KaaIoT

Requirements:

• Strong production experience with Vue.js
• Solid Python experience for backend development
• Proficiency with JavaScript / TypeScript for frontend development
• Hands-on experience with Three.js and Canvas-based rendering
• Demonstrable frontend performance optimization experience (page load time, rendering efficiency, working with large or real-time data sets)
• Ability to work highly autonomously, proactively clarify requirements and drive work forward with minimal supervision
• Strong communication skills and experience working in distributed teams (Europe/Ukraine time zone; ~1–2 hours of daily overlap with a US-based team)
• Hands-on experience with AI-assisted development tools — e.g. Claude Code and Codex — including AI skills, plugins, and MCP (Model Context Protocol), with a clear understanding of how to boost personal productivity and code quality using AI tooling.

Nice-to-Have:

• React experience — a planned Vue → React migration is in scope for the team, so familiarity with both frameworks is a strong plus
• Hands-on experience migrating a frontend from Vue to React
• Experience rendering large, real-time data streams in the browser
• Familiarity with deck.gl and Leaflet
• WebGL experience beyond Three.js abstractions
• Familiarity with C++ or general backend development.

Key Responsibilities:

• Build and maintain features across a portfolio of Vue-based projects
• Develop and support Python backend services
• Optimize frontend performance — faster page loads and more efficient rendering of heavy real-time 3D scenes and data
• Build and maintain visualization UI components that render synchronized 3D, lidar, and media data
• Contribute to the planned Vue → React migration as it ramps up
• Take ownership of features end-to-end, proactively clarifying requirements and assumptions
• Collaborate with a primarily US-based team with ~1–2 hours of daily overlap, while working largely independently
• Participate in code reviews and design discussions.

About the Project:

You'll join the engineering team of a leading technology company building autonomous mobility and robotics systems. The team develops an internal data visualization platform — essentially a "video and data player" that renders a 3D scene, a lidar feed, and an audio feed in sync, used to inspect and debug the data recorded by autonomous vehicles.

The work centers on a set of Vue-based projects, including the platform itself and supporting Python backend services, with a Vue → React migration on the roadmap as the broader engineering organization standardizes on React. This is a long-term engagement for an engineer who is comfortable across the stack, cares about clean and performant code, and can drive features from design to production with high ownership and minimal supervision.

Requirements:

• Strong backend development experience with C++ (the bulk of the backend is built on a custom in-house framework)
• Hands-on experience with AWS, particularly S3
• Proficiency with Protobuf
• Experience deploying and operating services on Kubernetes, including performance-driven traffic routing to pods
• Performance engineering skills — hash-based routing, SSD-based caching, and memory-mapped files
• Solid systems / low-level fundamentals (there is no off-the-shelf framework to rely on — much of the stack is custom)
• Ability to work highly autonomously and drive work forward with minimal supervision
• Strong communication skills and experience working in distributed teams (Europe/Ukraine time zone; ~1–2 hours of daily overlap with a US-based team)
• Hands-on experience with AI-assisted development tools — e.g. Claude Code and Codex — including AI skills, plugins, and MCP (Model Context Protocol), with a clear understanding of how to boost personal productivity and code quality using AI tooling.

Nice-to-Have:

• Familiarity with OpenTelemetry / observability tooling
• Understanding of LevelDB SSTable concepts and sorted, time-keyed data structures
• Familiarity with Python (used only for a small metadata/auth service)
• Basic DevOps skills.

Key Responsibilities:

• Optimize the performance of the C++ data-streaming backend — efficient delivery of large recorded datasets to the browser
• Work on infrastructure-level optimizations: traffic routing to Kubernetes pods, hash-based routing, SSD-based caching, and memory-mapped files
• Operate within a custom data-handling stack — a C++ framework, a custom HTTP server, and a proprietary data format built on top of LevelDB SSTables and Protobuf
• Take ownership of performance work end-to-end, proactively identifying bottlenecks and driving solutions independently
• Collaborate with a primarily US-based team with ~1–2 hours of daily overlap, while working largely independently
• Participate in code reviews and design discussions.

