- Develop innovative solutions using data analysis - Lead Machine Learning projects - Coach junior data scientists - Design predictive models/algorithms - Apply machine learning for process optimization - Work with analytics software (SQL, R, Python) Required Candidate profile Technical skills : Machine Learning, Data Science, Python, Spark SQL, Microsoft Azure Cloud, Databricks, Mlflow Job Experience: 5+ years in Data Science/Machine Learning

About the role: Wells Fargo is seeking a Senior Business Execution Consultant. We believe in the power of working together because great ideas can come from anyone. Through collaboration, any employee can have an impact and make a difference for the entire company. Explore opportunities with us for a career in a supportive environment where you can learn and grow. In this role, you will: Lead support functions or operations for multiple business groups and contribute to large scale strategic initiatives Ensure efficiency, quality, cost effectiveness of solutions, and pipeline management relating to assigned operations Research moderately complex business, operational, and strategic initiatives that require analytical skills, basic knowledge of organizational strategy and Business Execution, and understanding of international business Work independently to make recommendations for support function by providing support and leadership Assist in the planning and execution of a variety of programs and initiatives that may include risk mitigation, efficiency, and customer experience Collaborate and consult with team leaders in developing project plans, policies and procedures Provide leadership in management of relationships and implementation of programs, services, and initiatives with cross functional business partners Required Qualifications: 4+ years of Business Execution, Implementation, or Strategic Planning experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: Expertise in Python and key/major frameworks like TensorFlow, PyTorch, and HuggingFace. Experience in using ML Ops tools (e.g., MLflow, Kubeflow) for scaling and deploying models in production. Experience working with on Google Cloud Platform and expertise in using the GCP services. Expertise in Scala for processing large-scale data sets for use cases. Proficiency in Java, JavaScript (Node.js) for back-end integrations and for building interactive AI-based web applications or APIs. Experience with SQL and NoSQL languages for managing structured, unstructured, and semi-structured data for AI and Generative AI applications. Critical thinking and strong problem-solving skills. Ability to learn the latest technologies and keep up with the trends in the Gen-AI space and apply to the business problems quickly. Ability to multi-task and prioritize between projects and able to work independently and as part of a team. Graduate degree from a top tier university (e.g., IIT, ISI, IIIT, IIM, etc.,) is preferred. Required to work individually or as part of a team on multiple AI and Generative AI projects and work closely with business partners across the organization. Mentor and coach budding Data Scientists on developing and implementing AI solutions. Perform various complex activities related to neural networks and transformer-based models. Provide analytical support for developing, evaluating, implementing, monitoring, and executing models across business verticals using emerging technologies. Expert knowledge on working on large datasets using SQL or NoSQL and present conclusions to key stakeholders. Establish a consistent and collaborative framework with the business and act as a primary point of contact in delivering the solutions. Experience in building quick prototypes to check feasibility and value to business. Expert in developing and maintaining modular codebase for reusability. Review and validate models and help improve the performance of the model under the preview of regulatory requirements. Work closely with technology teams to deploy the models to production. Prepare detailed documentations for projects for both internal and external use that comply with regulatory and internal audit requirements.

