Revenueroll

We’re looking for a Senior DevOps to join our growing team.
You’ll be responsible for building and operating a multi-cloud (AWS + GCP) infrastructure, driving observability and CI/CD governance, and owning our SOC 2 compliance automation.

This is a senior, hands-on position for someone who can combine infrastructure engineering, security, and automation — and enjoys owning systems end-to-end.

You’ll collaborate directly with our CTO and US engineering team (UTC-5 to UTC+2 overlap).

What You’ll Do:

Infrastructure & Automation

• Manage and automate AWS (EKS, EC2, IAM, S3) and GCP (GKE, Cloud Functions, BigQuery) environments.
• Maintain and expand Terraform infrastructure as code — from provisioning to drift detection.
• Migrate and optimize workloads such as Neo4j Fabric, Airbyte, Kafka, and dbt across clouds.
• Configure GPU-enabled EC2 and manage cost optimization (Spot + TCO tracking).

Observability & Monitoring

• Implement Sumo Logic + OpenTelemetry pipelines for structured logging and distributed tracing.
• Configure Slack-based alerting segmented by environment (dev, staging, prod).
• Maintain health and cost dashboards across AWS, GCP, and data platform services.

CI/CD Governance

• Own and enforce GitHub Actions pipelines (linting, coverage, schema validation).
• Integrate Codecov, SonarQube, and dbt tests into CI/CD flows.

Ensure reproducible builds and audit-ready logs (coverage.xml, results.xml).
Security & Secret Management

• Administer IAM roles and enforce MFA across AWS, GCP, GitHub, and Auth0.
• Manage secrets via 1Password CLI and scan codebases using TruffleHog and GitGuardian.

Configure Lambda/GCS malware scanning for file storage.
SOC 2 Compliance Automation

• Automate control evidence collection (logs, screenshots, IAM diffs).
• Integrate compliance checks with GitHub Actions, Slack, and Sumo Logic.
• Maintain structured audit folders (GCS/S3, Jira, Confluence).

Support quarterly access reviews and rotation monitoring.

Must-Have Qualifications:

• 5+ years experience in DevOps, SRE, or Cloud Infrastructure roles.
• Proven background in AWS and GCP operations, ideally multi-cloud.
• Proficient with Terraform, GitHub Actions, and IaC security best practices.
• Experience with Sumo Logic and OpenTelemetry SDKs (Node.js, Python, JS).
• Working knowledge of Neo4j, Airbyte, Kafka, dbt, and BigQuery.
• Comfortable implementing SOC 2 / ISO 27001 security and evidence frameworks.

Strong English communication (written and spoken).

Bonus Points:

• Experience with Prometheus/Grafana dashboards.
• Prior work with 1Password CLI, Auth0, or Salesbricks.

• Familiarity with AWS Config, GCP Policy Analyzer, or compliance-as-code tooling.

   

What You’ll Deliver in the First 90 Days

1. Migrate GKE → EKS workloads and automate environment provisioning via Terraform.
2. Set up centralized logging (Sumo Logic) and distributed tracing (OpenTelemetry).
3. Implement CI/CD enforcement (coverage, schema validation, secrets scanning).

Deliver a SOC 2 evidence automation framework (Slack alerts, audit exports, test reports).

Work Setup

• Full remote, flexible hours (overlap with US East Coast 3–4 hours/day).
• Direct collaboration with CTO and engineering leads.

Asynchronous-first communication via Slack, GitHub, Notion, and Jira.

What We Offer:

• We’re building a modern data & identity infrastructure powering eCommerce personalization.
• You’ll have full ownership of our infrastructure and compliance stack — no legacy baggage, no red tape.
• You’ll work with modern tools — Terraform, EKS, OTEL, dbt, Kafka — and have autonomy to improve them.

$100K budget for the role, with annual performance and bonus upside.
 

What else: 

• 4 weeks of paid vacation per year (with flexible scheduling)
• Unlimited sick leave — we trust your judgment and care about your health
• US Bank Holidays off — following the American holiday calendar
• A chance to work in a dynamic, multicultural team of professionals driven by a
• shared passion for product innovation and real impact
• A flat structure, no micromanagement, and full ownership of your work

• Remote-first culture and flexible working hours

   

How to Apply

Send your CV / GitHub profile and a short note describing:

• A project where you implemented observability or SOC 2 automation.
• Your favorite IaC or monitoring tool and why.

Our Interview Process Is Simple and Fast:

1. Recruitment Screening Call

General conversation about your background, English check, and Q&A from your side.

3. 1H Test task in  HackerRank

2. Initial call with CTO (30 min) in English

3. Technical deep dive Interview in English

4. Final Culture Fit Interview

General conversation in English with our CОO and Head of Delivery.

Why Join Us?

