Remote, RemoteAbout the Company
The company builds an AI-powered workflow intelligence platform that helps organizations prevent revenue loss before it happens.
By analyzing API-enabled metadata from existing business systems, the platform automatically identifies misalignment between teams such as Sales, Customer Success, Legal, Finance, Product, and Operations — before deals stall, renewals fail, or contracts slip.
The solution requires:
The AI continuously learns what successful execution looks like within each organization and alerts teams in real time when dangerous deviations occur.
The founding team has previously built and scaled a B2B platform adopted by hundreds of financial institutions, which was later successfully acquired. Those lessons are now applied to execution, delivery, and scale. The product is supported by senior leaders from large enterprise and SaaS companies across fintech, HR, ERP, healthcare, and enterprise software.
Position Overview
We are looking for a Senior AI Backend Engineer to design, build, and scale the core systems behind a workflow intelligence platform.
The platform connects to customer systems, ingests large volumes of event-based metadata, reconstructs real business workflows, and applies machine learning to:
discover ideal process paths (“golden paths”)
detect anomalies
surface actionable insights
The system processes fragmented signals from multiple business systems (CRM, communication tools, internal platforms) and reconstructs unified workflows using techniques such as embeddings, similarity search, and probabilistic matching.
This is a hands-on senior role for someone equally strong in backend & distributed systems, data-intensive architectures, applied machine learning, experimentation & research, and production-grade reliability.
Core stack: Python, MongoDB, ClickHouse, RabbitMQ
Additional technologies used: C# (.NET Core / ASP.NET Core)
We’re looking for an engineer who deeply understands system architecture, large-scale data processing, and production ML systems, and is comfortable building, training, experimenting with, and deploying ML models into production.
Location: Ukraine or Europe (remote)
What You Will Do
Backend & Systems
Design and build backend services and APIs using Python (experience with C# / .NET Core is a plus)
Architect scalable, fault-tolerant, and observable systems for large-scale event ingestion, processing, and analytics
Build event-driven and asynchronous pipelines using RabbitMQ
Design data models and storage strategies using MongoDB and ClickHouse
Work with high-volume metadata streams from multiple external systems
Own system performance, reliability, and scalability
Implement observability: logging, metrics, tracing, and alerting
Troubleshoot production issues, perform root cause analysis, and implement long-term fixes
Data & Machine Learning
Design and implement end-to-end ML pipelines: ingestion → feature engineering → training → evaluation → deployment
Train and iterate on ML models for workflow discovery, anomaly detection, and behavioral pattern recognition
Work with large-scale event streams and metadata pipelines
Apply techniques such as embeddings, similarity search, probabilistic matching to link fragmented entities (tasks, communications, leads, opportunities) into unified business workflows
Run experiments, test hypotheses, and evaluate models using offline and online metrics
Conduct applied research and explore new modeling approaches
Build batch and near-real-time inference pipelines
Monitor model performance, drift, and degradation in production
Implement retraining strategies and model versioning
Required Qualifications
6+ years of experience in backend or systems engineering
Strong hands-on experience with Python
Experience building data-intensive systems (event ingestion, pipelines, analytics)
Experience processing large-scale event streams or metadata pipelines
Practical experience working with machine learning systems in production
Experience with:
model training
feature engineering
experimentation
model evaluation and monitoring
Familiarity with techniques such as embeddings, similarity search, or probabilistic matching
Ability to work with uncertain or fragmented data signals when reconstructing workflows across multiple systems
Experience with MongoDB, ClickHouse, or similar analytical databases
Experience with message brokers (RabbitMQ or similar)
Strong understanding of distributed systems concepts, including:
idempotency
retries
backpressure
eventual consistency
failure modes
Excellent analytical and problem-solving skills
High ownership mindset and ability to take responsibility for end-to-end solutions
Nice to Have
Experience with C# / .NET Core / ASP.NET Core
Experience with workflow analytics, process mining, or event-based systems
Experience with entity linking, similarity search, or graph-like data models
Familiarity with time-series or behavioral modeling
Experience with large-scale experimentation platforms
Understanding of MLOps practices
Experience with cost optimization for data and ML workloads
Experience with Kubernetes and cloud infrastructure
If you enjoy working at the intersection of systems, data, and intelligence, this role is a strong fit.
What They Offers
Operating Model & Expectations
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