Please apply using the link here: https://saarthee.keka.com/careers/jobdetails/72540 Position Summary: We are seeking a skilled Machine Learning Engineer to design, develop, and deploy ML models for various applications, including recommendation systems. The ideal candidate will implement and optimize machine learning algorithms to enhance model performance and accuracy while ensuring that enterprise infrastructure and data pipelines are equipped to support scalable ML solutions. This role involves collaborating with ML engineers and stakeholders to translate business requirements into effective ML solutions. Responsibilities include designing scalable machine learning pipelines for data preprocessing, model training, and deployment, as well as enhancing model monitoring to track scalability and error rates. Additionally, the role focuses on ML model load testing, developing end-to-end test cases, and evaluating model scalability and latency under varying workloads. The candidate will automate test cases to ensure seamless model deployment and operation in production environments. Responsibilities: Design and develop machine learning models for various applications, such as recommendation systems. Deploy machine learning solutions to meet enterprise goals and support experimentation and innovation. Ensure that enterprise infrastructure and data pipelines are well-equipped to support machine learning solutions. Implement and optimize machine learning algorithms to improve model performance and accuracy. Collaborate with ML engineers, and stakeholders to understand business requirements and translate them into effective ML solutions. Design and implement scalable machine learning pipelines for data preprocessing, model training, and deployment. Enhance Monitoring of model scalability, and incident of increased error rate and ensure optimal results. Focus on ML model load testing and creation of E2E Test Cases. Evaluate models scalability and latency by running suites of metrics under different RPS and creating and automating the test cases for individual models, ensuring a smooth rollout of the models. Qualifications: Bachelor's or master's degree in computer science, Engineering, Mathematics, or a related field. Minimum of 3-8 years of experience in machine learning, with a proven track record of building, monitoring, large scale ML models. Strong problem-solving skills and understanding of recommendation system. Strong programming skills in languages like Python, Scala, and Java. Hands on experience in Databricks, mlFlow, Seldon, Kubeflow, Jenkins, Tecton. Familiarity with custom machine learning platforms, feature store and monitoring ML Models. Expertise in recommendation algorithms. Experience with software engineering principals and use of cloud services like AWS . Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams. Mandatory Skills: Databricks, ML flow, Seldon, Kubeflow, ML Load Testing, AWS Services, Tecton, Jenkins, Java/Python/Scala, Evaluate Scalability & latency of Models.

Job Title: Data/ML Platform Engineer Location: Gurgaon, Pune, Bangalore, Chennai, Bhopal, Jaipur, Hyderabad (Work from office) Notice Period: ImmediateiSource Services is hiring for one of their client for the position of Data/ML Platform Engineer. As a Data Engineer you will be relied on to independently develop and deliver high-quality features for our new ML Platform, refactor and translate our data products and finish various tasks to a high standard. Youll be part of the Data Foundation Team, which focuses on creating and maintaining the Data Platform for Marktplaats. 5 years of hands-on experience in using Python, Spark,Sql. Experienced in AWS Cloud usage and management. Experience with Databricks (Lakehouse, ML, Unity Catalog, MLflow). Experience using various ML models and frameworks such as XGBoost, Lightgbm, Torch. Experience with orchestrators such as Airflow and Kubeflow. Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes). Fundamental understanding of Parquet, Delta Lake and other data file formats. Proficiency on an IaC tool such as Terraform, CDK or CloudFormation. Strong written and verbal English communication skill and proficient in communication with non-technical stakeholderst Location - Gurgaon, Pune, Bangalore, Chennai, Bhopal, Jaipur, Hyderabad (Work from office)

We are seeking an experienced Generative AI Engineer to design, develop, and optimize AI models for text, image, video, and audio generation. The ideal candidate should have expertise in deep learning, natural language processing (NLP), transformer models (GPT, BERT, LLaMA, etc.), and multimodal AI. This role involves working with large-scale datasets, fine-tuning AI models, and deploying scalable AI solutions. Key Responsibilities: Design, develop, and fine-tune Generative AI models for text, image, video, and audio synthesis. Work with transformer architectures such as GPT, BERT, T5, Stable Diffusion, and CLIP. Implement and optimize LLMs (Large Language Models) using Hugging Face, OpenAI, or custom architectures. Develop AI-powered chatbots, virtual assistants, and content generation tools. Work with diffusion models, GANs (Generative Adversarial Networks), and VAEs (Variational Autoencoders) for creative AI applications. Optimize AI models for performance, inference speed, and cost efficiency in cloud or edge environments. Deploy AI models using TensorFlow, PyTorch, ONNX, and MLflow on AWS, Azure, or GCP. Work with vector databases (FAISS, Pinecone, Weaviate) and embedding-based search techniques. Fine-tune models using RLHF (Reinforcement Learning with Human Feedback) for better alignment. Collaborate with data scientists, ML engineers, and product teams to integrate AI capabilities into applications. Ensure AI model security, bias mitigation, and ethical AI practices. Stay updated with the latest advancements in Generative AI, foundation models, and prompt engineering. Required Skills & Qualifications: 6+ years of experience in AI, machine learning, and deep learning. Strong expertise in Generative AI models and transformer architectures. Proficiency in Python, TensorFlow, PyTorch, and Hugging Face libraries. Experience with NLP, text embeddings, and retrieval-augmented generation (RAG). Knowledge of vector databases, embeddings, and scalable model serving (FastAPI, Triton, Ray Serve). Experience with GPU acceleration (CUDA, TensorRT, ONNX optimization) for AI workloads. Familiarity with cloud-based AI services like AWS Bedrock, Azure OpenAI, or Google Vertex AI. Strong understanding of data preprocessing, annotation, and model evaluation metrics. Experience working with large-scale datasets and distributed training techniques. Strong problem-solving skills and ability to work in Agile/DevOps environments. Preferred Qualifications: Experience with multimodal AI (text-to-image, text-to-video, speech synthesis). Knowledge of RLHF, prompt engineering, and AI-assisted code generation. Certifications in Machine Learning, AI, or Cloud AI services.

