responsiveness to business needs. Experience in supporting technical operations for enterprise data platforms, preferably in a Microsoft Azure environment. Basic understanding of Site Reliability Engineering (SRE) practices, including incident response, monitoring, and automation. Ability to drive operational stability, supporting proactive issue resolution and performance optimization. Strong analytical and problem-solving skills, with a continuous improvement mindset. Experience working in large-scale, data-driven environments, ensuring smooth operations of business-critical solutions. Ability to support governance and compliance initiatives, ensuring adherence to data standards and best practices. Familiarity with data acquisition, cataloging, and data management tools. Strong organizational skills, with the ability to manage multiple priorities effectively.

Hi, Wishes from GSN!!! Pleasure connecting with you!!! We been into Corporate Search Services for Identifying & Bringing in Stellar Talented Professionals for our reputed IT / Non-IT clients in India. We have been successfully providing results to various potential needs of our clients for the last 20 years. At present, GSN is hiring MLOPS ENGINEER for one of our leading MNC client. PFB the details for your better understanding: 1. WORK LOCATION : Bangalore & Mumbai 2. Job Role: MLOPS ENGINEER 3. EXPERIENCE : 5+ yrs 4. CTC Range: Rs. 20 LPA to Rs. 30 LPA 5. Work Type : WFO (all 5 days) ****** Looking for IMMEDIATE JOINER ****** Who are we looking for ? MLOPS Engineer with AWS Experience. Required Skills : Set up and manage infrastructure for model inferencing across our AWS DEV, TEST, and PROD environments on AWS using services like EC2, ECS, EKS, Lambda, SageMaker, Step Functions, Networking etc. and expertise in IAC tools like Terraform. Should have prior experience in deploying ML models on any MLOps platforms (AWS, GCP, Azure, etc.) Having prior experience working on enterprise scale applications and environments. Develop highly scalable architectures both server and serverless. Familiarity with frameworks like Docker, Django, FastAPI Work closely with Data Scientists to deploy their code , necessitating a strong grasp of Python, prompt engineering (including but not limited to Lang Chain and Lang Graph), data science concepts, as well as foundational mathematics and statistics. ****** Looking for IMMEDIATE JOINER ****** Feel free to CALL me @ 8939 666 294 for IMMEDIATE response . Best regards, Shobana | GSN | shobana@gsnhr.net | 8939666294 | Google review : https://g.co/kgs/UAsF9W

Architecture & Design: Design scalable, reliable, and secure AI/ML platforms; define technical specifications for AI applications. Core ML Use Cases Computer Vision & NLP Mlops & Deployment Explainable AI (XAI)

