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Requirements:
4+ years of experience with Backend Engineering in a high-level programming language (e.g. Java, C#, Golang)
Experience with cloud platforms (e.g. AWS, GCP, Azure)
Experience with microservices based architecture
Experience with databases and message queue technologies
Experience with continuous integration and deployment (CI/CD)
Bachelor of Science in Computer Science/Software Engineering or related field.

Requirements:
4+ years of experience writing code in a high-level programming language (e.g., Python, C#, C/C++, or Go, TypeScript).
Proficiency in SQL for data analysis, reporting, and driving data-driven decisions.
Strong problem-solving skills and ability to work with cross-functional teams.
Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and version control (e.g., Git).
Knowledge of DevOps practices (e.g., CI/CD, Docker).
Excellent communication skills and a stakeholder-focused mindset.
ADVANTAGE
Background in customer-facing roles (e.g., support engineering, customer engineering) with experience designing and implementing tools for those systems.
Experience growing into tool development from within customer-focused teams.

Requirements:
Hands-on proficiency in Linux, Windows, Python, Bash, Docker, Kubernetes, PowerShell, and a strong understanding of the OSI model.
Solid knowledge of networking concepts and cloud infrastructure.
Proven experience in applying cybersecurity best practices to real-world scenarios.
Self-motivated and capable of working both independently and collaboratively within a team.
Strong communication skills.

Requirements:
Bachelors or Masters degree in a relevant technical field, or equivalent hands-on experience in software development or DevOps.
3+ years of experience working as a Data Engineer, including end-to-end designing, orchestrating, and building cloud-based data pipelines (e.g., Airflow, Prefect, Dagster).
3+ years of experience with dimensional data modeling and data warehouse implementation, specifically MPP databases like BigQuery, Snowflake, and Redshift.
Strong knowledge of Python and Python-based data analysis tools such as Jupyter Notebooks and pandas.
Strong SQL writing skills. Ability to write highly performant queries.
Strong track record of executing projects independently in dynamic environments.
Fast understanding of data and business needs and ability to translate them into data models.
Team player with excellent communication skills.
Containerization (Docker): Essential for reproducible environments.
Knowledge of software engineering best practices: CI/CD concepts, code reviews, and unit testing.
ADVANTAGE
Production-level experience with dbt, including project design, transformation, testing, and documentation.
Infrastructure-as-Code (Terraform): Managing cloud resources (S3 buckets, IAM roles) via code.
CI/CD pipelines (GitHub Actions/Jenkins): Automating the testing and deployment of data models.

Requirements:
B.Sc. in Computer Science, Statistics, Mathematics, or a related field or equivalent high-level practical experience
5+ years of experience in leading and managing data science and machine learning projects, preferably in the intersection of AI and cybersecurity
Deep expertise in NLP and Classification - specifically in exploring and deriving signals from high-volume, unstructured datasets
Hands-on experience building complex agentic workflows using LLM frameworks such as LangChain or LangGraph
Exceptional "Failure Analysis" mindset, the ability to dive into data to understand why a model failed and how to fix it
Familiarity with distributed cloud systems and experience building production-grade ML solutions
Ability to work independently in a fast-paced, and come up with creative solutions to challenging problems
Excellent communication (both written and verbal) and presentation skill
Advantage: Knowledge of cybersecurity principles, attack vectors, and defense mechanisms.