Qureos Mentor

Saal.ai

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 Job Purpose:
 
 
 
 As a DevOps Engineer, you will be responsible for implementing and maintaining the systems and processes that enable efficient and automated software development, deployment, and operations. You will collaborate with cross-functional teams to enhance our development and operations lifecycle, enabling faster and more reliable software delivery.
 
 
 
 
 
 
 Job Specific Responsibilities:
 
 
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 Design, develop, and maintain a scalable and efficient infrastructure for software development, testing, and deployment.
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 Implement and maintain continuous integration and continuous delivery (CI/CD) pipelines, ensuring smooth and automated software releases.
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 Collaborate with software developers to streamline build, deployment, and release processes.
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 Handling deployments at customer premises.
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 Monitor and optimize system performance, ensuring high availability and scalability.
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 Implement and maintain configuration management tools and practices, ensuring consistency across environments.
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 Implement and maintain containerization and orchestration technologies such as Docker and Kubernetes.
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 Collaborate with security teams to implement and enforce best practices for infrastructure and application security.
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 Troubleshoot and resolve issues related to infrastructure, deployments, and performance.
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 Stay up-to-date with emerging technologies and industry trends related to DevOps and cloud computing.
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 Essential Skills:
 
 
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 Proven experience working as a DevOps Engineer or in a similar role.
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 Strong knowledge of software development methodologies and best practices.
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 Experience with cloud computing platforms such as AWS, Azure, or Google Cloud.
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 Proficiency in any of the scripting languages such as Python, Bash etc.
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 Experience with any of the CI/CD tools such as Jenkins, GitLab CI/CD, or CircleCI.
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 Hands-on experience with containerization and orchestration technologies like Docker and Kubernetes.
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 Understanding of networking concepts, including TCP/IP, DNS, load balancing, and firewalls.
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 Strong problem-solving skills and the ability to work independently as well as in a team environment.
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 Excellent communication and collaboration skills, with the ability to interact effectively with cross-functional teams.
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 Desirable Skills:
 
 
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 Certification in DevOps-related technologies (e.g., AWS Certified DevOps Engineer, Certified Kubernetes Administrator).
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 Experience with infrastructure-as-code tools like Terraform or CloudFormation.
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 Knowledge of monitoring and logging tools such as ELK Stack, Prometheus, or Grafana.
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 Understanding of Agile and DevOps methodologies.
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 Familiarity with database management systems like MySQL, PostgreSQL, and MongoDB.
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DevOps Engineer

<div>
 
 
 Job Purpose
 
 
 
 As a Data Scientist at SAAL.ai, you will apply machine learning and deep learning techniques to solve real-world business problems across numeric and text-based analytics. You will work across the full data science lifecycle&mdash;from data acquisition and feature engineering to model development and deployment&mdash;collaborating closely with engineering, product, and DevOps teams to deliver customer-centric AI solutions.
 
 
 
 
 
 
 Key Responsibilities
 
 
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 Develop and deliver high-quality machine learning and deep learning models across a broad range of analytics use cases.
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 Understand the practical scope and constraints of AI models within products and apply appropriate modeling techniques to ensure business relevance and technical robustness.
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 Acquire data from diverse sources, performing data exploration and preprocessing on structured and unstructured datasets, conducting feature engineering, evaluating algorithms and architectures, and iteratively refining models to improve performance.
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 Identify valuable data sources and automate data collection and preparation processes where possible.
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 Build, fine-tune, and maintain machine learning pipelines, integrate algorithms and packages into the platform, and contribute new models to the platform marketplace.
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 Collaborate closely with engineering and DevOps teams to support model deployment and operationalization.
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 Contribute to applied research activities, propose data-driven solutions to business challenges, and work with product, support, and client-facing teams to ensure successful rollout of AI solutions into trials and general availability.
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 Key Skills
 
 
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 Strong foundation in the theory and applied practice of machine learning and deep learning. Experience developing pipelines for structured and unstructured data.
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 Hands-on experience with deep learning frameworks such as TensorFlow and PyTorch, and machine learning libraries such as scikit-learn.
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 Proficiency in Python, with working knowledge of Java or R.
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 Experience developing and exposing models through RESTful APIs and containerized deployments using Docker.
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 Strong coding practices, including appropriate use of data structures and clean, maintainable code.
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 Applied experience in NLP, including the use of transformer-based models and libraries such as Hugging Face, spaCy, or Gensim. Familiarity with SQL, Pandas, Apache Spark, and data processing at scale.
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 Experience using ML lifecycle tools such as MLflow.
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 Strong analytical thinking, communication skills, and the ability to present technical concepts clearly to internal and external stakeholders.
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 Ability to work collaboratively across multidisciplinary teams.
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 Desirable Skills
 
 
 
 Experience working with large language models (LLMs), including offline deployment scenarios. Exposure to cloud-based environments (AWS, GCP, Azure). Experience with data visualization tools and collaborative environments such as Jupyter Notebooks and Git-based workflows. Domain experience with education or finance datasets is an advantage.
 
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Data Scientist

Join Saal.ai and explore 2   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

Saal.ai

About Company

DevOps Engineer

Data Scientist