Technical Lead (Enterprise AI Systems) We are seeking a Technical Lead to drive the design and development of our enterprise-grade AI product. This role requires a deep understanding of AI/ML systems, cloud-native architectures, and scalable software design. The applicant should have mandatorily worked on enterprise grade AI product or project for at least 2 - 3 years. This role includes at least 50% hands on development, 30% LLD and the remaining 20% of HLD / Architecture. The role is on the technical career path and is NOT suitable for project managers, technical managers, engineering managers etc. Key Responsibilities: Develop scalable, modular, and secure AI-driven software systems. Design APIs, microservices, and cloud infrastructure for AI workloads. Ensure seamless AI model integration and efficient scaling. Optimize performance & scalability of systems. Implement caching, load balancing, and distributed computing. Ensure security compliance (GDPR, SOC2) and secure data handling. Enforce best practices in authentication, encryption, and API security. Mandatory Skills & Qualifications: Experience: 8 - 12 years in software architecture & design, with at least 3+ years focused on AI/ML product design & development. Last 1 - 2 years should include deep Gen AI experience in enterprise grade AI projects or applications. Experience with design and developing applications with LLMs, RAG Applications, Gen AI Agents. Fine-tuning open-source LLMs. Experience with deploying and monitoring LLMs. Experience with LLM evaluation frameworks or tools like DeepEval, Langfuse or similar. Experience with frameworks like langchain, llamaindex, pydantic etc. Proficiency in RDBMS like PostgreSQL and knowledge of vector databases (eg: chromadb,Pinecone, FAISS, Weaviate). Strong background in microservices, RESTful & GraphQL APIs, event-driven architectures, Working experience with Kafka, RabbitMQ etc... Proficient in Python, Java for backend development Experience with TensorFlow, PyTorch, Hugging Face etc... Skilled in data processing (NumPy, Pandas, Dask,NLTK) and front-end (TypeScript, React.js). Experience in scalable AI systems, data pipelines, MLOps, and real-time inference. Strong understanding of security protocols, IAM, OAuth2, JWT, RBAC. Expertise in cloud platforms (AWS, GCP, Azure), Kubernetes, Docker. Hands-on experience with CI/CD (Eg: github,Jenkins etc...) Hands-on with system monitoring (Eg: ELK Stack,OpenTelemetry, Prometheus, Grafana). Nice-to-Have Skills: Ensuring AI model interpretability, fairness, and bias mitigation strategies. Optimizing model inference using quantization, distillation, and pruning techniques. Experience in deploying AI models in production at scale, including MLOps best practices. Designing AI systems with privacy-preserving techniques (differential privacy, homomorphic encryption etc.). Experience with knowledge graph-based AI applications.

Primary skills- GCP, Python CODING MUST, SQL Coding skills, Big Query, Dataflow, Airflow, Kafka and Airflow Dags. Bachelors Degree or equivalent experience in Computer Science or related field Required- Immediate or 15 days Job Description 3+ years experience as a software engineer or equivalent designing large data-heavy distributed systems and/or high-traffic web-apps Experience in at least one programming language (Python-2 yrs strong coding is must) or java. Hands-on experience designing & managing large data models, writing performant SQL queries, and working with large datasets and related technologies Experience designing & interacting with APIs (REST/GraphQL) Experience working with cloud platforms such as GCP, Big Query Experience in DevOps processes/tooling (CI/CD, GitHub Actions), using version control systems (Git strongly preferred), and working in a remote software development environment Strong analytical, problem solving and interpersonal skills, have a hunger to learn, and the ability to operate in a self-guided manner in a fast-paced rapidly changing environment Preferred: Experience using infrastructure as code frameworks (Terraform) Preferred: Experience using big data tools such as Spark/PySpark Preferred: Experience using or deploying MLOps systems/tooling (eg. MLFlow) Must have: Experience in pipeline orchestration (eg. Airflow) Must Have Experience in Data Flow 1 yr experience atleast Preferred: Experience using infrastructure as code frameworks (Terraform) Preferred: Experience in an additional programming language (JavaScript, Java, etc) Preferred: Experience using data science/machine learning technologies

