Senior-Principal .NET Engineer (EST working hours)

Luxoft; Polska; On-site; PLN 180,000 - 240,000; Full time

Benefits offered by this job

Private medical insurance

Dental insurance

Life insurance

Internal mobility program

Rotation between projects

Job summary

Luxoft is seeking a self-directed software engineer to build the next generation of agentic systems for a leading hedge fund. You will design, prototype, and deploy distributed services using Python/TypeScript, AI agents, and cloud infrastructure.

You'll work directly with investment professionals, evaluate models, and contribute to production-grade workflows that reason, retrieve, and execute across data and tools. This dynamic role offers internal mobility between projects and locations.

Qualifications

• 7+ years of software engineering experience.
• Strong Python and .NET skills.
• A track record of building distributed, service-oriented systems.
• Comfort with concurrent programming, APIs, messaging, and handling failures gracefully.
• Experience with Docker, containers, cloud infrastructure, and modern deployment practices.
• Knowledge of how AI agents are designed, including architecture, orchestration, and memory.
• Familiarity with retrieval and grounding techniques such as RAG, embeddings, and hybrid search.
• An understanding of how to evaluate, monitor, and improve agent behavior over time.
• A grasp of planning, tool and model selection, and sandboxed execution.
• An understanding of statistics and ML basics: how models, embeddings, inference, and evaluation work, and where probabilistic systems fall short. No ML research background needed.

Responsibilities

• Designing and building agent workflows, orchestration, and execution frameworks.
• Building retrieval and knowledge systems across structured and unstructured data.
• Evaluating and refining agent performance and behavior.
• Building distributed, production-grade services and APIs (Python/TypeScript, cloud infrastructure).
• Evaluating new models, frameworks, and tools for production use.
• Investigating and resolving complex production issues across the stack.

Skills

Python

DotNet

Distributed systems

Concurrency

APIs

Messaging

Docker

Cloud

RAG

Model selection

Sandboxed execution

ML basics

Education

Bachelor's degree in CS

Tools

Kubernetes

TypeScript

Cloud infrastructure

APIs design

Job description

Private Medical & Dental care & Life Insurance covered

Internal Mobility program - possibility of rotation between projects, locations, accounts

...and more!

Project Description:

We're looking for a self-directed engineer to help build the next generation of agentic systems at a leading global hedge fund. You'll design and build systems that combine LLMs, software engineering, data, search, and domain-specific tools, and help shape how these systems reason, retrieve, and execute in production. This is an opportunity to solve hard engineering problems, work directly with investment professionals, and influence the evolution of a mission-critical platform.

Responsibilities:

• Designing and building agent workflows, orchestration, and execution frameworks
• Building retrieval and knowledge systems across structured and unstructured data
• Evaluating and refining agent performance and behavior
• Building distributed, production-grade services and APIs (Python/TypeScript, cloud infrastructure)
• Evaluating new models, frameworks, and tools for production use
• Investigating and resolving complex production issues across the stack

This is a hands-on engineering role. You will prototype ideas, test them, break them, understand why they broke, and turn the useful ones into production systems.

Mandatory Skills Description:

• 7+ years of software engineering experience
• Strong Python and .NET skills
• A track record of building distributed, service-oriented systems
• Comfort with concurrent programming, APIs, messaging, and handling failures gracefully
• Experience with Docker, containers, cloud infrastructure, and modern deployment practices
• Knowledge of how AI agents are designed, including architecture, orchestration, and memory
• Familiarity with retrieval and grounding techniques such as RAG, embeddings, and hybrid search
• An understanding of how to evaluate, monitor, and improve agent behavior over time
• A grasp of planning, tool and model selection, and sandboxed execution
• An understanding of statistics and ML basics: how models, embeddings, inference, and evaluation work, and where probabilistic systems fall short. No ML research background needed.

Nice-to-Have Skills Description:

• 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 like bonds, yields, options, volatility, and delta, or a willingness to learn them quickly. Deeper knowledge of derivatives, fixed income, credit, convertibles, or quantitative finance is a plus.
• Experience with domain-driven design or modeling complex business domains, turning real-world concepts and workflows into clean abstractions, data models, APIs, and agent capabilities
• Open-source contributions, ML research, or independent technical projects