Design and build scalable .NET backend services and gRPC microservices; optimize data layers (SQL Server, MongoDB, Elasticsearch); implement cloud integrations on AWS and Azure; ship AI/ML features (LLMs, RAG, vector search, ranking, personalization) and operate MCP servers; contribute to CI/CD, observability, containerization, and production ML integrations impacting high-traffic consumer marketplaces.
Senior Backend Engineer - .NET and AI/ML
(Backend Systems • Distributed Architecture • gRPC Microservices • Cloud Services • AI/ML & LLM Engineering)
Full Time | Hybrid
Zonda is redefining the future of housing. We are perfectly placed in the heart of the fast-growing real estate industry. We are making big bets on the future of real-estate, trailblazing a 2030 vision for the industry. Here at Zonda, you’ll be able to use your passion and curiosity to drive the next generation of real estate analysts, advisors, and technologists.
We are hiring a Senior Backend Engineer with strong .NET and AI/ML expertise to design and build scalable, high-performance backend systems and AI-powered features for NewHomeSource.com and Livabl, two of North America's largest new-home and pre-construction marketplaces.
This is a dual-focus role: you will spend meaningful time on both core backend engineering (.NET, gRPC microservices, SQL Server, MongoDB, Elasticsearch, AWS, Azure) and AI/ML engineering (LLMs, RAG, vector search, MCP, ranking, personalization). You'll work across our API Gateway, gRPC microservices, Search Engine, MVC web applications, and MCP (Model Context Protocol) server to ship features that directly impact millions of homebuyers. Working hours align with the Eastern Time (EDT/EST) zone.
What You'll Do
Backend Engineering
- Design, develop, and maintain backend services, REST APIs, and gRPC microservices using .NET Framework and .NET Core / .NET 8+ in C#.
- Build and evolve gRPC service contracts using Protocol Buffers(.proto), including streaming, error handling, and versioning across services and API gateways.
- Build and optimize data layers across SQL Server, MongoDB, and Elasticsearch, including indexing, query tuning, sharding, and relevance tuning.
- Apply DDD, CQRS,MediatR, and Minimal API patterns; structure code into Aggregate Roots, Commands/Handlers, and endpoints consistent with our engineering conventions.
- Improve performanceviacaching, async patterns, distributed processing, and event-driven messaging.
- Build cloud integrations across AWS and Azure (compute, storage, queues, observability). Both clouds are equally important.
- Contribute to CI/CD pipelines, GitHub Actions workflows, automated testing, containerization (Docker), and deployment automation.
- Uphold code quality, observability, structured logging, and operational excellence.
AI/ML Engineering
- Design and ship AI/ML-powered backend features such as search ranking, recommendations, personalization, and intelligent listing experiences.
- Build and operate RAG pipelines, vector search, embeddings stores, and LLM-powered services, integrated cleanly into our .NET ecosystem.
- Build, extend, and maintain our MCP (Model Context Protocol) server, including designing MCP tools, managing context windows, and exposing backend capabilities to AI agents and assistants.
- Integrate ML models into production via inference APIs, ranking services, gRPC endpoints, and personalization endpoints.
- Use GA4 / Segment event data to power behavioral signals, personalization, and ranking features.
- Evaluate and apply emerging AI tooling (LLM providers, agent frameworks, vector DBs) to solve real product problems.
- Apply AI-assisted development tools (GitHub Copilot, Copilot Coding Agent, Copilot Spaces) in day-to-day engineering.
Who You Are
Backend Engineering
- 7+ years building production backend systems with .NET Framework and .NET Core / .NET 6+.
- Strong expertise in REST API design, distributed systems, and microservices architecture.
- Hands-on experience designing and operating gRPC services in production with Protocol Buffers(.proto), including service contracts, code generation, error handling, deadlines, and inter-service communication patterns.
- Deep SQL Server experience: complex queries, indexing, stored procedures, and performance tuning.
- Hands-on production experience with MongoDB and Elasticsearch.
- Production experience on both AWS and Azure (compute, queues, storage, observability).
- Solid grasp of Git/GitHub, CI/CD, Docker, automated testing, and DevOps fundamentals.
- Experience with high-traffic, performance-sensitive consumer web platforms.
AI/ML Engineering
- 2+ years of hands-on experience shipping AI/ML features into production, such as ranking, personalization, recommendations, or LLM-powered services.