Designation: Senior Software Engineer IExperience: 6-8 years Our Engineers are at the forefront of how customers interact with C2FO. Working with our Product team, we create applications for internal and external customers. Our philosophy: short iterations with a focus on scalability while ensuring maintainability. Essential Requirements Concerned with the success of their teammates as well as themselves. Respectful towards teammates regardless of their abilities. Collaborate with C2FO's Engineering, Product, DevOps, and operational stakeholders to define and refine requirements, break down work into smaller tasks, and provide accurate estimations. Work with minimal help on big slices of work/requirements that require high level and low level design and external dependencies while tracking and pushing the work through the process reliably. Take ownership of the codebase and process and contribute to any required improvements. Willing to debate, obtain, and move forward with the best solution. Always digging deeper to understand the problem space and the 'why' of your work. Vigilant in identifying tech debt and always improving how we do things. Persistent in facing roadblocks; dispatches them efficiently, pulling in others as necessary. Proficient communication in English, both written and verbal Comfortable with source control, especially GitHub. Engage in contributing to improve processes, take technical decisions, improve technical systems to steer the team towards the team and C2FO goals. Solicit feedback from peers, teammates, and managers to identify improvement areas and take steps to learn and grow. Passionate about testing, code quality, and continuous integration. Uphold our high engineering standards and consistently deliver quality code & documentation. Mentor and guide other engineers, creating a collaborative learning and working environment. Suggest, Share, plan for, and implement new tools or processes to help the team or engineering be more collaborative, effective, or efficient. Improve our documentation and document design decisions and rationale. Take part in the on-call rotations. Strong problem-solving skills. Basic Qualifications Minimum Six years of experience as a Software Engineer in developing products Bachelor's or higher degree or its equivalent Necessary Experience on: Backend programming languages such as Java, Python, or GoLang Relational database (PostgreSQL) RESTful or GraphQL APIs AWS or GCP or other Cloud Work experience in a microservice & event-driven architectures and messaging queues (e.g., RabbitMQ/Kafka/SNS) Work experience in building scalable, fault-tolerant, durable, highly available distributed Systems Strong understanding of System Design & Architecture Experience on building frontend to backend flow/feature Preferred Experience on : Nest.js/ Node.Js GoLang ORM tools Docker and Kubernetes Datadog or other similar tools

DS Key Responsibilities Combine expertise in mathematics statistics computer science and domain knowledge to create AIML models to solve various business challenges Collaborate closely with the AI Technical Manager and GCC Petro technical professionals and data engineers to integrate models into the business framework Identify and frame opportunities to apply advanced analytics modeling and related technologies to data to help businesses gain insight and improve decision making workflow and automation Understand and communicate the value of proposed opportunity with team members and other stakeholders Identify needed data and appropriate technology to solve identified business challenges Clean data and develop and test models Establish the life cycle management process for models Provide technical mentoring in modeling and analytics technologies the specifics of the modeling process and general consulting skills Drive innovation in AIML to enhance capabilities in data driven decision making Aligns with team on shared goals and outcomes recognizes others contributions and work collaboratively seek diverse perspectives Takes actions to develop self and others beyond existing skillset Encourages innovative ideas adapts to change and changing technologies Understand and communicate data insights and model behaviors to stakeholders with varying levels of technical expertise Required Qualification Minimum 5 years of experience in designing and developing AIML models and or various optimization algorithms 5 to 9 years of experience Solid foundation in mathematics probability and statistics with demonstrated depth of knowledge and experience in advanced analytics and data science methodologies eg supervised and unsupervised learning statistics data science model development Proficiency in Python and working knowledge of cloud AIML services Azure Machine Learning and Databricks preferred Domain knowledge relevant to the energy sector and working knowledge of Oil and Gas value chain eg upstream midstream or downstream and associated business workflows Proven ability to frame data science opportunities leverage standard foundational tools and Azure services to perform exploratory data analysis for purposes of data cleaning and discovery visualize data and identify actions to reach needed results Ability to quickly assess current state and apply technical concepts across cross functional business workflows Experience with driving successful execution deliverables and accountabilities to meet quality and schedule goals Ability to translate complex data into actionable insights that drive business val Demonstrated ability to engage and establish collaborative relationships both inside and outside immediate workgroup at various organizational levels across functional and geographic boundaries to achieve desired outcomes Demonstrated ability to adjust behavior based on feedback and provide feedback to other Team oriented mindset with effective communication skills and the ability to work collaboratively Strong problem solving skills and attention to detail Excellent communication and collaboration skills ML Engineer Key Responsibilities Transform data science prototypes into appropriate scale solutions in a production environment Orchestrate and configure infrastructure that assists Data Scientists and analysts in building low latency scalable and resilient machine learning and optimization workloads into an enterprise software product Combine expertise in mathematics statistics computer science and domain knowledge to create advanced AIML models Collaborate closely with the AI Technical Manager and GCC Petro technical professionals and data engineers to integrate and scale models into the business framework Identify data appropriate technology and architectural design patterns to solve business challenges using approved standard analytical tools and AI design patterns and architectures Partner with Data Scientists and IT Foundational services to implement complex algorithms and models into enterprise scale machine learning pipelines Run machine learning experiments and finetune algorithms to ensure optimal performance Consistently deliver complex innovative and complete solutions driving them through design planning development and deployment that simplify business processes and workflows to drive business value Work collaboratively with a large variety of different teams including data scientists data engineers and solution architects from various organizations within business units and IT Required Qualification Minimum 2 years experience in Object Oriented Design andor Functional Programming in Python 2 to 5 years of experience Mature software engineering skills such as source control versioning requirement spec architecture and design review testing methodologies CICD etc Must have a disciplined methodical minimalist approach to designing and constructing layered software components that can be embedded within larger frameworks or applications Experience implementing machine learning frameworks and libraries such as MLflow Experience with containers and container managements docker Kubernetes Experience developing cloud first solutions using Microsoft Azure Services including building machine learning pipelines in Azure Machine Learning and or Fabric Hands on experience in deploying machine learning pipelines with Azure Machine Learning SDK Working knowledge of mathematics primarily linear algebra probability statistics and algorithms Proficient at orchestrating largescale MLDL jobs leveraging big data tooling and modern container orchestration infrastructure to tackle distributed training and massive parallel model executions on cloud infrastructure Experience designing custom APIs for machine learning models for training and inference processes and designing implementing and delivering frameworks for MLOps Experience with model lifecycle management and automation to support retraining and model monitoring Experience implementing and incorporating ML models on unstructured data using cognitive services and or computer vision as part of AI solutions and workflows History of working with large scale model optimization and hyperparameter tuning applied to MLDL models Knowledge of enterprise SaaS complexities including security access control scalability high availability concurrency online diagnoses deployment upgrade migration internationalization and production support Knowledge of data engineering and transformation tools and patterns such as Databricks Spark Azure Data Factory Ability to engage other technical experts at all organizational levels and assess opportunities to apply machine learning and analytics to improve business workflows and deliver information and insight to support business decisions Ability to communicate in a clear concise and understandable manner both orally and in writing

