Luxoft Poland
Senior Data/AI engineer
Senior data/AI engineer at Luxoft building reusable data-platform capabilities, pipelines, and data products for quantitative researchers in an investment-management context. Day to day: Python backend and large-scale data processing, AI-first development with LLM/agent frameworks (LangChain/LangGraph), data quality/lineage, and deploying to AWS.
- Compensation
- Not disclosed
- Location
- Poland
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Contract
- Experience
- 5+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 10+ years professional software development, incl. 5+ years data engineering
- Advanced Python, incl. scalable backend services and data processing
- Hands-on data pipelines, data integration, large-scale data processing architectures
- Relational databases and modern data storage: graph, vector, or analytical platforms
- Building and deploying AI/LLM-enabled applications in production
- LangGraph, LangChain, agentic workflows, or AI orchestration frameworks; evaluations
- Familiarity with modern AI tooling and AI-assisted software engineering
- Strong communication with stakeholders incl. business users
Nice to have
- Databricks, Spark, Delta Lake, or similar modern data engineering platforms
- Designing enterprise data lakes, lakehouses, or large-scale analytical platforms
- Domain experience in Investment Management, Asset Management, Capital Markets, or Financial Services
- Understanding of Fixed Income products and investment workflows
- Experience with Aurora, Neptune, and other AWS data services
- Production infrastructure on AWS: ECS Fargate, Lambda, SQS, cloud-native services
- Infrastructure-as-Code with AWS CDK or Terraform
Full description
Build reusable platform capabilities and data products that let quantitative researchers discover, access, and consume data across a complex, disparate landscape, and then accelerate the good prototypes to production.
- Design, build, and enhance data pipelines and integration frameworks supporting AI, analytics, and business applications.
- Improve data quality, reliability, lineage, and monitoring.
- Implement AI first approach: use AI agents as a primary tool to design, build, test, and support solutions
- Collaborate closely with business stakeholders, product managers, and engineering teams to translate business objectives into technical solutions.
Mandatory Skills Description:
- 10+ years of professional software development, including 5+ years of data engineering experience.
- Advanced proficiency in Python, including experience building scalable backend services and data processing solutions.
- Strong hands-on experience designing and implementing data pipelines, data integration solutions, and large-scale data processing architectures
- Experience working with relational databases and modern data storage technologies, including graph, vector, or analytical data platforms.
- Practical experience building and deploying AI/LLM-enabled applications in production environments.
- Experience with LangGraph, LangChain, agentic workflows, or orchestration frameworks for AI applications; evaluations.
- Familiarity with modern AI tooling and workflows, including and AI-assisted software engineering practices.
- Strong communication skills and ability to interact efficiently with various stakeholder groups including business users.
Nice-to-Have Skills Description:
- Experience with Databricks, Spark, Delta Lake, or similar modern data engineering platforms.
- Experience designing enterprise data lakes, lakehouses, or large-scale analytical platforms.
- Domain experience within Investment Management, Asset Management, Capital Markets, or Financial Services.
- Strong understanding of Fixed Income products and related investment workflows. Candidates with significant Fixed Income domain expertise will be considered highly valuable.
- Experience with Aurora, Neptune, and other AWS data services.
- Strong hands-on experience designing and implementing data pipelines, data integration solutions, and large-scale data processing architectures.
- Experience architecting, provisioning, and managing production infrastructure on AWS, including services such as ECS Fargate, Lambda, SQS, and related cloud-native technologies.
- Experience defining cloud architecture programmatically using Infrastructure-as-Code (IaC) tools such as AWS CDK or Terraform.