Описание
The team builds agentic systems for a global hedge fund, combining LLMs, software engineering, data, search, and domain-specific tools to reason, retrieve, and execute in production.
Задачи
- Design and build agent workflows, orchestration, and execution frameworks
- Build retrieval and knowledge systems across structured and unstructured data
- Evaluate and refine agent performance and behavior
- Build distributed, production-grade services and APIs using Python, TypeScript, and cloud infrastructure
- Evaluate new models, frameworks, and tools for production use
- Investigate and resolve complex production issues across the stack
- Prototype, test, and refine ideas, then turn useful ones into production systems
- Work directly with investment professionals and influence the evolution of a mission-critical platform
Требования
- 7+ Years of software engineering experience
- Strong Python and .NET skills
- Hands-on skills in TypeScript and JavaScript
- Solid computer science fundamentals, including DSA
- Track record of building distributed, service-oriented systems
- Comfortable with concurrent programming, APIs, messaging, and graceful failure handling
- Experience with Docker, containers, cloud infrastructure, and modern deployment practices
- Knowledge of AI agent architecture, orchestration, and memory
- Familiarity with retrieval and grounding techniques such as RAG, embeddings, and hybrid search
- Understanding of how to evaluate, monitor, and improve agent behavior over time
- Understanding of planning, tool and model selection, and sandboxed execution
- Understanding of statistics and ML basics, including models, embeddings, inference, evaluation, and limitations of probabilistic systems; ML research background is not required
- English at C1 Advanced level
Будет плюсом
- Production experience with LLM or agent systems
- hands-on work with search, retrieval, and knowledge-representation technologies such as vector databases and knowledge graphs
- experience with distributed and event-driven infrastructure such as Kubernetes and messaging platforms
- familiarity with agent evaluation, observability, and secure execution tooling
- familiarity with financial market basics or willingness to learn them quickly
- deeper knowledge of derivatives, fixed income, credit, convertibles, or quantitative finance
- experience with domain-driven design or modeling complex business domains, open-source contributions, ML research, or independent technical projects
Условия
- Global relocation
- Benefits include recognition and evaluation, time off, training, well-being and work-life balance, health care, company events, internal mobility, a self-learning library, and CSR projects
- Benefits eligibility depends on the form of cooperation; benefits apply to employees under an employment contract
- Relocation is not available for all open positions
- At Luxoft Poland, remote work is available only from Poland