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

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

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

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

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

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

Model Deployment and Management: Drive ML prototypes into production ensuring seamless deployment and management on cloud at scale. Monitor real-time performance of deployed models, analyze data, and proactively address performance issues. Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability. Collaboration and Integration: Collaborate with DevOps engineers to manage cloud compute resources for ML model deployment and performance optimization. Work closely with ML scientists, software engineers, data engineers, and other stakeholders to implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, and automated deployment. Innovation and Continuous Improvement: Stay updated with the latest advancements in MLOps technologies and recommend new tools and techniques. Contribute to the continuous improvement of team processes and workflows. Share knowledge and expertise to promote a collaborative learning environment. Development and Documentation: Build software to run and support machine-learning models. Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes. Participate in fast iteration cycles and adapt to evolving project requirements. Business Solutions and Strategy: Propose solutions and strategies to business challenges. Collaborate with Data Science team, Front End Developers, DBA, and DevOps teams to shape architecture and detailed designs. Mentorship: Conduct code reviews and mentor junior team members. Foster strong interpersonal skills, excellent communication skills, and collaboration skills within the team. Mandatory Skills: Programming Languages: Proficiency in Python (3.x) and SQL. ML Frameworks and Libraries: Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture. Databases: Proficiency in SQL and NoSQL databases. Mathematics and Algorithms: In-depth knowledge of mathematics, statistics, and algorithms. ML Modules and REST API: Proficient with ML modules and REST API. Version Control: Hands-on experience with version control applications (GIT). Model Deployment and Monitoring: Experience with model deployment and monitoring. Data Processing: Ability to turn unstructured data into useful information (e.g., auto-tagging images, text-to-speech conversions). Problem-Solving: Analytically agile with strong problem-solving capabilities. Learning Agility: Quick to learn new concepts and eager to explore and build new features.

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

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

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

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

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