Ralabs

Hi! We are seeking for a Technical Leader/ Solution Architect responsible for defining and guiding the architecture of a next-generation UK healthcare platform.

What is the role about?

The role combines solution architecture, technical leadership, and AI-native software delivery. You will help evolve an existing AI-driven implementation into a secure, scalable, maintainable, and production-ready platform while preserving the delivery speed enabled by modern agentic development practices.
A key part of the role is shaping the Agentic Development Lifecycle (ADLC): how requirements are captured, AI agents handle implementation, generated solutions are reviewed, testing is performed, and changes are released.
The role is also highly client-facing and requires the ability to clearly explain architectural decisions, trade-offs, risks, effort, and business impact.

How many stages of the interview are there?

• Interview with the Recruiter – up to 30 min.
• Technical Interview  – up to 1 hour;
• Final interview  – up to 1 hour;
  
  Hard skills
• 7+ years of software engineering experience, including technical leadership responsibilities.
• 3+ years in a Solution Architect, Software Architect, Principal Engineer, Staff Engineer, Tech Lead, or similar role.
• Proven experience designing and delivering complex production systems.
• Experience evolving prototypes or early-stage platforms into production-grade solutions.

• Experience working directly with clients and senior technical stakeholders.
   

  Technical Expertise

• Strong understanding of software architecture and distributed systems.
• Experience designing scalable, secure, cloud-native solutions.
• Strong AWS experience.
• Deep knowledge of APIs, integrations, and third-party systems.
• Strong understanding of relational databases, transactional systems, data modelling, and data integrity.
• Understanding of data migration, ETL, and reconciliation approaches.
• Strong knowledge of security, performance, scalability, reliability, and maintainability.
• Ability to evaluate technology choices and architectural trade-offs.
• Practical experience with modern AI-assisted software engineering tools and workflows.
• Ability to critically review AI-generated code and identify architectural, security, scalability, and maintainability risks.

• Experience with AI-native or agentic development approaches.
   

  Leadership & Consulting

• Ability to define technical vision and architectural direction.
• Ability to translate architecture into practical guidance for engineering teams.
• Strong decision-making and problem-solving skills.
• Ability to operate effectively with ambiguity and incomplete requirements.
• Ability to challenge existing solutions constructively and support recommendations with evidence.

• Strong stakeholder management and client-facing communication skills.
   

  Communication

• Upper-Intermediate or higher English proficiency.
  
  
  Responsibilities
   

  Solution Ownership & Technical Governance

• Defining the overall technical strategy and architectural direction.
• Defining engineering patterns and guardrails for AI-assisted development.
• Ensuring AI-generated solutions meet production engineering standards.
• Leading decisions around application architecture, APIs, integrations, databases, AWS infrastructure, authentication, and deployment.
• Evaluating architectural trade-offs based on scalability, security, maintainability, performance, cost, and delivery speed.
• Providing technical guidance to engineers and QA.
• Leading architecture reviews and technical assessments.

• Identifying technical risks and defining mitigation approaches.
   

  The role is accountable for key architectural decisions, including:

• API and integration architecture.
• Data storage and modelling.
• Cloud and infrastructure design.
• Application architecture and codebase organisation.
• Framework and platform selection.
• Authentication and access-control approaches.
• Data migration and reconciliation.

• AI-related architecture and engineering controls.
   

  Engineering Excellence

  The AI-Native Solution Architect helps the team transition from a traditional SDLC towards an AI-native Agentic Development Lifecycle.
   

  Key focus areas include:

• AI-assisted engineering practices.
• Architecture and code quality.
• Secure development.
• Automated and risk-based testing.
• End-to-end and synthetic scenario testing.
• CI/CD and release controls.
• Monitoring and observability.

• Technical documentation and specifications.
   

  Collaboration & Delivery Support

• Works closely with Project Management, Business Analysis, QA, engineering, and client stakeholders.
   

  Responsibilities include:

• Balancing delivery speed, scope, budget, and technical complexity.
• Translating business and clinical needs into technical solutions.
• Establishing a repeatable process for turning requirements into specifications.
• Keeping the team focused on the underlying problem, not recreating legacy functionality.
• Challenging unnecessary complexity and historical requirements.
• Providing technical leadership to the engineering team.

