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As a Senior Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.

Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.


Key Job Responsibilities and Duties:

Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.

Dealing with massive textual sources to train GenAI foundation models.

Solving issues with data and data pipelines, prioritizing based on customer impact.

End-to-end ownership of data quality in our core datasets and data pipelines.

Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.

Providing tools that improve Data Quality company-wide, specifically for ML scientists.

Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.

Acting as an intermediary for problems, with both technical and non-technical audiences.

Promote and drive impactful and innovative engineering solutions.

Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation.

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

A leading fintech information provider, delivering digital solutions in labor law, taxation, finance and pensions.
We are looking for a Product Manager.
Key Responsibilities:
Implementation of the product's strategy and vision and make sure its aligned with the company's overall objectives and market trends in the legal industry.
Conduct in-depth market research and analysis to identify emerging trends, competitive landscape, product market fit, and customer needs within the legal technology space.
Define and prioritize the product roadmap based on strategic initiatives, customer feedback, and market opportunities, ensuring alignment with business goals and resources.
Lead cross-functional agile teams through the product development lifecycle, from ideation and requirements gathering to design, development, and launch, while maintaining focus on quality and timeliness.
Ensure that all product features and functionalities comply with relevant legal regulations, data privacy laws, and security standards.
Identify opportunities to embed AI capabilities into the product: automations, intelligent workflows, and decision-support features that add measurable value to customers.
Apply AI tools in day-to-day PM work: research, spec writing, discovery synthesis, backlog management, and stakeholder communication.

As a Machine Learning Engineer, you will work closely with experienced engineers and ML scientists to build scalable, production-grade GenAI applications. Your work will focus on designing, training, and deploying ML systems leveraging LLMs,, recommendation systems, and agent-based architectures, using state-of-the-art technologies. These solutions will directly power customer-facing experiences and play a key role in shaping the future of AI-driven travel products.

Key Job Responsibilities and Duties:

Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.

Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.

Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, chatbots, translation or other innovative applications.

Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.

Owning a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.

Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

We are looking for a talented and hands-on DevOps Engineer to join our team and play a key role in building, maintaining, and scaling our cloud infrastructure and delivery pipelines.
What youll do:
Design, implement, and maintain cloud infrastructure across AWS and Azure
Build and manage CI/CD pipelines using Azure DevOps
Develop and maintain Infrastructure as Code using Terraform and Terragrunt
Manage containerized environments using Docker
Administer and maintain Windows and Linux servers
Implement and manage monitoring, logging, and alerting systems using Grafana and related tools
Automate operational processes using PowerShell, Bash, and Python
Collaborate closely with development teams to improve performance, reliability, and system availability
Troubleshoot and resolve issues in complex production environments.