ITS Recruiting

Responsibilities

• Extract Revit and IFC model data—properties, parameters, type/instance data, and relationships—using Autodesk Platform Services APIs
• Transform extracted model data into structured, analysis-ready formats such as Parquet, JSON, or CSV
• Process and manage derived geometry data (SVF/SVF2, OBJ, STL, IFC) for downstream analysis and visualization
• Build and maintain geometry data checks, including proximity/clash detection, bounding-box and volume validation, missing-element detection, and parameter completeness
• Run IFC quality checks to validate schema conformance, property-set completeness, and geometry integrity
• Develop and operate robust data ingestion pipelines, handling asynchronous translation jobs, webhooks, retries, and large/heavy models
• Map BIM data semantics onto the platform’s structured data model and identify API coverage gaps
• Collaborate with data, engineering, and AEC domain teams to ensure extracted data accurately reflects model intent
 

Required Skills

• Strong backend/data engineering skills in Python
• Experience with Autodesk Platform Services (APS) APIs (Model Derivative, Data Management)
• Proficiency in extracting and transforming BIM data from Revit and IFC models
• Familiarity with IFC schema, Revit property/parameter structures, and BIM data semantics
• Ability to handle and process geometry data (meshes, transforms, spatial queries)
• Experience with data formats such as JSON, Parquet, and CSV
• Knowledge of analytical/columnar data querying
• Experience with asynchronous pipelines, webhooks, and cloud-based data processing
• Familiarity with tools like trimesh, shapely, IfcOpenShell, or xBIM
• Git proficiency for version control

Senior MLOps Engineer with 7+ years of experience needed for a global project in defense AI technologies.
 

Requirements:

• 7+ years of experience
• MLOps infrastructure and pipeline experience
• Cloud platforms (AWS, GCP, Azure)
• Kubernetes and Docker
• CI/CD automation
• Python proficiency
   

Conditions:

• B2B Freelance contract
• Full Remote
• 12+ months duration

• ASAP start date

   

Tech Stack: Python, Kubernetes, Docker, CI/CD, AWS/Cloud platforms

Project:
Global project in the area of defense AI technologies. We're looking for several senior MLOps Engineers with more than 7 years experience.

A growing international company is scaling its recruitment function and looking for a Team Lead to take full ownership of hiring and lead a small recruitment team. You'll work closely with hiring managers, drive the process end-to-end, and help shape how recruitment runs as the company expands.
 

What you'll do

• Own the full-cycle recruitment process
• Lead, mentor, and grow the recruitment team
• Recruit hands-on, including senior and executive roles
• Track and improve hiring through metrics and analytics
• Maintain a strong, ready talent pipeline
• Run market research: salary benchmarks, competitor analysis, EVP
• Plan and manage the hiring budget
• Partner with managers and keep candidate experience high
   

What we're looking for

• Proven experience as a Team Lead / Head of Recruitment managing a team
• Background hiring for performance-marketing / media-buying teams
• Hands-on experience recruiting media buyers
• 5+ years in recruitment overall
• Confident with reporting, metrics, and process optimization
• Skilled at sourcing beyond LinkedIn and job boards (alternative channels)
   

Skills we value

• Strong ownership and a data-driven mindset
• Systems thinking, autonomy, proactivity
• Great communication and negotiation
• Leadership: mentoring and motivating a team
• English B2+ (interviews conducted in English)

COMPANY OVERVIEW:

IT product company is a leader in providing advanced telecommunications and IoT solutions to enterprises and enterprise customers worldwide. The company's mission is to empower communication, which is why we offer a wide range of communication products that are carefully customized to meet the specific requirements of our customers. Regardless of the industry or size of the organization, we develop optimal solutions to meet your unique needs.

RESPONSIBILITIES:

• Assist in building, maintaining, and monitoring CI/CD pipelines.
• Support deployment and operation of applications in cloud environments (AWS, Azure, GCP).
• Work with Docker, Kubernetes to containerize and orchestrate services.
• Monitor system performance and troubleshoot infrastructure issues.
• Collaborate with developers and QA to ensure smooth delivery of features.
• Maintain and improve automation scripts and infrastructure-as-code (IaC).
   