About the Project:

You'll join the engineering team of a leading technology company building autonomous mobility and robotics systems. The team develops an internal data visualization platform — a "video and data player" that renders 3D scenes, lidar, and audio recorded by autonomous vehicles. This role sits on the performance work stream: the backend reads on-vehicle data stored in S3, deserializes it from a custom, time-keyed format, and streams it to the browser. The data-transfer service is implemented in C++, and most of the platform's performance gains are expected to come from the backend.

This is a long-term engagement for a strong systems engineer who enjoys low-level performance work and is comfortable ramping on a heavily custom stack with high ownership and minimal supervision.

Requirements:

• Strong production experience with Vue.js
• Solid Python experience for backend development
• Proficiency with JavaScript / TypeScript for frontend development
• Hands-on experience with Three.js and Canvas-based rendering
• Demonstrable frontend performance optimization experience (page load time, rendering efficiency, working with large or real-time data sets)
• Ability to work highly autonomously, proactively clarify requirements and drive work forward with minimal supervision
• Strong communication skills and experience working in distributed teams (Europe/Ukraine time zone; ~1–2 hours of daily overlap with a US-based team)
• Hands-on experience with AI-assisted development tools — e.g. Claude Code and Codex — including AI skills, plugins, and MCP (Model Context Protocol), with a clear understanding of how to boost personal productivity and code quality using AI tooling

Nice-to-Have:

• React experience — a planned Vue → React migration is in scope for the team, so familiarity with both frameworks is a strong plus
• Hands-on experience migrating a frontend from Vue to React
• Experience rendering large, real-time data streams in the browser
• Familiarity with deck.gl and Leaflet
• WebGL experience beyond Three.js abstractions
• Familiarity with C++ or general backend development

Key Responsibilities:

• Build and maintain features across a portfolio of Vue-based projects
• Develop and support Python backend services
• Optimize frontend performance — faster page loads and more efficient rendering of heavy real-time 3D scenes and data
• Build and maintain visualization UI components that render synchronized 3D, lidar, and media data
• Contribute to the planned Vue → React migration as it ramps up
• Take ownership of features end-to-end, proactively clarifying requirements and assumptions
• Collaborate with a primarily US-based team with ~1–2 hours of daily overlap, while working largely independently
• Participate in code reviews and design discussions

About the Project:

You'll join the engineering team of a leading technology company building autonomous mobility and robotics systems. The team develops an internal data visualization platform — essentially a "video and data player" that renders a 3D scene, a lidar feed, and an audio feed in sync, used to inspect and debug the data recorded by autonomous vehicles.

The work centers on a set of Vue-based projects, including the platform itself and supporting Python backend services, with a Vue → React migration on the roadmap as the broader engineering organization standardizes on React. This is a long-term engagement for an engineer who is comfortable across the stack, cares about clean and performant code, and can drive features from design to production with high ownership and minimal supervision.

Requirements:

• Strong backend development experience with C++ (the bulk of the backend is built on a custom in-house framework)
• Hands-on experience with AWS, particularly S3
• Proficiency with Protobuf
• Experience deploying and operating services on Kubernetes, including performance-driven traffic routing to pods
• Performance engineering skills — hash-based routing, SSD-based caching, and memory-mapped files
• Solid systems / low-level fundamentals (there is no off-the-shelf framework to rely on — much of the stack is custom)
• Ability to work highly autonomously and drive work forward with minimal supervision
• Strong communication skills and experience working in distributed teams (Europe/Ukraine time zone; ~1–2 hours of daily overlap with a US-based team)
• Hands-on experience with AI-assisted development tools — e.g. Claude Code and Codex — including AI skills, plugins, and MCP (Model Context Protocol), with a clear understanding of how to boost personal productivity and code quality using AI tooling

Nice-to-Have:

• Familiarity with OpenTelemetry / observability tooling
• Understanding of LevelDB SSTable concepts and sorted, time-keyed data structures
• Familiarity with Python (used only for a small metadata/auth service)
• Basic DevOps skills

Key Responsibilities:

• Optimize the performance of the C++ data-streaming backend — efficient delivery of large recorded datasets to the browser
• Work on infrastructure-level optimizations: traffic routing to Kubernetes pods, hash-based routing, SSD-based caching, and memory-mapped files
• Operate within a custom data-handling stack — a C++ framework, a custom HTTP server, and a proprietary data format built on top of LevelDB SSTables and Protobuf
• Take ownership of performance work end-to-end, proactively identifying bottlenecks and driving solutions independently
• Collaborate with a primarily US-based team with ~1–2 hours of daily overlap, while working largely independently
• Participate in code reviews and design discussions

About the Project:

You'll join the engineering team of a leading technology company building autonomous mobility and robotics systems. The team develops an internal data visualization platform — a "video and data player" that renders 3D scenes, lidar, and audio recorded by autonomous vehicles. This role sits on the performance work stream: the backend reads on-vehicle data stored in S3, deserializes it from a custom, time-keyed format, and streams it to the browser. The data-transfer service is implemented in C++, and most of the platform's performance gains are expected to come from the backend.

This is a long-term engagement for a strong systems engineer who enjoys low-level performance work and is comfortable ramping on a heavily custom stack with high ownership and minimal supervision.

Requirements:

• Strong backend development experience with C++ (the bulk of the backend is built on a custom in-house framework)
• Hands-on experience with AWS, particularly S3
• Proficiency with Protobuf
• Experience deploying and operating services on Kubernetes, including performance-driven traffic routing to pods
• Performance engineering skills — hash-based routing, SSD-based caching, and memory-mapped files
• Solid systems / low-level fundamentals (there is no off-the-shelf framework to rely on — much of the stack is custom)
• Ability to work highly autonomously and drive work forward with minimal supervision
• Strong communication skills and experience working in distributed teams (Europe/Ukraine time zone; ~1–2 hours of daily overlap with a US-based team)
• Hands-on experience with AI-assisted development tools — e.g. Claude Code and Codex — including AI skills, plugins, and MCP (Model Context Protocol), with a clear understanding of how to boost personal productivity and code quality using AI tooling

Nice-to-Have:

• Familiarity with OpenTelemetry / observability tooling
• Understanding of LevelDB SSTable concepts and sorted, time-keyed data structures
• Familiarity with Python (used only for a small metadata/auth service)
• Basic DevOps skills

Key Responsibilities:

• Optimize the performance of the C++ data-streaming backend — efficient delivery of large recorded datasets to the browser
• Work on infrastructure-level optimizations: traffic routing to Kubernetes pods, hash-based routing, SSD-based caching, and memory-mapped files
• Operate within a custom data-handling stack — a C++ framework, a custom HTTP server, and a proprietary data format built on top of LevelDB SSTables and Protobuf
• Take ownership of performance work end-to-end, proactively identifying bottlenecks and driving solutions independently
• Collaborate with a primarily US-based team with ~1–2 hours of daily overlap, while working largely independently
• Participate in code reviews and design discussions

About the Project:

You'll join the engineering team of a leading technology company building autonomous mobility and robotics systems. The team develops an internal data visualization platform — a "video and data player" that renders 3D scenes, lidar, and audio recorded by autonomous vehicles. This role sits on the performance work stream: the backend reads on-vehicle data stored in S3, deserializes it from a custom, time-keyed format, and streams it to the browser. The data-transfer service is implemented in C++, and most of the platform's performance gains are expected to come from the backend.

This is a long-term engagement for a strong systems engineer who enjoys low-level performance work and is comfortable ramping on a heavily custom stack with high ownership and minimal supervision.

