Were on the hunt for an experienced DevOps Engineer with a strong focus on security to enhance our infrastructure and processes across Azure, AWS, and multi-cloud environments. Youll play a critical role in both DevOps and DevSecOps implementing security strategies, automating tasks with Python, Optimizing cost, and ensuring our systems are robust, secure, and scalable. If youre passionate about cloud solutions, have a proactive approach to security, and enjoy scripting, this is a great opportunity to make an impact in a collaborative team. What Youll Do: - Collaborate with development and infrastructure teams to design and implement effective CI/CD pipelines across Azure, AWS, and potentially GCP, with a focus on security. - Build and maintain secure infrastructure on Azure and AWS, leveraging core services like VMs, networking, and PaaS. - Develop and document security strategies, improving processes to include exception reporting and risk acceptance for review. - Conduct vulnerability assessments and provide continuous monitoring across multiple applications. - Integrate security scanning tools (e.g., SonarQube, Black Duck, Twistlock, Coverity) within CI/CD pipelines to identify and mitigate risks. - Utilize Infrastructure as Code (IaC) tools such as Terraform, ARM/Bicep, and Ansible to provision and secure cloud resources. - Write and maintain automation scripts in Python, PowerShell, Azure CLI, and AWS CLI, following security best practices. - Support API integrations and collaborate with API management platforms like Apigee or similar. - Deploy and manage containerized applications on Kubernetes (AKS/EKS) and Docker, ensuring both scalability and security. - Troubleshoot and optimize SQL queries and assist in debugging SQL-related issues. What Were Looking For: - Experience: 3-7 years in a DevOps/DevSecOps role, with hands-on experience in Azure and/or AWS. - Skills: Proficiency in Python for automation, API integrations, and cloud-native development with a security-conscious approach. - Security Knowledge: Experience with security software and vulnerability tools such as Black Duck, Twistlock, or Coverity. - CI/CD Expertise: Practical experience with CI/CD tools like Azure DevOps, AWS CodePipeline, or GitHub, with security scanning integration. - IaC Proficiency: Hands-on experience with Infrastructure as Code tools such as Terraform, ARM/Bicep, and Ansible. - Networking and Security: Strong networking knowledge, including virtual networks, VPNs, firewalls, and load balancers, with a focus on secure configurations. - SQL and JavaScript Skills: Competent in SQL for database troubleshooting, management and automation tasks. Youll Stand Out If You Also Have: - Multi-cloud experience, particularly with AWS, Azure, and/or GCP. - Deep knowledge of security standards and automated scanning for DevSecOps. - Advanced Kubernetes and Docker skills, especially with Azure AKS and AWS EKS for container orchestration. - Certifications: Azure certifications (AZ-104, AZ-305, AZ-400) and/or AWS certifications (AWS Certified Solutions Architect, AWS Certified DevOps Engineer) are highly valued.

We are seeking a highly skilled, hands-on Machine Learning Engineer/Data Scientist with a robust background in developing, coding, and deploying advanced ML models. The ideal candidate will have demonstrable expertise in computer vision, object detection, object tracking, and NLP, with a proven track record of writing production-grade code and developing state-of-the-art models to solve complex business challenges. Key Responsibilities: End-to-End ML Pipeline Development: Design, code, test, and deploy robust machine learning pipelinesfrom data collection and preprocessing to model training and production deployment. Computer Vision & Object Detection: Develop and fine-tune algorithms for computer vision applications using tools like Ultralytics YOLO and OpenCV, with hands-on experience in object detection and object tracking. NLP Model Development: Build and optimize NLP models for tasks such as text analysis, sentiment analysis, and language processing using frameworks like Hugging Face Transformers or spaCy. Cloud-Based Model Deployment: Utilize Google Cloud services, particularly Vertex AI, for scalable model development, training, testing, and deployment. Code Quality & Optimization: Write clean, modular, and efficient code; perform debugging, code reviews, and performance tuning to ensure scalable and maintainable codebases. Collaboration & Integration: Work closely with data engineering and software development teams to integrate ML models into production environments and ensure seamless deployment. Continuous Improvement: Stay up-to-date with the latest advancements in machine learning and AI, integrating new technologies and methods as appropriate. Documentation & Best Practices: Maintain thorough documentation of code, methodologies, and experiments, adhering to industry best practices and MLOps principles.Required Skills and Qualifications: Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field. 5+ years of professional experience in data science and machine learning with a strong emphasis on hands-on coding and model development. Advanced proficiency in Python, and extensive experience with ML libraries and frameworks such as TensorFlow, PyTorch, OpenCV, and Ultralytics YOLO. Demonstrable expertise in developing and deploying computer vision models (object detection and tracking) and NLP solutions. Hands-on experience with cloud platforms, especially Google Cloud (Vertex AI), for scalable ML model training and deployment. Strong analytical, debugging, and performance optimization skills, with a commitment to high-quality code and software engineering best practices. Familiarity with data annotation platforms (e.g., Roboflow) and experience in integrating these into model development workflows. Excellent communication and collaboration skills to work effectively in cross-functional teams. Preferred Skills: Experience with MLOps practices and CI/CD pipelines for machine learning. Familiarity with big data technologies and distributed computing frameworks. Contributions to open-source projects or publications in relevant fields. Expertise in model interpretability, monitoring, and performance tuning in production environments.