REQUIREMENTS:

• Basic understanding of Linux/Unix systems administration.
• Familiarity with cloud platforms (AWS, Azure, or GCP).
• Hands-on knowledge of Docker and basic Kubernetes concepts.
• Experience with CI/CD tools, especially Jenkins (GitLab CI, GitHub Actions as a plus).
• Proficiency in Bash scripting for automation (Python as a plus).
• Basic knowledge of networking concepts (DNS, HTTP, TCP/IP).
• English — Intermediate level (documentation and communication)..

NICE TO HAVE:

• Familiarity with monitoring tools (Prometheus, Grafana, ELK).
• Experience with infrastructure-as-code (Terraform, Ansible).
• Interest in telecommunications or IoT-related projects.

COMPANY OFFERS:

• Employment in according to the Ukrainian labor law (24 days of vacation, sick leave).
• Opportunity to work with advanced telecommunications technologies and gain hands-on experience.
• Exposure to a global clientele and the opportunity to contribute to the development of effective solutions.
• Mentorship from experienced professionals in the telecommunications industry.
• Collaboration and a supportive work environment.
• Competitive compensation package and growth prospects.

This role sits within a fast-growing technology company developing advanced AI agents for the
construction industry, focused on automating the analysis of building models, drawings, and
regulatory documents to enhance compliance, planning, and design validation. As part of a
senior engineering team, this position plays a critical role in ensuring the reliability and
trustworthiness of complex, non-deterministic AI systems that support key decision-making
processes in construction projects.
 

Responsibilities:
• Design and execute comprehensive tests for agentic AI systems, focusing on tool selection,
multi-step reasoning, and recovery from failures
• Evaluate agent outputs for grounding, citation accuracy, and evidence-backed claims
• Develop and maintain automated frameworks to assess generative AI outputs for hallucination,
consistency, and factual accuracy
• Build and run guardrail and adversarial tests to identify vulnerabilities such as prompt injection
and instruction manipulation
• Validate multi-turn conversations and ensure correct handling of conversational state across
sessions
• Implement and monitor continuous evaluation of agent performance in production
environments
• Investigate and triage quality incidents, tracing failures to specific models or datasets and
collaborating on fixes

• Create and maintain robust integration and API tests to ensure seamless backend and
user-facing workflows
• Enforce quality gates and produce verification evidence to support confident releases
• Map test cases to system requirements, ensuring traceability and comprehensive coverage
 

Skills:
- Expert Python programming for test automation and custom evaluation tooling
- Hands-on experience with LLM/agent evaluation frameworks (e.g., DeepEval, TruLens,
RAGAS, or custom evaluators)
- Proficiency in designing and implementing non-deterministic and generative AI test strategies
- Strong knowledge of integration, API, and UI automation tools (e.g., Selenium, Playwright,
Requests)
- Experience with performance and load testing tools (e.g., Locust, JMeter, k6)
- Proficiency with SQL and data validation tools (e.g., Great Expectations)
- Familiarity with vector databases and retrieval corpora
- Experience integrating automated tests into CI/CD pipelines (e.g., GitLab)
- Ability to define and communicate pass/fail criteria for probabilistic systems
- Strong collaboration and communication skills with engineering and product teams

About:
This role sits at the intersection of advanced AI development and rigorous quality assurance
within a leading technology organization focused on delivering secure, reliable solutions for
mission-critical sectors. The Senior QA Automation Engineer will play a key part in ensuring that
innovative AI and supply chain systems meet the highest standards of reliability and
compliance, supporting the organization’s commitment to operational excellence and
technological advancement.
 

Responsibilities:
- Lead the design and implementation of automated testing frameworks for AI and LLM-based
systems
- Develop and maintain automated test suites to validate AI outputs for accuracy, consistency,
and alignment with ground truth datasets
- Build and execute integration tests to ensure seamless data flow between AI agents and
enterprise systems such as ERP platforms
- Create and run performance tests to benchmark AI model latency, throughput, and reliability in
real-time environments
- Automate API testing to verify secure data exchange, RBAC enforcement, and PII redaction
before data reaches AI models
- Map automated test cases to system requirements and user needs to support formal
verification and validation processes

- Prepare and deliver test readiness documentation and evidence for stage gate and production
release reviews
- Manage defect identification, tracking, and resolution, ensuring traceability to specific model
versions or datasets
- Collaborate with cross-functional teams, including data scientists and systems engineers, to
align testing with acceptance criteria
 

Skills:
- Expert proficiency in Python for test automation
- Strong experience with Pytest, Selenium, and Playwright
- Advanced skills in performance testing tools (Locust, JMeter, K6)
- Hands-on experience evaluating LLMs and AI models (DeepEval, TruLens, custom evaluators)
- Proficiency in SQL and data validation tools (Great Expectations)
- Experience integrating automated tests into CI/CD pipelines (GitLab)
- Familiarity with requirements management and traceability tools (Jira, Jama, Polarion)
- Strong knowledge of test management and reporting tools (TestRail, Allure)
- Solid understanding of version control systems (Git, GitLab)
- Deep knowledge of modern quality engineering practices (Shift Left, Test Pyramid, mono-repo
architecture)

Responsibilities

• Architect and implement backend systems for AI agent orchestration, including workflow logic, task routing, and error handling
• Design and build scalable, containerized microservices to support real-time data processing and high-concurrency requests
• Develop secure API gateways and data access layers for integration with enterprise systems such as SAP S/4HANA, Ariba, and external data providers
• Create backend logic for knowledge retrieval, including integration with vector databases and retrieval-augmented generation (RAG) models
• Construct connectors for external data feeds, managing rate limiting, caching, and data normalization
• Implement security controls such as authentication, RBAC, and data redaction to meet defense-grade requirements
• Optimize backend systems for on-premise and air-gapped deployments, ensuring efficient resource usage and reliability
• Establish comprehensive logging, monitoring, and observability to support operational excellence and compliance
• Collaborate with cross-functional teams to deliver robust, production-ready backend solutions for AI-driven platforms
   

Required Skills

• Expert proficiency in Python (FastAPI or Django)
• Strong experience with Go or Java for high-performance microservices
• Deep knowledge of Docker and Kubernetes for containerization and orchestration
• Advanced skills in designing RESTful APIs and gRPC services
• Experience building and managing API Gateways (e.g., Kong, NGINX)
• Proficiency with PostgreSQL and vector databases (e.g., Weaviate, Milvus)
• Familiarity with enterprise integration protocols (OData, SOAP)
• Strong understanding of authentication, RBAC, and data security best practices
• Experience deploying and optimizing systems in on-premise or air-gapped environments
• Proven ability to implement logging, monitoring, and observability in production systems

Senior Full Stack Engineer with 10+ years of experience needed for a global project in defense AI technologies.

     Requirements:

• 10+ years of experience
• Strong fundamentals in data structures, algorithms, and problem-solving
• Language-agnostic mindset – comfortable learning new languages as needed
• Python + JavaScript/TypeScript (React preferred)
• Depth of knowledge over breadth of tools
  
  Conditions:
• B2B Freelance contract
• Full Remote
• 12+ months duration

• ASAP start date
   

  Project:
  Global project in the area of defense AI technologies.

   

Responsibilities:
- Architect and implement data lakehouse solutions to centralize and harmonize supply chain
and procurement data from multiple enterprise systems
- Design and deploy data ingestion pipelines for structured and unstructured data, including
ERP sources (SAP S/4HANA, Ariba), external market feeds, and technical documents
- Develop and maintain unified data models and taxonomies to support analytics and AI-driven
forecasting
- Build and optimize pipelines for processing unstructured data (PDFs, CAD files, regulatory
documents) into formats suitable for AI and RAG applications
- Manage and optimize vector databases to enable high-speed retrieval of engineering and
procurement data for generative AI tools
- Establish and enforce data lineage, traceability, and governance protocols to ensure data
integrity and compliance
- Implement and monitor data quality controls to validate completeness and accuracy of critical
datasets

- Collaborate with cross-functional teams to map enterprise data sources and define
requirements for AI and analytics use cases
- Optimize data workflows for secure, on-premise, and air-gapped environments, ensuring
efficient use of infrastructure
- Support the technical execution of foundational data platform initiatives within structured sprint
cycles
 