Requirements:

• Strong production experience with both Vue.js and React
• Proficiency with JavaScript / TypeScript for frontend development
• Hands-on experience with Three.js and Canvas-based rendering
• Demonstrable frontend performance optimization experience (page load time, rendering efficiency, working with large or real-time data sets)
• Comfortable working across the stack — willingness to occasionally pivot to backend tasks (backend is primarily C++ with a small Python service)
• Ability to work highly autonomously, proactively clarify requirements and drive work forward with minimal supervision
• Strong communication skills and experience working in distributed teams (Europe/Ukraine time zone; ~1–2 hours of daily overlap with a US-based team)
• Hands-on experience with AI-assisted development tools — e.g. Claude Code and Codex — including AI skills, plugins, and MCP (Model Context Protocol), with a clear understanding of how to boost personal productivity and code quality using AI tooling

Nice-to-Have:

• Hands-on experience migrating a frontend from Vue to React (strong plus — the team will be defining its migration strategy together with this engineer)
• Experience rendering large, real-time data streams in the browser
• Familiarity with deck.gl and Leaflet
• WebGL experience beyond Three.js abstractions
• Familiarity with C++ or general backend development

Key Responsibilities:

• Help drive the migration of the application's frontend from Vue.js to React, including advising the team on transition strategy
• Optimize frontend performance — faster page loads and more efficient rendering of heavy real-time 3D scenes and data
• Build and maintain visualization UI components that render synchronized 3D, lidar, and media data
• Take ownership of features end-to-end, proactively clarifying requirements and assumptions
• Collaborate with a primarily US-based team with ~1–2 hours of daily overlap, while working largely independently
• Participate in code reviews and design discussions

About the Project:

You'll join the engineering team of a leading technology company building autonomous mobility and robotics systems. The team develops an internal data visualization platform — essentially a "video and data player" that renders a 3D scene, a lidar feed, and an audio feed in sync, used to inspect and debug the data recorded by autonomous vehicles.

The current focus spans two directions: optimizing the platform's performance and migrating the frontend from Vue.js to React, as the broader engineering organization standardizes on React. This is a long-term engagement for an engineer who is comfortable across the stack, cares about clean and performant code, and can drive features from design to production with high ownership and minimal supervision.

Requirements:

• Strong production experience with both Vue.js and React
• Proficiency with JavaScript / TypeScript for frontend development
• Hands-on experience with Three.js and Canvas-based rendering
• Demonstrable frontend performance optimization experience (page load time, rendering efficiency, working with large or real-time data sets)
• Comfortable working across the stack — willingness to occasionally pivot to backend tasks (backend is primarily C++ with a small Python service)
• Ability to work highly autonomously, proactively clarify requirements and drive work forward with minimal supervision
• Strong communication skills and experience working in distributed teams (Europe/Ukraine time zone; ~1–2 hours of daily overlap with a US-based team)
• Hands-on experience with AI-assisted development tools — e.g. Claude Code and Codex — including AI skills, plugins, and MCP (Model Context Protocol), with a clear understanding of how to boost personal productivity and code quality using AI tooling

Nice-to-Have:

• Hands-on experience migrating a frontend from Vue to React (strong plus — the team will be defining its migration strategy together with this engineer)
• Experience rendering large, real-time data streams in the browser
• Familiarity with deck.gl and Leaflet
• WebGL experience beyond Three.js abstractions
• Familiarity with C++ or general backend development

Key Responsibilities:

• Help drive the migration of the application's frontend from Vue.js to React, including advising the team on transition strategy
• Optimize frontend performance — faster page loads and more efficient rendering of heavy real-time 3D scenes and data
• Build and maintain visualization UI components that render synchronized 3D, lidar, and media data
• Take ownership of features end-to-end, proactively clarifying requirements and assumptions
• Collaborate with a primarily US-based team with ~1–2 hours of daily overlap, while working largely independently
• Participate in code reviews and design discussions

About the Project:

You'll join the engineering team of a leading technology company building autonomous mobility and robotics systems. The team develops an internal data visualization platform — essentially a "video and data player" that renders a 3D scene, a lidar feed, and an audio feed in sync, used to inspect and debug the data recorded by autonomous vehicles.

The current focus spans two directions: optimizing the platform's performance and migrating the frontend from Vue.js to React, as the broader engineering organization standardizes on React. This is a long-term engagement for an engineer who is comfortable across the stack, cares about clean and performant code, and can drive features from design to production with high ownership and minimal supervision.

Office Location: 40G, Kyryllivska St, Kyiv, Ukraine (remote work is available)

Requirements:

• Strong production experience with both Vue.js and React
• Proficiency with JavaScript / TypeScript for frontend development
• Hands-on experience with Three.js and Canvas-based rendering
• Demonstrable frontend performance optimization experience (page load time, rendering efficiency, working with large or real-time data sets)
• Comfortable working across the stack — willingness to occasionally pivot to backend tasks (backend is primarily C++ with a small Python service)
• Ability to work highly autonomously, proactively clarify requirements and drive work forward with minimal supervision
• Strong communication skills and experience working in distributed teams (Europe/Ukraine time zone; ~1–2 hours of daily overlap with a US-based team)

• Hands-on experience with AI-assisted development tools — e.g. Claude Code and Codex — including AI skills, plugins, and MCP (Model Context Protocol), with a clear understanding of how to boost personal productivity and code quality using AI tooling

   

Nice-to-Have:

• Hands-on experience migrating a frontend from Vue to React (strong plus — the team will be defining its migration strategy together with this engineer)
• Experience rendering large, real-time data streams in the browser
• Familiarity with deck.gl and Leaflet
• WebGL experience beyond Three.js abstractions

• Familiarity with C++ or general backend development

   

Key Responsibilities:

• Help drive the migration of the application's frontend from Vue.js to React, including advising the team on transition strategy
• Optimize frontend performance — faster page loads and more efficient rendering of heavy real-time 3D scenes and data
• Build and maintain visualization UI components that render synchronized 3D, lidar, and media data
• Take ownership of features end-to-end, proactively clarifying requirements and assumptions
• Collaborate with a primarily US-based team with ~1–2 hours of daily overlap, while working largely independently

• Participate in code reviews and design discussions

   

About the Project:

You'll join the engineering team of a leading technology company building autonomous mobility and robotics systems. The team develops an internal data visualization platform — essentially a "video and data player" that renders a 3D scene, a lidar feed, and an audio feed in sync, used to inspect and debug the data recorded by autonomous vehicles.

The current focus spans two directions: optimizing the platform's performance and migrating the frontend from Vue.js to React, as the broader engineering organization standardizes on React. This is a long-term engagement for an engineer who is comfortable across the stack, cares about clean and performant code, and can drive features from design to production with high ownership and minimal supervision.

We are looking for a practical security engineer who can improve detection quality, investigate real incidents, and help reduce manual SOC work.

This role is not about building an “AI security platform”. The focus is on better visibility, cleaner detections, faster investigations, and useful automation.

What You Will Work On

Threat Hunting

Run targeted hunts based on clear hypotheses, including:

• Suspicious cloud activity
• Abnormal IAM or API usage
• Unusual network or application behavior
• Weak or missing detection coverage
• Gaps in logging and telemetry

You will use internal data and external threat intelligence to validate risks and improve detection logic.

Security Incident Investigation

Investigate security alerts and incidents end-to-end:

• Understand what happened
• Validate whether the alert is real or noisy
• Identify affected users, systems, and data
• Collect evidence from logs and infrastructure
• Document findings clearly
• Recommend fixes or detection improvements

Expected experience: 2+ years of hands-on security incident investigation.

Detection Engineering

Improve existing detections and create new ones where needed:

• Reduce false positives
• Improve signal quality
• Tune alerts based on real data
• Validate whether detections catch meaningful activity

• Map useful detections to MITRE ATT&CK where appropriate

   

Automation and Internal Tooling

Build small but useful tools to reduce repetitive work:

• Scripts for log analysis
• Alert enrichment
• Investigation helpers
• Data normalization
• Tool integrations

• Basic SOAR-style automation

   

Practical AI / LLM Usage

Use LLMs only where they bring practical value:

• Alert enrichment
• Investigation summaries
• Internal tooling
• Analyst productivity

No hype. No AI-first positioning. AI is just one tool in the workflow.