Department : Market Innovation Level - Senior position Expertise in supervised, unsupervised, machine learning, deep learning, reinforcement learning, statistics techniques. Proficiency in Python, PyTorch, TensorFlow. Good to have knowledge of Bayesian inference, probability distribution, hypothesis testing, A/B testing, and time series forecasting. Hands-on experience with feature engineering and hyperparameter tuning. Experience with MLflow, Weights & Biases, DVC (Data Version Control). Ability to track model performance across multiple experiments and datasets. End-to-end ML lifecycle management from Data ingestion, preprocessing, feature engineering, model training, deployment, and monitoring. Expertise in CI/CD for ML, containerization (Docker, Kubernetes), and orchestration (Kubeflow). Good to have experience in automating data labelling and feature stores (Feast). Good to have data processing experience in Spark, Flink, Druid, Nifi. Good to have real-time data streaming in Kafka and other messaging systems Designing and optimizing data pipelines for structured data. Good to have hands-on experience with Pyspark. Strong SQL skills for data extraction, transformation, and query optimization. Atleast one database experience such as NOSQL database. Implementing parallel and distributed ML techniques for large-scale systems. Good to have knowledge of model explainability (SHAP, LIME), bias mitigation, and adversarial robustness. Experience in model drift monitoring. Good to have CUDA for GPU acceleration. Good to have choosing between CPU/GPU for different ML workloads (batch inference on CPU, training on GPU). Good to have scaling deep learning models on multi-GPU. Strong presentation skills, including data storytelling, visualization (Matplotlib, Seaborn, Superset), and report writing. Experience in mentoring data scientists, research associates, and data engineers. Contributions to research papers, patents, or open-source projects.

P3-C3-TSTSBechtel Corporation is seeking talented and ambitious big data engineer to join our Big Data and A.I. Center of Excellence (BDAC) team. BDAC designs, develops, and deploys industry-leading data science and big data engineering solutions, using Artificial Intelligence (AI), Machine Learning (ML), and big data platforms and technologies, to increase efficiency in the complex work processes, enable and empower data-driven decision making, planning, and execution throughout the lifecycle of projects, and improve outcomes to Bechtel organization and its customers. Who you are:You yearn to be part of groundbreaking projects that work to deliver world-class solutions on schedule. Someone who is motivated to find opportunity in and develop solutions for evolving challenges, is passionate about their craft, and driven to deliver exceptional results. You love to learn modern technologies and guide other engineers to raise the bar on your team. You are imaginative and engaged about intuitive user interfaces, as well as new/emerging concepts and techniques. Job Responsibilities:Big data design and analysis, data modeling, development, deployment, and CICD operations of big data pipelines Collaborate with a team of other data engineers, data scientists, and business subject matter experts to process data and prepare data sources. Mentor other data engineers to develop a world class data engineering team. Ingest, Process, and Model data from heterogeneous data sources to support Data Science projects. Basic Qualifications:Bachelors degree or higher in Computer Science, or equivalent degree and 3-10 years related working experience. In-depth experience with a big data cloud platform, preferably Azure. Strong grasp of programming languages (Python, PySpark, or equivalent) and a willingness to learn new ones. Experience writing database-heavy services or APIs. Experience building and optimizing data pipelines, architectures, and data sets. Working knowledge of queueing, stream processing, and highly scalable data stores Experience working with and supporting cross-functional teams. Strong understanding of structuring code for testability. Preferred Qualifications:Professional experience implementing and maintaining MLOps pipelines in MLflow or AzureML. Professional experience implementing data ingestion pipelines using Data Factory. Professional experience with Databricks and coding with notebooks. Professional experience processing and manipulating data using SQL and Python code. Professional experience with user training, customer support, and coordination with cross-functional teams.

