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описание
The client is a global investment management company headquartered in London. It manages over $228 billion in assets for institutional investors, pension funds, wealth managers, and other sophisticated clients worldwide, specializing in quantitative investing, alternative investments, systematic trading strategies, and technology-driven asset management.
задачи
Design and build production backend services and APIs in .NET/C# and/or Python for internal applications and AI agents;
Build data access and integration layers connecting AI-enabled applications with enterprise data sources;
Develop agent-consumable APIs and tools for controlled enterprise data discovery, retrieval, and interpretation;
Build and enhance data catalogue and metadata capabilities;
Implement ingestion and transformation pipelines for structured and semi-structured data;
Build automated data-quality controls;
Implement provenance and traceability mechanisms;
Build entitlement-aware data services;
Develop semantic and analytical services for AI applications;
Integrate with existing AI and data platforms following architecture, security, governance, and operational standards;
Design solutions for large-scale enterprise and on-premise data estates;
Implement automated testing and quality gates;
Implement monitoring and observability for services and pipelines;
Work with AI engineers, data engineers, platform teams, architects, and business stakeholders to convert requirements into technical capabilities;
Participate in solution design, technical reviews, and architectural discussions;
Support production readiness and continuous improvement, including performance optimization, troubleshooting, and technical debt reduction.
требования
5+ Years of production software engineering experience with strong hands-on expertise in .NET/C# and Python;
Strong experience designing and developing production APIs and backend services;
Experience with asynchronous processing and distributed systems;
Hands-on experience with data-intensive applications and data engineering;
Strong SQL skills and experience with relational databases and large enterprise datasets;
Experience designing data access and integration layers across heterogeneous enterprise sources;
Experience building metadata-driven services, catalogue integrations, or discovery capabilities;
Understanding of data modelling, metadata, data lineage, provenance, and data quality;
Experience implementing data validation and quality controls;
Experience designing secure and governed data access, including authentication, authorization, entitlements, and permission-aware APIs;
Strong understanding of clean architecture, SOLID principles, automated testing, CI/CD, code review, observability, and production support;
Experience with cloud and/or on-premise enterprise environments;
Comfortable using AI-assisted development tools and working with AI agents and LLM-based applications;
Understanding of tool/function calling, APIs, retrieval, and context management in LLM and agentic systems;
Ability to translate business and data requirements into scalable technical solutions;
Ability to work directly with architects, data engineers, platform engineers, and business stakeholders;
Fluent English;
Ability to operate independently within a client-facing, distributed engineering team;
Nice to have: Experience with Snowflake, Databricks, SQL Server, PostgreSQL or similar enterprise data platforms, services consumed by AI agents, MCP servers or other agent/tool integration frameworks, data catalogues, semantic layers, data dictionaries, metadata management or data governance platforms, embeddings, vector search, semantic search or RAG, event-driven architectures, messaging platforms and background processing, fine-grained access control or entitlement models, financial markets, investment data, fund reporting or reference data, financial services or another regulated environment.