• Communicating technical trade-offs, dependencies, and risks.
   

  Pre-Sales & Solution Design (optional involvement)

• Lead architecture reviews, discovery sessions, and technical workshops.
• Define solution architecture, assumptions, constraints, and non-functional requirements.
• Review technical findings and validate priority and severity.
• Evaluate alternative approaches, including benefits, risks, effort, and cost.
• Support estimation and prioritisation of technical initiatives.
• Present and defend architectural recommendations to the client.

• Research relevant technologies and engineering practices and provide recommendations.
   

  Technology Office Contribution

  The AI-Native Solution Architect contributes to the evolution of AI-native engineering practices across the organisation.
   

  Responsibilities include:

• Sharing lessons learned from implementing ADLC in production.
• Defining reusable AI-native engineering practices and guardrails.
• Evaluating AI tools and approaches for broader organisational use.
• Supporting architecture discussions for strategic initiatives.
• Sharing knowledge through mentoring, workshops, and internal discussions.
  
  Nice to Have
•  Experience in Healthcare, HealthTech, FinTech, or another regulated industry.
•  Experience with UK healthcare or NHS-related systems.
•  Experience with Python, FastAPI.
•  Hands-on experience with AWS Bedrock.
•  Experience with AI/agent frameworks such as LangGraph, vLLM, Ollama, or similar.
•  Experience with large-scale data migration or legacy modernisation.
•  Understanding of clinical safety or medical software regulation.

We are looking for a Senior/Lead Embedded Software Engineer (Ukraine-based) to join Ralabs and work on technically challenging projects involving embedded systems, edge computing, and AI-powered solutions.

Engagement: Initially, approximately 10–20 hours per week during the first 3 months, with a transition to full-time afterward.

What is the role about?

The role combines hands-on software development, hardware integration, and performance optimization. You will work with resource-constrained devices, adapt existing software to new hardware platforms, and help develop reliable, production-ready solutions.

Beyond individual projects, you will contribute to our broader engineering capabilities in embedded systems and edge AI.

How many stages of the interview are there?

• Interview with the Recruiter – up to 30 min.
• Technical Interview  – up to 1 hour;
• Final interview  – up to 1 hour;
  
  Hard skills
• 6+ years of commercial software engineering experience.
• Proven experience developing software for embedded Linux environments.
• Hands-on experience with ARM-based computing platforms.
• Proven experience integrating software with hardware components and external devices.
• Proven experience optimizing software for performance, memory, and hardware constraints.
• Ability to independently investigate technical challenges and deliver working solutions.

• Upper-Intermediate or higher English proficiency.
   

  Technical Expertise

• Strong proficiency in C/C++ and/or Python.
• Solid knowledge of Linux internals, system-level programming, and debugging.
• Experience with embedded platforms such as NVIDIA Jetson, Raspberry Pi, or similar.
• Experience with YOLO-NAS, YOLOX, and DarkYOLO in ONNX.
• Understanding of hardware interfaces and communication protocols (UART, GPIO, SPI, I2C, etc.).
• Experience with computer vision, image processing, or real-time data processing.
• Understanding of ML model deployment and optimization on edge devices.
• Familiarity with software security, encryption, and secure deployment practices.

• Strong understanding of performance optimization, testing, and troubleshooting.

  
  Leadership & Coloboration

• Ability to take technical ownership of assigned tasks and solutions.
• Strong analytical and problem-solving skills.
• Ability to evaluate technical approaches and make practical engineering decisions.
• Comfortable working with incomplete requirements and exploring alternative solutions.

• Ability to collaborate with cross-functional engineering teams and technical stakeholders.
  
  Responsibilities
   

  Embedded Software Development

• Design, develop, and maintain software for embedded computing platforms.
• Adapt existing software solutions to different hardware architectures.
• Integrate software with hardware components, peripherals, and external systems.
• Troubleshoot system-level issues and improve overall reliability.

• Ensure software operates efficiently within hardware limitations.
   

  Edge AI & Computer Vision

• Support the integration and deployment of AI/ML models on edge devices.
• Optimize inference performance, resource usage, and processing efficiency.
• Work with computer vision and real-time data processing pipelines.