Requirements

• Solid experience with SIEM tools, such as:   - Elastic   - Splunk   - Microsoft Sentinel   - Similar platforms
• 2+ years investigating real security incidents
• Strong understanding of:   - AWS logs and IAM activity   - Cloud security basics   - Network fundamentals   - APIs, authentication flows, and modern application behavior
• Comfortable working with:   - Large datasets   - Raw logs   - Noisy alerts   - Incomplete telemetry
• Python or similar language for data processing
• Experience with automation:   - SOAR   - Scripts   - Internal tools   - Custom workflows

• Basic DevOps knowledge:   - AWS   - Linux   - Docker   - Kubernetes basics   - CI/CD basics   - Cloud infrastructure concepts   - Logs, metrics, and monitoring

   

Nice to Have

• Experience applying simple ML or statistical methods to logs
• Familiarity with LLMs in internal tooling
• Experience with data pipelines:   - Kafka   - Spark   - Similar systems
• MITRE ATT&CK mapping
• Detection-as-code experience

• Understanding of infrastructure monitoring:   - Prometheus   - Grafana   - OpenTelemetry

   

What Will Make You Effective Here

You will be effective in this role if:

• You do not trust alerts blindly
• You care about signal quality, not the number of detections
• You can explain why a detection is noisy or useless
• You understand systems, not just security tools
• You are comfortable reading raw logs
• You prefer building automation once instead of repeating manual work
• You can work with engineers and explain security findings in practical terms

About the company

Our client is a leading autonomous vehicle technology company operating at the cutting edge of self-driving software development. 

Role overview

We are not looking for a traditional SOC analyst. This role is about building detection systems, not clicking through alerts. You will work with large volumes of logs (cloud, application, network), design detection logic, and improve it over time. That includes both rule-based detection (SIEM/XDR) and data-driven approaches (statistical models, anomaly detection, simple ML where it makes sense). You should be comfortable treating logs as a data problem, not just a monitoring stream.

Important! This is not a role where you:

• sit in SIEM all day clicking alerts
• blindly add rules
• overcomplicate things with ML

This is a role where you: build detection systems that work in practice.

What you'll be doing

Detection & analytics

• Build and maintain detections across:
• application logs (auth, APIs, business logic abuse)
• cloud activity (CloudTrail, IAM, infrastructure changes)
• network telemetry (DNS, proxy, firewall, NetFlow)
• Work directly with data in platforms like Elasticsearch / Splunk / similar
• Write and tune detection logic (queries, correlation rules)
• Reduce noise — expect to spend time fixing false positives, not just adding new rules.

Working with data (this is a big part of the job)

• Build pipelines for:
• log ingestion
• normalization
• feature extraction
• Work with streaming or batch pipelines (Kafka / queues / scheduled jobs)
• Turn raw logs (e.g. CloudTrail) into structured data usable for detection.

Anomaly detection / ML (practical, not theoretical)

• Apply simple models where they add value (e.g. One-Class SVM or similar approaches)
• Focus on:
• behavior baselining
• anomaly scoring
• Tune models based on real output (false positives matter more than theory) 
• If ML does not improve signal — don't use it.

Automation & response

• Build response playbooks for common cases
• Automate where it actually saves time (not for the sake of it)
• Integrate with SOAR or write lightweight orchestration
• Improve:
• detection speed
• response consistency.

AI usage (realistic expectations)

• Use LLMs for:
• alert enrichment
• investigation summaries
• internal tooling
• Not building an "AI security platform"
• Focus on practical gains, not hype.

Threat hunting

• Run targeted hunts based on hypotheses
• Use internal data + external signals
• Identify gaps in logging and detection.

Requirements

• Solid experience with SIEM (Elastic, Splunk, Sentinel, etc.)
• Strong understanding of:
• cloud activity (especially AWS logs and IAM)
• network basics
• modern application behavior (APIs, auth flows)
• Comfortable working with large datasets (not afraid of raw logs)
• Python or similar for data processing
• Experience with automation (SOAR or custom scripts).

Nice-to-have (but not critical)

• Experience applying ML to logs (even simple models is enough)
• Familiarity with LLMs in tooling (not research)
• Experience with data pipelines (Kafka, Spark, etc.)
• MITRE ATT&CK mapping

What will make you effective here

• You don't trust alerts blindly
• You care about signal quality, not number of detections
• You can debug why a detection is noisy or useless
• You understand systems, not just tools
• You prefer building something once instead of repeating manual work.

How success looks like

• Fewer false positives
• Better visibility across systems
• Faster investigations
• Less manual work in SOC
• Detections that actually catch real issues

We are looking for a practical security engineer who can improve detection quality, investigate real incidents, and help reduce manual SOC work.

This role is not about building an “AI security platform”. The focus is on better visibility, cleaner detections, faster investigations, and useful automation.

What You Will Work On

Threat Hunting

Run targeted hunts based on clear hypotheses, including:

• Suspicious cloud activity
• Abnormal IAM or API usage
• Unusual network or application behavior
• Weak or missing detection coverage
• Gaps in logging and telemetry

You will use internal data and external threat intelligence to validate risks and improve detection logic.

Security Incident Investigation

Investigate security alerts and incidents end-to-end:

• Understand what happened
• Validate whether the alert is real or noisy
• Identify affected users, systems, and data
• Collect evidence from logs and infrastructure
• Document findings clearly
• Recommend fixes or detection improvements

Expected experience: 2+ years of hands-on security incident investigation.

Detection Engineering

Improve existing detections and create new ones where needed:

• Reduce false positives
• Improve signal quality
• Tune alerts based on real data
• Validate whether detections catch meaningful activity
• Map useful detections to MITRE ATT&CK where appropriate

Automation and Internal Tooling

Build small but useful tools to reduce repetitive work:

• Scripts for log analysis
• Alert enrichment
• Investigation helpers
• Data normalization
• Tool integrations
• Basic SOAR-style automation

Practical AI / LLM Usage

Use LLMs only where they bring practical value:

• Alert enrichment
• Investigation summaries
• Internal tooling
• Analyst productivity

No hype. No AI-first positioning. AI is just one tool in the workflow.

Requirements

• Solid experience with SIEM tools, such as:   - Elastic   - Splunk   - Microsoft Sentinel   - Similar platforms
• 2+ years investigating real security incidents
• Strong understanding of:   - AWS logs and IAM activity   - Cloud security basics   - Network fundamentals   - APIs, authentication flows, and modern application behavior
• Comfortable working with:   - Large datasets   - Raw logs   - Noisy alerts   - Incomplete telemetry
• Python or similar language for data processing
• Experience with automation:   - SOAR   - Scripts   - Internal tools   - Custom workflows
• Basic DevOps knowledge:   - AWS   - Linux   - Docker   - Kubernetes basics   - CI/CD basics   - Cloud infrastructure concepts   - Logs, metrics, and monitoring

Nice to Have

• Experience applying simple ML or statistical methods to logs
• Familiarity with LLMs in internal tooling
• Experience with data pipelines:   - Kafka   - Spark   - Similar systems
• MITRE ATT&CK mapping
• Detection-as-code experience
• Understanding of infrastructure monitoring:   - Prometheus   - Grafana   - OpenTelemetry

What Will Make You Effective Here

You will be effective in this role if:

• You do not trust alerts blindly
• You care about signal quality, not the number of detections
• You can explain why a detection is noisy or useless
• You understand systems, not just security tools
• You are comfortable reading raw logs
• You prefer building automation once instead of repeating manual work
• You can work with engineers and explain security findings in practical terms

About the Role:

Ми шукаємо інженера прошивок, який реально вміє працювати з сучасним залізом (ESP32-S3, PICAD-сумісні плати) та Zephyr OS. Основне завдання — писати стійкий, production-ready код для IoT-пристроїв рівня “data logger / gateway”: сенсори, Modbus, LTE/Wi-Fi, OTA, локальна логіка, енергозбереження.

Жодного академічного R&D — потрібна людина, яка швидко робить робочий продукт.