- Strong working knowledge of LLMs, embeddings, vector databases, and RAG architectures.
- Hands-on experience with MCP (Model Context Protocol), including designing and configuring MCP tools, managing context windows, and integrating MCP servers into backend services.
- Experience integrating inference APIs and ML models into production backends.
- Familiarity with prompt engineering, evaluation, and guardrails for LLM-based systems.
- Experience with GA4 / Segment event data for personalization or ranking is a plus.
- Exposure to Python ML tooling (LangChain, Hugging Face, SageMaker, Vertex AI,MLflow) is a plus.
Preferred Qualifications
- Experience with search relevance, ranking, or recommendation systems at scale, especially on Elasticsearch.
- Experience modernizing legacy .NET (Framework to .NET Core / .NET 8+) or migrating monoliths to gRPC-based, cloud-native architectures.
- Familiarity with distributed caching (Redis), event-driven systems (Kafka / SNS / Azure Service Bus), API Gateways, and CQRS/MediatR.
- Experience building MCP servers, AI agents, or internal AI developer tools.
- Real estate, marketplace, or classifieds domain experience.
Tech Stack
- Backend: .NET Framework, .NET Core / .NET 8+, C#, ASP.NET Minimal APIs, gRPC, Protocol Buffers,MediatR
- Databases: SQL Server, MongoDB, Elasticsearch
- Cloud: AWS, Azure
- AI/ML: LLMs, RAG, Vector DBs, MCP (Model Context Protocol),LangChain,MLflow, Python (optional)
- DevOps: GitHub, GitHub Actions, Docker, CI/CD, observability tooling
- Analytics: GA4, Segment
- Frontend (adjacent awareness): Next.js, React, TypeScript
Why People Love Working Here
- We offer meaningful work and opportunities for career growth
- Competitive Salary
- 100% paid by Zonda health benefit package
- 100% Company paid Life Insurance coverage
- Paid vacation and general holidays
- Employee Assistance Program (EAP)
- Paid Volunteer Day
- Summer Fridays
- Live Meditation Sessions
- Employee Recognition Platform
- Virtual Wellness Program
- Visionary Leadership Team
Inclusion & Equal Opportunity Employment
Zonda is proud to be an Equal Opportunity Employer committed to diversity, inclusion & belonging. Here at Zonda, we are interested in every qualified candidate who is eligible to work in Canada.
Similar Jobs
Big Data • Fintech • Mobile • Payments • Financial Services
Build and scale Affirm’s managed cache platform using Redis and Valkey. Responsibilities include cluster migrations, capacity and shard planning, failover and replication, upgrades, self-service automation, monitoring, SLOs, incident response, and advising teams on caching strategy. The role also involves designing tested backend systems, collaborating across engineering and product teams, participating in on-call operations, and contributing to code reviews and team development.
Top Skills:
AWSDynamoDBKotlinKubernetesMySQLPythonRedisTerraformTidbValkey
Artificial Intelligence • Cloud • Consumer Web • Productivity • Software • App development • Data Privacy
Own company-critical technical problems and long-term strategy across multiple organizations. Architect scalable customer-facing systems and platforms, remain hands-on in software development, influence senior product and engineering leaders, establish engineering standards, and drive responsible AI adoption in products and workflows. The role requires deep expertise in large-scale software systems, consumer products, distributed systems, and production ML and LLM applications.
Top Skills:
Agentic FrameworksAIConcurrencyDatabasesDistributed SystemsFrontend SystemsLlm ApisMachine LearningMobile SystemsSearch SystemsStorage Systems
Cloud • Security • Software • Cybersecurity • Automation
Manage the AMER Support Engineering team, including hiring, mentorship, performance development, and technical growth. Lead customer support quality, process improvement, automation, AI adoption, and cross-functional collaboration. Build scalable workflows, promote team ownership and psychological safety, and ensure Support Engineers resolve complex technical issues throughout the support lifecycle. Partner with product and development teams to improve customer outcomes and support efficiency.
Top Skills:
AIChatopsDjangoGitlabLaravelLinuxMvcPHPPythonRuby On Rails
What you need to know about the Colorado Tech Scene
With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.
Key Facts About Colorado Tech
- Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
- Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
- Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
- Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute
