AI Engineer

• Enablement & coaching — work directly with engineers through 1:1s, pair programming, workshops, office hours, and cohorts to raise their effectiveness with AI-assisted development: prompting, workflow design, and model/tool selection. Identify under-adoption and lift teams up to the standard.
• Best-practice propagation — capture what works (and what fails) in one team and systematically transfer it: living best-practice guides, prompt libraries, troubleshooting playbooks, and an internal AI knowledge base and skills registry.
• Scale through champions — stand up and support a network of embedded AI champions so enablement scales past a single person.
• Unblocking & feedback loop — be the go-to when engineers get stuck with AI tooling. Proactively tour teams to surface blockers early, aggregate them into patterns, and escalate systemic issues to leadership.
• Internal integrations & tooling — build internal AI integrations and reusable tooling hands-on (Node.js/TypeScript, MCP) — from prototype to production independently, including auth, access control, security review, and CI/CD.
• Evaluation & scouting — run structured evaluations of AI tools, models, and internal integrations; monitor the fast-moving landscape (Claude Code, Cursor, Copilot/Codex, new model releases) and deliver curated, actionable recommendations.
• Measurement & impact — define and track adoption and productivity metrics by team, tool, and individual. Build dashboards on real usage and output — adoption/active-usage rate, assistant acceptance rate, PR throughput and cycle time — with a quality guardrail (change failure rate) so speed never comes at the cost of quality. Reference frameworks: DX Core 4, DORA.
• Cross-functional partnering — partner with security/compliance on safe-by-design guardrails, and with GTM to translate field needs into internal enablement and feed adoption patterns back.

Senior AI Enablement Engineer

• Research and define how to implement client requirements and ideas best
• Engage directly with client architects and end-users through design calls and syncs to gather requirements, clarify edge cases, and shape the future of the testing tool
• Develop and maintain a Python-based validation framework designed to test low-level SDK APIs
• Use modern AI coding tools (LLMs, Copilot, Codex, etc.) as your primary workflow to rapidly generate, refactor, and expand test scripts

Network & Hardware AI Validation Engineer

Machine Learning Engineer

• Build core ML systems that power a proactive, long-horizon AI product.
• Own work end-to-end: data preparation, training, evaluation, inference, and iteration.
• Turn research ideas into working systems that run reliably in production.
• Debug model failures and system issues using real production signals.
• Iterate quickly: ship, measure outcomes, refine, and repeat.
• Collaborate closely with research, product, and engineering to deliver real user impact.
• Mentor and review work from other ML engineers through example and technical judgment.
• Work under real production constraints: latency, cost, reliability, and safety

Senior Machine Learning Engineer

• Build and improve ML components across data, training, evaluation, and inference.
• Fine-tune and adapt models as part of larger production systems.
• Implement evaluation and testing to understand model behavior.
• Help build and maintain data pipelines for real-world and synthetic data.
• Debug model issues, performance problems, and production incidents.
• Ship improvements iteratively and learn from real user feedback.
• Work closely with senior ML engineers and product teams.
• Work under real production constraints: latency, cost, reliability, and safety

Member of Technical Staff, Machine Learning

• Identify, qualify, and shape high-impact AI and automation opportunities across diverse business functions
• Formulate robust automation roadmaps aligned with Global Business Services (GBS), Shared Services, and enterprise-wide transformation agendas
• Conduct rigorous process assessments to identify high-value use cases for artificial intelligence and automation
• Direct the design, development, and deployment of sophisticated automation solutions, including RPA, AI/ML workflows and ID
• Ensure all technical solutions are highly scalable, secure, sustainable, and strictly aligned with overarching business objectives
• Drive measurable cost reduction, operational efficiency, and service optimization through targeted automation initiatives
• Construct robust business cases, establishing clear governance frameworks to track ROI and realize tangible business benefits
• Seamlessly integrate automation strategies into broader, multi-functional enterprise transformation programs
• Partner closely with business unit leaders, IT organizations, and executive leadership teams to align technology with business strategy
• Translate complex technical capabilities into compelling, value-driven business propositions for non-technical stakeholders
• Serve as a subject matter expert and trusted advisor on intelligent automation strategy and enterprise AI adoption
• Establish and refine automation frameworks, operating models, governance structures, and industry best practices
• Mentor cross-functional teams and build sustainable automation capabilities for both internal teams and client organizations
• Maintain a forward-looking understanding of emerging trends, tools, and methodologies in AI, machine learning, and digital transformation

AI Automation Lead

• Architect and scale our GTM systems across sales, marketing, and product
• Lead development of AI agents and workflows that automate key parts of the funnel
• Orchestrate tools like Salesforce and HubSpot into a unified system
• Turn product and user data into actionable growth signals
• Build intelligent workflows (lead scoring, routing, outbound triggers, onboarding flows)
• Define how AI is used across the entire customer lifecycle
• Identify bottlenecks and design systems to remove them
• Partner closely with founders on growth strategy, experiments, and priorities

GTM Engineer, AI Engineer

• Own internal tools end to end - from understanding the operational problem through design, implementation, deployment, and iteration.
• Build across the stack as the product requires: Python/Go services on Cloud Run, TypeScript web apps and internal admin tools, event-driven integrations on Pub/Sub, and cloud infrastructure on GCP.
• Integrate with third-party SaaS platforms and cloud APIs - handling REST, webhooks, OAuth, and event-driven patterns, including failures, retries, and schema drift.
• Build and maintain workflow automation and business process orchestration as named, observable flows rather than logic buried in vendor scripts.
• Use AI tooling effectively across the development lifecycle - spec, code, and review - directing Claude as a coding collaborator while owning the output.
• Review AI-generated code before it ships for correctness, security, and type safety.
• Operate what you ship: monitoring, reliability, cost, and failure modes in production.
• Contribute to shared engineering standards, playbooks, and internal tooling.