Responsibilities:

• Розробка firmware під Zephyr OS для ESP32-S3 / ESP32-C6;
• Робота з PICAD-сумісною периферією (датчики, інтерфейси, IO);
• Реалізація протоколів: Modbus RTU/TCP, UART, RS-485, SPI, I2C;
• Налаштування мережевих стеків: Wi-Fi, LTE (через зовнішні модулі), MQTT;
• OTA-оновлення (MCUboot, зразки Zephyr, кастомні схеми);
• Буферизація даних, локальний storage, fallback-режими;
• Інтеграція з backend API (REST/MQTT), обробка queue/telemetry;
• Оптимізація енергоспоживання та продуктивності;
• Написання unit-/integration-tests, робота з hardware-bring-up.

Requirements:

• Глибокий досвід розробки прошивок на C/C++;
• Комерційний досвід із Zephyr RTOS обов’язковий (device tree, Kconfig, drivers, subsystems);
• Практичний досвід роботи з ESP32-S3 / ESP32-C3 / ESP32-C6;
• Розуміння MCUboot, partition layout, OTA-pipeline;
• Уміння працювати з периферією: UART, SPI, I2C, PWM, ADC, RS-485;
• Досвід роботи з Modbus (master/slave), бажано з реальними пристроями;
• Досвід дебагу: JTAG, OpenOCD, logic analyzer;
• Git, CI/CD, уміння працювати в нормальному продукті, а не «прошивка нашвидкуруч».

Nice to have:

• BLE (Zephyr BT stack);
• Python для tooling (скрипти, тести, виробничі тулзи).

We Offer:

• Робота над реальним IoT-продуктом (energy logger / gateway), який уже йде у виробництво;
• Можливість професійного зростання та кар’єрного розвитку;
• Відпустка — 18 робочих днів на рік;
• Дружнє робоче середовище;
• Лікарняний — 5 робочих днів (без документів);
• Медичне страхування.

About the Role:

Ми шукаємо інженера прошивок, який реально вміє працювати з сучасним залізом (ESP32-S3, PICAD-сумісні плати) та Zephyr OS. Основне завдання — писати стійкий, production-ready код для IoT-пристроїв рівня “data logger / gateway”: сенсори, Modbus, LTE/Wi-Fi, OTA, локальна логіка, енергозбереження.

Жодного академічного R&D — потрібна людина, яка швидко робить робочий продукт.

Responsibilities:

• Розробка firmware під Zephyr OS для ESP32-S3 / ESP32-C6;
• Робота з PICAD-сумісною периферією (датчики, інтерфейси, IO);
• Реалізація протоколів: Modbus RTU/TCP, UART, RS-485, SPI, I2C;
• Налаштування мережевих стеків: Wi-Fi, LTE (через зовнішні модулі), MQTT;
• OTA-оновлення (MCUboot, зразки Zephyr, кастомні схеми);
• Буферизація даних, локальний storage, fallback-режими;
• Інтеграція з backend API (REST/MQTT), обробка queue/telemetry;
• Оптимізація енергоспоживання та продуктивності;
• Написання unit-/integration-tests, робота з hardware-bring-up.

Requirements:

• Глибокий досвід розробки прошивок на C/C++;
• Комерційний досвід із Zephyr RTOS обов’язковий (device tree, Kconfig, drivers, subsystems);
• Практичний досвід роботи з ESP32-S3 / ESP32-C3 / ESP32-C6;
• Розуміння MCUboot, partition layout, OTA-pipeline;
• Уміння працювати з периферією: UART, SPI, I2C, PWM, ADC, RS-485;
• Досвід роботи з Modbus (master/slave), бажано з реальними пристроями;
• Досвід дебагу: JTAG, OpenOCD, logic analyzer;
• Git, CI/CD, уміння працювати в нормальному продукті, а не «прошивка нашвидкуруч».

Nice to have:

• BLE (Zephyr BT stack);
• Python для tooling (скрипти, тести, виробничі тулзи).

We Offer:

• Робота над реальним IoT-продуктом (energy logger / gateway), який уже йде у виробництво;
• Можливість професійного зростання та кар’єрного розвитку;
• Відпустка — 18 робочих днів на рік;
• Дружнє робоче середовище;
• Лікарняний — 5 робочих днів (без документів);
• Медичне страхування.