Company Overview :

Is a technology-driven firm specializing in building scalable data infrastructure and advanced analytical solutions. Operating at the intersection of big data engineering and machine learning, the company empowers enterprises to derive actionable intelligence from complex datasets. With a focus on high-performance computing and robust software architecture, Devlats supports clients across diverse sectors, including fintech, e-commerce, and logistics, by delivering reliable, production-grade data pipelines and intelligent systems.

Role Overview :

As a Data Science Engineer, you will bridge the gap between complex data modeling and scalable software engineering. You will be responsible for designing, deploying, and maintaining high-performance data systems that integrate advanced machine learning models into production environments. Working closely with cross-functional teams of data scientists, software engineers, and product managers, you will ensure that data-driven insights are translated into reliable business outcomes. This role is pivotal in scaling our internal data platforms and optimizing the performance of large-scale language models to meet the evolving needs of our global client base.

Key Responsibilities :

- Architect and implement end-to-end data pipelines that ingest, process, and store massive datasets to support real-time analytical requirements.

- Develop and optimize production-grade machine learning models, specifically focusing on LLM integration, to enhance automation and user experience.

- Collaborate with engineering teams to refactor prototype models into efficient, scalable codebases that maintain high availability and performance.

- Monitor and troubleshoot data infrastructure to identify bottlenecks, ensuring the integrity and reliability of data flowing into downstream applications.

- Translate complex technical requirements into actionable engineering tasks to ensure project milestones are met within defined timelines.

Required Skillset :

- Demonstrated expertise in Python for developing complex data applications and automating machine learning workflows.

- Proven ability to manage and process large-scale datasets using Big Data frameworks and distributed computing technologies.

- Hands-on experience in deploying and fine-tuning Large Language Models (LLMs) within production environments to solve real-world business problems.

- Strong analytical mindset with the ability to communicate technical findings effectively to non-technical stakeholders and cross-functional partners.

- Capability to work independently in a fully remote environment, maintaining high productivity and clear communication across distributed teams.

- Minimum of 5 - 8 years of professional experience in data engineering or applied data science roles.

- A degree in Computer Science, Engineering, or a related quantitative field is preferred.