We are seeking a highly skilled, hands-on Machine Learning Engineer/Data Scientist with a robust background in developing, coding, and deploying advanced ML models. The ideal candidate will have demonstrable expertise in computer vision, object detection, object tracking, and NLP, with a proven track record of writing production-grade code and developing state-of-the-art models to solve complex business challenges. Key Responsibilities: End-to-End ML Pipeline Development: Design, code, test, and deploy robust machine learning pipelinesfrom data collection and preprocessing to model training and production deployment. Computer Vision & Object Detection: Develop and fine-tune algorithms for computer vision applications using tools like Ultralytics YOLO and OpenCV, with hands-on experience in object detection and object tracking. NLP Model Development: Build and optimize NLP models for tasks such as text analysis, sentiment analysis, and language processing using frameworks like Hugging Face Transformers or spaCy. Cloud-Based Model Deployment: Utilize Google Cloud services, particularly Vertex AI, for scalable model development, training, testing, and deployment. Code Quality & Optimization: Write clean, modular, and efficient code; perform debugging, code reviews, and performance tuning to ensure scalable and maintainable codebases. Collaboration & Integration: Work closely with data engineering and software development teams to integrate ML models into production environments and ensure seamless deployment. Continuous Improvement: Stay up-to-date with the latest advancements in machine learning and AI, integrating new technologies and methods as appropriate. Documentation & Best Practices: Maintain thorough documentation of code, methodologies, and experiments, adhering to industry best practices and MLOps principles. Required Skills and Qualifications: Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field. 5+ years of professional experience in data science and machine learning with a strong emphasis on hands-on coding and model development. Advanced proficiency in Python, and extensive experience with ML libraries and frameworks such as TensorFlow, PyTorch, OpenCV, and Ultralytics YOLO. Demonstrable expertise in developing and deploying computer vision models (object detection and tracking) and NLP solutions. Hands-on experience with cloud platforms, especially Google Cloud (Vertex AI), for scalable ML model training and deployment. Strong analytical, debugging, and performance optimization skills, with a commitment to high-quality code and software engineering best practices. Familiarity with data annotation platforms (e.g., Roboflow) and experience in integrating these into model development workflows. Excellent communication and collaboration skills to work effectively in cross-functional teams. Preferred Skills: Experience with MLOps practices and CI/CD pipelines for machine learning. Familiarity with big data technologies and distributed computing frameworks. Contributions to open-source projects or publications in relevant fields. Expertise in model interpretability, monitoring, and performance tuning in production environments. (Hands-On Coding & Model Development)

We are looking for a highly skilled and motivated Senior DevOps Engineer to join our growing team. This individual will play a critical role in building and maintaining our CI/CD pipelines, cloud infrastructure, and security automation. The ideal candidate brings hands-on expertise in both AWS and GCP environments, infrastructure-as-code with Terraform, and modern container orchestration technologies. Responsibilities: -Design, implement, and manage cloud infrastructure across AWS and GCP using Terraform -Deploy and maintain Kubernetes clusters and manage workloads using Helm/ArgoCD -Build and enhance secure, scalable CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI, and ArgoCD -Implement best practices in containerization using Docker, including image hardening and container runtime security -Integrate quality gates in CI/CD including automated testing, code coverage, and SonarQube configuration -Ensure secure CI/CD processes and apply container security standards -Collaborate with development, QA, and security teams to streamline DevSecOps culture -Contribute to system architecture, monitoring, and observability practices -Participate in incident response and root cause analysis. Must-Have Skills: -5+ years of experience in DevOps/SRE roles -Proficiency in AWS and Google Cloud Platform (GCP) -Strong hands-on experience with Terraform and Infrastructure as Code (IaC) -Solid experience with Kubernetes, Docker, Helm -CI/CD tools expertise: Jenkins, GitLab CI, GitHub Actions, ArgoCD -Experience with SonarQube and integrating static code analysis into pipelines -Familiarity with secure software development practices, including container and pipeline security -Strong knowledge of scripting in Python, Go, or Bash -Excellent communication and presentation skills, both written and verbal Good to Have: -Industry certifications (AWS/GCP Certified DevOps Engineer or similar) -Understanding of compliance frameworks such as ISO 27001, CIS Benchmarks, and NIST -Experience with monitoring tools (Prometheus, Grafana), and logging (ELK, Loki) Soft Skills: -Strong problem-solving abilities and troubleshooting mindset -Ability to mentor junior engineers and lead technical discussions -Comfortable presenting technical concepts to both technical and non-technical audiences