Skills:
- Expert proficiency in Python, SQL, and modern data engineering frameworks (Apache Spark,
Kafka, Airflow).
- Enterprise ERP: Strong experience extracting data from complex ERP environments,
specifically SAP S/4HANA and SAP Ariba. Familiarity with SAP BTP is a plus.
- Database Technologies: Deep understanding of Data Lakehouse architectures
(Databricks/Delta Lake), Relational Databases (PostgreSQL), and Vector Databases
(Weaviate/Milvus).
- Data Pipeline Development: Experience building pipelines for RAG solutions, Conversational
agents and classical ML models with tools like dbt, dagster, or prefect
- DevOps/DataOps: Proficiency with containerization (Docker, Kubernetes) and CI/CD pipelines
for deploying data workflows in secure environments.
- Experience: 7+ years of experience in Data Engineering, with at least 2 years focused on
building pipelines for Machine Learning or Generative AI applications in an enterprise setting.
- Domain Knowledge: Experience in Supply Chain, Manufacturing, or Defense sectors is highly
desirable. Ability to understand "Bill of Materials" (BOM) structures and procurement lifecycles.
- Problem Solving: Ability to navigate the "Governance Collision" between agile data work and
rigid systems engineering requirements, ensuring data deliverables meet formal Stage Gate
reviews.
- Collaboration: Proven ability to work alongside Data Scientists and Backend Engineers to
define data schemas that support predictive modeling and AI agents.

Join a team building the infrastructure behind autonomous driving technology. We run high-performance computing environments for machine learning, and we're looking for a Linux engineer who enjoys keeping complex systems fast, stable, and secure.

You don't need to tick every box below — if you're a strong Linux person excited to grow into HPC, we'd love to hear from you.

What you'll do:

• Keep our Linux infrastructure running smoothly — servers, web services, ERP, mail, LDAP, messaging
• Work with HPC clusters for machine learning workloads (Slurm, Ceph — or similar tools you'll pick up)
• Automate infrastructure and provisioning with Ansible
• Keep our distributed systems stable, fast, and secure

What we're looking for:

• Solid hands-on Linux administration (Ubuntu / Debian preferred)
• Comfort working in C++-based environments (you don't need to be a C++ developer)
• Some experience with — or genuine interest in — HPC, job schedulers (Slurm), and distributed storage (Ceph)
• Familiarity with infrastructure automation (Ansible or similar)
• A good grasp of networking and security basics

Nice to have:

• Python, Rust, or CUDA
• Low-level Linux / kernel knowledge
• Web or backend background

What's in it for you:

• Work at the cutting edge of autonomous driving
• Flexible working hours
• Fast-moving engineering culture
• Competitive pay + stock options
• Fully remote within Germany

Senior MLOps Engineer with 7+ years of experience needed for an AI-powered construction software company.
 

Responsibilities:

• Design, build, and maintain infrastructure for deploying, serving, and monitoring machine learning models at scale
• Deploy and scale large language models (7B-376B parameters) across varied sizes, optimizing for latency, throughput, and cost
• Operate and enhance a multi-provider gateway for routing requests between self-hosted models and external APIs
• Develop and manage automated pipelines for model training, fine-tuning, evaluation, and continuous delivery
• Monitor production ML services, implement observability, and respond to incidents
• Implement evaluation and verification systems to detect quality and performance regressions
• Collaborate with cross-functional teams to integrate ML models into production environments
• Deliver infrastructure-as-code and CI/CD workflows for repeatable, secure deployments
• Optimize model and infrastructure efficiency through quantization, pruning, and distributed inference
• Provide self-serve tooling and documentation for internal engineering teams
   

Must-have:

• Deep expertise in LLM deployment and scaling (7B to 376B parameters)
• Strong Python proficiency, with C/C++ experience for performance-critical components
• Hands-on experience with inference serving engines (vLLM, Triton, TGI)
• Advanced knowledge of distributed training frameworks (DeepSpeed, FSDP, Accelerate)
• Proficiency with MLOps platforms and pipelines (MLflow, Kubeflow, SageMaker Pipelines)
• Cloud infrastructure and orchestration (AWS, Docker, Kubernetes, EKS, EC2, Lambda)
• GPU/cloud resource optimization and cost efficiency management
• Monitoring, logging, alerting, and incident response for ML services
• Experience with quantization, pruning, and multi-GPU/distributed inference
• Experience building or operating multi-provider model gateways

Conditions:

• B2B Freelance contract
• Full Remote
• 12+ months duration
• ASAP start date

Project:
AI-powered construction software startup. Pioneer in artificial intelligence focused on developing secure, enterprise-grade solutions bridging advanced AI research with real-world applications.