Your Role Design and implement scalable, cloud-native MLOps infrastructure to support the entire ML lifecycle, from experimentation to production deployment Help develop IAC scripts for required Infra provisioning Establish best practices for ML infrastructure monitoring, logging, and observability Collaborate with MLOPS pipeline developers to understand their workflows and optimize infrastructure accordingly Drive technical decisions around tooling selection and infrastructure architecture Mentor junior engineers and provide technical leadership to the MLOps team Your Background Key ingredients for succeeding in this role are your: 7+ years of software engineering experience, with at least 3 years focused on ML/AI infrastructure Strong experience with cloud platforms (AWS/GCP/Azure) and container orchestration (Kubernetes) Expertise in infrastructure-as-code using tools like Terraform, CloudFormation Proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow, etc.) Deep understanding of ML workflows, including training, experimentation, and deployment Experience with MLOps tools (MLflow, Kubeflow, Seldon, etc.) and CI/CD platforms (Jenkins, GitLab CI, etc.) Strong background in distributed systems and scalable architecture design Experience with monitoring and observability tools

We are seeking a highly skilled and experienced Junior Gen AI Developer to join our innovative team. The ideal candidate will be responsible for designing, developing, and deploying advanced Generative AI models and applications, including LLMs, GANs, and other cutting-edge AI models. This role will play a key part in enhancing our product offerings, driving technical excellence, and implementing scalable AI solutions to meet complex business needs. What youll do: Model Development & Optimization Design, develop, and fine-tune Generative AI models, including transformer-based architectures, GANs, VAEs, image generation models and other advanced models. Implement techniques for model optimization, such as quantization, pruning, and distillation, to ensure efficient model performance. Experiment with different architectures, hyperparameters, and training methodologies to optimize model performance methodologies to optimize model performance Data Pipeline & Infrastructure Develop robust data pipelines to support generative model training and fine-tuning processes, ensuring data quality and availability. Work closely with MLOps and data engineering teams to streamline model deployment, monitoring, and version control. Application Development & Integration Build end-to-end Generative AI applications, from prototyping to deployment, ensuring seamless integration with existing systems. Design APIs and other interfaces for integrating GenAI models into various applications. Performance Monitoring & Model Improvement Set up monitoring tools and frameworks to track model performance and accuracy over time, implementing model retraining where necessary. Address issues related to model drift, retraining, and update requirements for enhanced model effectiveness. Research & Innovation Stay updated with the latest developments in GenAI, ML, and NLP, driving continuous innovation within the team. Present research findings within internal teams to contribute to our thought leadership. What to Have for this position. Must have Skills. Education : Bachelors or Masters in Computer Science, Data Science, AI/ML, or a related field. Experience : 2+ years in AI/ML, with at least 1 years in Generative AI model development and deployment. Certifications (optional): AI/ML certifications or training in GenAI frameworks is a plus. Technical Proficiency Strong expertise in GenAI frameworks and tools (e.g., Hugging Face, OpenAI API, DeepSpeed, DALL-E, and others). Proficient in programming languages such as Python and experience with ML libraries such as TensorFlow, PyTorch, Keras, etc. Solid understanding of transformer architectures, attention mechanisms, LLM fine-tuning, and reinforcement learning techniques. Familiarity with machine learning techniques and algorithms Data Engineering & MLOps Hands-on experience with data pipelines and working knowledge of MLOps tools like Docker, Kubernetes, MLflow, and cloud platforms (AWS/Azure/GCP). Proficient in managing large-scale datasets, implementing ETL processes, and ensuring data quality for training models. Problem-Solving & Analytical Skills Demonstrated ability to break down complex business requirements and design scalable GenAI solutions that address these needs. Strong skills in debugging, model interpretability, and addressing model bias. Communication & Collaboration Good verbal and written communication skills to explain technical concepts to stakeholders. Experience collaborating across multidisciplinary teams, including data engineers, MLOps, and business stakeholders. Preferred Skills : Experience with large language models, text generation, and language understanding tasks Experience with image generation and computer vision techniques Knowledge of reinforcement learning and its application to generative AI. Experience with model explainability, ethical AI, and interpretability techniques. Experience in deploying GenAI models for industry-specific applications (e.g., Insurance , healthcare, finance, customer service). Knowledge of frontend/backend frameworks and API development.

Hiring Data Engineer with expertise in AWS (Redshift, Glue), Spark, SQL, and Airflow to build scalable data pipelines, optimize ETL workflows, and process real-time streaming data. Experience in CI/CD, MLFlow, and Agile/Scrum preferred. Required Candidate profile Immediate joiners welcome!

Hiring Data Engineer with expertise in AWS (Redshift, Glue), Spark, SQL, and Airflow to build scalable data pipelines, optimize ETL workflows, and process real-time streaming data. Experience in CI/CD, MLFlow, and Agile/Scrum preferred. Required Candidate profile Immediate joiners welcome!

Role Description Location: Trivandrum / Kochi Experience: 3 to 5 years We are looking for flexible and talented individuals with a solid background in Machine Learning. You should ideally have 3-5 years of industry experience or 1-2 years of academic experience. You should be able to thrive in a collaborative environment and handle complex ML problems across different domains. Key Responsibilities Apply cutting-edge ML techniques to solve complex business problems.Problem framing: Deciding the right approach, whether it's Supervised, Self-Supervised, or Reinforcement Learning (RL).Data wrangling: Using techniques like Weak/Distant Supervision, Pseudo-labelling, and robust EDA (Exploratory Data Analysis).Model development: End-to-end modeling experience in ML, DL, and RL.Transfer Learning: Expertise in N-shot learning and fine-tuning pre-trained models.Work across various ML/DL verticals, including Time Series Modelling, Vision, NLP, and RL.Deploy ML/DL models into production, optimizing for performance and scalability.Collaborate with teams to deliver automated solutions and run experiments using MLOps tools like Kubeflow, Mlflow, Airflow, or SparkML.Maintain a GitHub portfolio with original ML repositories and contribute to the Kaggle community.Publish original first-author papers in reputed ML journals or conferences, or file patents related to AI or Automation. Must-Have Skills Programming: Expertise in Python or R.Applied ML Experience: Proven experience in problem framing, including when to use Supervised vs Self-Supervised or Reinforcement Learning (RL).Data Wrangling: Experience with techniques like Weak/Distant Supervision, Pseudo-labelling, EDA, and data augmentation.End-to-End Modeling: Solid experience in Machine Learning, Deep Learning, and Reinforcement Learning (RL).Transfer Learning: Strong experience with N-shot learning and fine-tuning pre-trained models.ML/DL Libraries: Proficiency in TensorFlow or PyTorch.MLOps: Hands-on experience with Kubeflow, Mlflow, Airflow, or SparkML for running and managing experiments.Model Deployment: Experience deploying ML models using tools such as TFServe, Seldon, or custom model serving techniques.GitHub/Kaggle Portfolio: Active GitHub repository with original contributions and a decent Kaggle leaderboard position.Problem-Solving: Strong problem-solving and mathematical skills, including optimization algorithms like SGD.Research: Experience in publishing 1st author papers in reputed ML conferences or journals. Good-to-Have Skills Computer Science/IT Background: A background in Computer Science or Information Technology.Statistics & Probability: Exposure to statistics and probability concepts.Docker & Kubernetes: Experience running dockerized code, as we are a Kubernetes-centric shop.Patents: AI or Automation-specific patents.Additional ML/DL Domains: Research or industry experience in other domains such as Vision, Time Series, or NLP. Education Bachelor's Degree in Computer Science, Engineering, or a related field. Skills Artificial Intelligence,Machine Learning,Deep Learning

