Role Description Join our Data & AI team as a Junior AI Engineer, where you will work on end-to-end projects for clients across various industries, including manufacturing, finance, healthcare, and media. You will design, build, and deploy AI systems within the AWS ecosystem, providing opportunities to grow by rotating between diverse initiatives. - A large-scale multi-tenant AI voicebot making hundreds of thousands of outbound calls monthly. - An internal AI Agents platform for a 10,000+ employee manufacturing enterprise. - Document processing pipelines: automated OCR, classification, and metadata extraction. - An end-to-end media solution: video scene analysis, product matching with LLM ranking, and automated marketing asset generation for a global broadcaster. Projects range from a few weeks (PoC, MVP) to many months or years (production deployments). Client scale varies from mid-sized Polish companies to international enterprises. Tech stack includes: - Cloud: AWS (Bedrock, SageMaker, Lambda, Step Functions, S3, DynamoDB, Bedrock AgentCore) - IaC: Terraform - Languages: Python - AI/agent frameworks: LangGraph, Pipecat (Strands Agents, CrewAI, or Haystack are a plus) - Observability: LangFuse - AI-native development: Cursor, Claude Code, Codex (licenses provided) - CI/CD: GitHub Actions, CodePipeline Your Responsibilities: - Design, implement, and test AI solutions within the AWS ecosystem. - Build agentic AI applications using frameworks such as LangGraph and Strands Agents. - Integrate AI solutions with clients’ existing technology stacks and systems. - Develop and maintain automation for model training, deployment, and CI/CD pipelines. - Support presales activities from a technical perspective, including co-authoring proposals and Statements of Work (SoW). - Collaborate in small, agile project teams (2–5 people), often working directly with clients. Qualifications - 2+ years of experience in AI/ML and strong Python skills, with the ability to write production-grade code. - Hands-on experience with AI models (fine-tuning, prompt engineering, RAG, API integrations) and familiarity with at least one agentic framework (e.g. LangGraph, CrewAI, Haystack, Strands Agents). - Experience using AI-native development tools (e.g. Cursor, Claude Code, Codex) in day-to-day work. - Solid software engineering practices (code reviews, Git, testing, deployment) and AWS knowledge or willingness to quickly ramp up. - Consultative mindset: understanding client problems and proposing solutions, not just executing tasks. - Self-driven, open to feedback, and proactive in identifying and addressing issues early. - Team-oriented approach, including active knowledge sharing as a standard way of working. - Fluency in both English and Polish, written and spoken. Requirements - Experience in Computer Vision (e.g. OpenCV, detection and segmentation models) is a plus. - Familiarity with Infrastructure as Code tools (e.g. Terraform) is a plus. - Experience with voice-based solutions (voicebots, speech-to-text, text-to-speech) is a plus. - Background in a consulting or software house environment is a plus. - Hands-on experience with AWS AI services such as Amazon Bedrock or SageMaker is a plus. Benefits - Continuous learning and growth through internal training, knowledge-sharing sessions, and hands-on learning from real projects. - Training budget for certifications, courses, and professional development aligned with your career goals. - Comprehensive private healthcare to help you stay healthy and focused. - Multisport card co-financing to encourage staying active. - Access to a language learning platform to improve your language skills at your own pace. - Flexible working hours & remote work options. - Company events for integration and fun.

• Partner with the AI Engineering lead to ship features from day one, ramping quickly into independent contributions on the Elixir platform • Complete a Forward Deployed Engineer (FDE) rotation in months 1–2 — embed with a business function to learn what the platform must support by being the one who needs it • Contribute to Elixir’s core platform: skill execution runtime, tool/MCP registry, retrieval and memory primitives, evaluation harnesses, observability and cost telemetry, and multi-model routing • Extend the skills surface: creating, editing, version control, AI assistance, testing, benchmarking, and permissions • Build event-driven background agents — absorb support-ticket pre-triage, ambient agents, and webhook-driven flows into Elixir as native event-driven flows • Productize what FDEs build — extract platform-level needs so each engagement compounds rather than serializes • Make Elixir secure for production — PII handling, access control, audit logging, evaluation gates, sandboxing, and multi-tenant isolation • Maintain clear and proactive communication with stakeholders and cross-functional partners • Demonstrate self-sufficiency by independently managing tasks, problem-solving, and meeting deadlines while adapting to new challenges.

• Build and ship LLM-powered features across the DerivativeEDGE platform, working directly with domain experts to translate complex financial workflows into reliable AI capabilities. • Design and implement agentic workflows that can reason, act, and recover gracefully across multi-step processes in a production environment. • Own the data engineering layer that feeds AI systems: pipelines, retrieval architectures, context design, and data quality. • Move fluidly between experimentation and production. You will prototype quickly, evaluate honestly, and know when something is ready to ship. • Contribute to the AI Lab's broader technical direction, including evaluations, tooling, MLOps practices, and the patterns the team builds on. • Depending on where projects take you, work may also touch NLP, model fine-tuning, synthetic data, or reinforcement learning.

• Design, develop, and deploy AI-powered applications, workflows, and automation solutions using LLMs and related AI technologies. • Build and maintain integrations between HubSpot, CRM platforms, EHR systems, business applications, and third-party tools using APIs and middleware solutions. • Develop, test, and optimize prompts, workflows, and AI agents to improve response accuracy, efficiency, and user experience. • Configure and optimize HubSpot Marketing, Finance, Sales, Service, and CMS hubs, including workflows, reporting, data management, and automation. • Collaborate with marketing, finance, sales, customer service, and product teams to identify automation opportunities and translate requirements into scalable technical solutions. • Create dashboards, reports, and data models to monitor performance, measure KPIs, and generate actionable insights. • Conduct end-to-end testing, troubleshooting, and performance monitoring of AI and integration solutions. • Maintain data quality, governance, security, and documentation across integrated systems. • Evaluate emerging AI technologies, integration tools, and automation best practices, recommending improvements where appropriate. • Provide technical guidance and training to internal teams on AI, automation, and HubSpot capabilities.