• Evaluate technical approaches for improving system performance.
   

  Engineering Excellence

• Write clean, maintainable, and well-tested code.
• Establish reliable development, testing, and deployment practices.
• Identify performance bottlenecks and propose improvements.
• Ensure appropriate security and reliability standards.

• Maintain technical documentation.
   

  Collaboration & Delivery Support

• Work closely with engineering teams, technical leads, and project stakeholders.
• Participate in technical discovery and solution design.
• Support technical estimations and feasibility assessments.
• Communicate technical challenges, risks, and dependencies.

• Take ownership of delivering working, production-ready solutions.
   

  Pre-Sales & Solution Design (Optional Involvement)

• Support technical discovery for new embedded and edge AI opportunities.
• Evaluate technical feasibility and potential implementation approaches.
• Contribute to solution design and effort estimation.
• Participate in technical discussions with prospective clients.
  
  Nice to Have
• Experience with CUDA, TensorRT, or other NVIDIA technologies.
• Experience with OpenCV, GStreamer, or similar frameworks.
• Practical experience deploying and optimizing ML/CV models on embedded hardware.
• Experience with real-time systems and video processing.
• Experience with secure boot, firmware protection, or embedded security.
• Experience in hardware prototyping and working directly with electronic components.
• Experience with hardware migration or embedded platform modernization.

We are looking for a Senior Data Engineer to join a project in the tech capital of the world – Silicon Valley.
 

17:00–02:00 Kyiv time = 7:00–16:00 Pacific Time (PST/PDT).
 

Project Idea

The project was founded back in 2014 with the goal of connecting private and government universities with regular people like we are. You have a variety of auditoriums, gyms, classrooms, and other venue options available for community use –
schedule facility uses and manage requests from the community all in one place.

Just imagine that you’re a football player and you can rent a football field at Harvard to play with your friends. Amazing, right?

Must have:

• 5 + years of experience in Data Engineering or related roles;
• Strong SQL skills and hands-on experience with PostgreSQL (including Aurora Serverless);
• Solid knowledge of Python for data processing and automation;
• Experience building and maintaining ETL/ELT pipelines using cloud-native tools (e.g., AWS Lambda, S3, SQS);
• Proven experience working with MongoDB and MongoDB Atlas, including event-driven architectures using Atlas Triggers, Stream Processing;
• Proficiency with dbt for building modular, testable, and well-documented data transformation workflows;
• Good understanding of data modeling principles for OLAP/OLTP systems, including normalization and dimensional modeling;
• Demonstrated experience designing and implementing data warehouses and data marts;
• Working knowledge of Node.js, particularly in backend logic tied to data ingestion or transformation workflows;
• Familiarity with cloud data platforms (e.g., AWS) and serverless computing patterns;
• A technical degree (e.g., Computer Science, Engineering, Math) is a plus;
• Upper-Intermediate English or higher for effective communication and documentation.

Soft skills:

• Proactive – you take ownership and act without waiting for direction;
• Detail-oriented – you deliver accurate, high-quality work;
• Initiative-driven – you’re eager to improve processes and take action.

Responsibilities:

• Design, implement, and maintain scalable and reliable data pipelines using Python, dbt, and AWS Lambda;
• Build and optimize data architectures to support analytics, reporting, and machine learning use cases, including data warehouse and data mart modeling on PostgreSQL (Aurora Serverless);
• Develop and manage ELT workflows that extract data from MongoDB (using Atlas triggers) and load into staging and production layers in PostgreSQL;
• Ensure data consistency and lineage by applying robust data quality checks, auditing, and reconciliation logic;
• Collaborate with cross-functional teams to gather data requirements, understand business logic, and translate them into efficient data models and transformations;
• Monitor and troubleshoot SQS/Lambda-based ingestion pipelines, addressing issues related to concurrency, message processing, and data duplication;
• Contribute to the semantic layer design used by BI and reporting tools to ensure consistency and accessibility of business metrics;
• Maintain and evolve dbt models (staging, intermediate, and marts) aligned with software engineering and analytics best practices;
• Drive continuous improvement in data engineering processes and data governance standards, ensuring scalability, maintainability, and security.