As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day. We have 3.44 PB of RAM deployed across our fleet of C* servers - and this traffic is growing daily. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We’re also a mission-driven company. We cultivate a culture that gives every CrowdStriker both the flexibility and autonomy to own their careers. We’re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you. About The Role The charter of the Data + ML Platform team is to harness all the data that is ingested and cataloged within the Data LakeHouse for exploration, insights, model development, ML Engineering and Insights Activation. This team is situated within the larger Data Platform group, which serves as one of the core pillars of our company. We process data at a truly immense scale. Our processing is composed of various facets including threat events collected via telemetry data, associated metadata, along with IT asset information, contextual information about threat exposure based on additional processing, etc. These facets comprise the overall data platform, which is currently over 200 PB and maintained in a hyper scale Data Lakehouse, built and owned by the Data Platform team. The ingestion mechanisms include both batch and near real-time streams that form the core Threat Analytics Platform used for insights, threat hunting, incident investigations and more. As an engineer in this team, you will play an integral role as we build out our ML Experimentation Platform from the ground up. You will collaborate closely with Data Platform Software Engineers, Data Scientists & Threat Analysts to design, implement, and maintain scalable ML pipelines that will be used for Data Preparation, Cataloging, Feature Engineering, Model Training, and Model Serving that influence critical business decisions. You’ll be a key contributor in a production-focused culture that bridges the gap between model development and operational success. Future plans include generative AI investments for use cases such as modeling attack paths for IT assets. What You’ll Do Help design, build, and facilitate adoption of a modern Data+ML platformModularize complex ML code into standardized and repeatable componentsEstablish and facilitate adoption of repeatable patterns for model development, deployment, and monitoringBuild a platform that scales to thousands of users and offers self-service capability to build ML experimentation pipelinesLeverage workflow orchestration tools to deploy efficient and scalable execution of complex data and ML pipelinesReview code changes from data scientists and champion software development best practicesLeverage cloud services like Kubernetes, blob storage, and queues in our cloud first environment What You’ll Need B.S. in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field and 7 + years related experience; or M.S. with 5+ years of experience; or Ph.D with 6+ years of experience.3+ years experience developing and deploying machine learning solutions to production. Familiarity with typical machine learning algorithms from an engineering perspective (how they are built and used, not necessarily the theory); familiarity with supervised / unsupervised approaches: how, why, and when and labeled data is created and used3+ years experience with ML Platform tools like Jupyter Notebooks, NVidia Workbench, MLFlow, Ray, Vertex AI etc.Experience building data platform product(s) or features with (one of) Apache Spark, Flink or comparable tools in GCP. Experience with Iceberg is highly desirable.Proficiency in distributed computing and orchestration technologies (Kubernetes, Airflow, etc.)Production experience with infrastructure-as-code tools such as Terraform, FluxCDExpert level experience with Python; Java/Scala exposure is recommended. Ability to write Python interfaces to provide standardized and simplified interfaces for data scientists to utilize internal Crowdstrike toolsExpert level experience with CI/CD frameworks such as GitHub ActionsExpert level experience with containerization frameworksStrong analytical and problem solving skills, capable of working in a dynamic environmentExceptional interpersonal and communication skills. Work with stakeholders across multiple teams and synthesize their needs into software interfaces and processes. Experience With The Following Is Desirable GoIcebergPinot or other time-series/OLAP-style databaseJenkinsParquetProtocol Buffers/GRPC VJ1 Benefits Of Working At CrowdStrike Remote-friendly and flexible work cultureMarket leader in compensation and equity awardsComprehensive physical and mental wellness programsCompetitive vacation and holidays for rechargePaid parental and adoption leavesProfessional development opportunities for all employees regardless of level or roleEmployee Resource Groups, geographic neighbourhood groups and volunteer opportunities to build connectionsVibrant office culture with world class amenitiesGreat Place to Work Certified™ across the globe CrowdStrike is proud to be an equal opportunity employer. We are committed to fostering a culture of belonging where everyone is valued for who they are and empowered to succeed. We support veterans and individuals with disabilities through our affirmative action program. CrowdStrike is committed to providing equal employment opportunity for all employees and applicants for employment. The Company does not discriminate in employment opportunities or practices on the basis of race, color, creed, ethnicity, religion, sex (including pregnancy or pregnancy-related medical conditions), sexual orientation, gender identity, marital or family status, veteran status, age, national origin, ancestry, physical disability (including HIV and AIDS), mental disability, medical condition, genetic information, membership or activity in a local human rights commission, status with regard to public assistance, or any other characteristic protected by law. We base all employment decisions--including recruitment, selection, training, compensation, benefits, discipline, promotions, transfers, lay-offs, return from lay-off, terminations and social/recreational programs--on valid job requirements. If you need assistance accessing or reviewing the information on this website or need help submitting an application for employment or requesting an accommodation, please contact us at recruiting@crowdstrike.com for further assistance.