Be part of a product-first, data-driven startup reshaping martech

Work on modern tech with real ownership and creative freedom

Collaborate with senior-level professionals in a global, fast-moving environment

Ready to build the future of data-powered marketing with us?

Apply now and let’s talk!

About Tie

Tie is building the next generation of identity resolution and marketing intelligence. Our platform connects hundreds of millions of consumers across devices, browsers, and channels—without relying on cookies—to power higher deliverability, smarter targeting, and measurable revenue lift for modern marketing teams.

At Tie, AI is not a feature—it is a core execution advantage. We operate large-scale identity graphs, real-time scoring systems, and production ML pipelines that directly impact revenue, deliverability, and customer growth.

The Role

We are looking for a Senior AI / Machine Learning Engineer to design, build, and deploy production ML systems that sit at the heart of our identity graph and scoring platform. You will work at the intersection of machine learning, graph data, and real-time systems, owning models end to end—from feature engineering and training through deployment, monitoring, and iteration.

This role is highly hands-on and impact-driven. You will help define Tie’s ML architecture, ship models that operate at sub-second latency, and partner closely with platform engineering to ensure our AI systems scale reliably.

What You’ll Do

• Design and deploy production-grade ML models for identity resolution, propensity scoring, deliverability, and personalization
• Build and maintain feature pipelines across batch and real-time systems (BigQuery, streaming events, graph-derived features)
• Develop and optimize classification models (e.g., XGBoost, logistic regression) with strong handling of class imbalance and noisy labels
• Integrate ML models directly with graph databases to support real-time inference and identity scoring
• Own model lifecycle concerns: evaluation, monitoring, drift detection, retraining, and performance reporting
• Partner with engineering to expose models via low-latency APIs and scalable services
• Contribute to GPU-accelerated and large-scale data processing efforts as we push graph computation from hours to minutes
• Help shape ML best practices, tooling, and standards across the team

What You’ll Bring

Required Qualifications

• 5+ years of experience building and deploying machine learning systems in production
• Strong proficiency in Python for ML, data processing, and model serving
• Hands-on experience with feature engineering, model training, and evaluation for real-world datasets
• Ability to travel outside of Ukraine is a must
• Experience deploying ML models via APIs or services (e.g., FastAPI, containers, Kubernetes)
• Solid understanding of data modeling, SQL, and analytical workflows
• Experience working in a cloud environment (GCP, AWS, or equivalent)
• Experience with graph data, graph databases, or graph-based ML

• Familiarity with Neo4j, Cypher, or graph algorithms (community detection, entity resolution)

  Preferred / Bonus Experience

• Experience with XGBoost, tree-based models, or similar classical ML approaches
• Exposure to real-time or streaming systems (Kafka, Pub/Sub, event-driven architectures)
• Experience with MLOps tooling and practices (CI/CD for ML, monitoring, retraining pipelines)
• GPU or large-scale data processing experience (e.g., RAPIDS, CUDA, Spark, or similar)
• Domain experience in identity resolution, marketing technology, or email deliverability

Our Technology Stack

• ML & Data: Python, Pandas, Scikit-learn, XGBoost
• Graphs: Neo4j (Enterprise, GDS)
• Cloud: Google Cloud Platform (BigQuery, Vertex AI, Cloud Run, Pub/Sub)
• Infrastructure: Docker, Kubernetes, GitHub Actions
• APIs: FastAPI, REST-based inference services

What We Offer

• Competitive compensation, including salary, equity, and performance incentives
• Opportunity to work on core AI systems that directly impact revenue and product differentiation
• High ownership and autonomy in a senior, hands-on role
• Remote-first culture with a strong engineering and data focus
• Exposure to cutting-edge problems in identity resolution, graph ML, and real-time AI systems

• Clear growth path toward Staff / Principal IC roles

  What else:

• 4 weeks of paid vacation per year (flexible scheduling)
• Unlimited sick leave — we trust your judgment and care about your health
• US Bank Holidays off (American calendar)
• Remote-first culture and flexible working hours
• Flat structure, no micromanagement, and full ownership

• Opportunity to make a real impact during a critical growth phase

  Interview Process

• Recruitment Screening Call
• Initial call with Head of Data Science & AI and CTO  (30 min) in English
• Technical deep dive Interview (1,5h)  in English

• Optional test-task (paid)

  Why Join Us?

• High-impact delivery leadership role during a critical period
• Real ownership and autonomy
• Opportunity to shape delivery across the entire engineering organization
• Exposure to SaaS, data, integrations, automation, and platform work
• Collaboration with global teams and vendors
• A strong product with real scale and momentum

Why This Role Matters

At Tie, your work will not live in notebooks or experiments—it will power production systems used by real customers at scale. You will help define how AI is embedded into the company’s core platform and play a key role in making machine learning a durable competitive advantage.