Applied AI Engineer – Poland

• Build customer-facing and internal product features end to end.
• Develop web flows, backend APIs, admin tools, dashboards and operational systems.
• Support products across insurance, payments, savings, investing, travel and financial services.
• Work with product, design, mobile and operations teams to launch fast.
• Integrate APIs, handle data flows and improve production reliability.
• Use analytics, feedback and production issues to improve features after launch.
• Help build tools that reduce manual work and improve speed across the business.

Full Stack Software Engineer – AI Neobank App

• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems.
• Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows.
• Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence.
• Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production.
• Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust.
• Turn manual processes into reliable AI-enabled systems that can scale.

Applied AI Engineer

• Build AI-powered workflows, assistants, agents and automation systems.
• Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools.
• Work with product and engineering teams to turn manual processes into scalable AI-native systems.
• Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems.
• Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable.
• Prototype quickly, test with users or operators, then productionize what works.
• Improve speed, quality and consistency across workflows using AI where it creates real business value.

Applied AI Engineer – AI Neobank App

• Design and implement advanced AI models and systems while leveraging modern frameworks like LangChain, LangGraph or agentic AI technologies.
• Develop new AI approaches and solutions, experimenting with cutting-edge architectures and driving innovation across business use cases.
• Deliver scalable and high-impact AI capabilities.

Senior GenAI Engineer

AI Research Scientist

• Deliver robust, scalable ML solutions for AI capabilities in educational products, taking significant implementation decisions independently.
• Work closely with Business Unit partners to understand their AI-related problems, help frame them clearly, and develop solutions that fit their actual needs.
• Identify opportunities to reuse existing AI components and capabilities across teams. This includes use case discovery, technical solutioning, and making a credible case to partner teams for why reuse is the right path rather than building from scratch.
• Review and validate the outputs of more junior scientists, providing expert-level technical feedback and catching issues before they reach production.
• Shape the direction of projects within the team, bringing experience and a point of view to decisions about approach, tooling, and scope.
• Propose new techniques and methods where current approaches have clear limitations, and take responsibility for evaluating and introducing them.
• Mentor peers and support the growth of colleagues at earlier career stages, through code reviews, pairing, and direct feedback.
• Maintain thorough documentation and uphold the technical health of deliverables over time, not just at point of delivery.

Advanced Specialist, AI Scientist

• Lead the technical design and delivery of complex, multi-component AI systems across our enterprise data platform.
• Define architectural patterns and engineering standards for AI development — RAG, agents, LLM integrations, and MCP-based enterprise connectivity.
• Drive secure, scalable integrations between AI models and enterprise data systems including Snowflake, AWS (SageMaker, Bedrock, S3), and ThoughtSpot.
• Own end-to-end AI solution quality — including evaluation frameworks, monitoring pipelines, cost governance, and production reliability.
• Lead cross-functional collaboration with data engineers, platform architects, and business stakeholders to shape requirements and validate solutions.
• Mentor and coach junior and mid-level engineers, conducting substantive code reviews and contributing to team technical growth.
• Champion AI governance standards — security, data access, PII, prompt injection, and responsible deployment practices.
• Proactively identify and resolve technical risks and architectural gaps; communicate escalations clearly to engineering leadership.
• Contribute to hiring processes — interviewing candidates and helping define role expectations.
• Lead knowledge-sharing sessions and represent the team in technical discussions with senior stakeholders.

Senior Gen AI Software Engineer

• Build and ship Android features using Kotlin.
• Create clean mobile flows for insurance, payments, claims, renewals and financial services.
• Work with product and design to simplify complex user journeys.
• Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
• Improve app performance, crash rate, loading states, responsiveness and memory usage.
• Use analytics, user behaviour and production issues to improve the product.
• Build AI-assisted mobile experiences only where they genuinely improve the user journey.

Mobile Developer – AI Neobank App

• Gather and analyse business requirements for Conversational AI initiatives
• Identify automation opportunities with business stakeholders and clients
• Design chatbot and voicebot conversation flows and user journeys
• Prepare conversation logic, prompts and process documentation
• Configure Conversational AI platforms including Kore.ai, Microsoft Copilot Studio, Dialogflow, Parloa, Cognigy, ElevenLabs or Zowie
• Support API integrations between Conversational AI platforms and enterprise systems
• Configure NLP models, intents, entities and dialogue management
• Support text to speech and voice automation capabilities
• Perform functional, NLP and end to end solution testing
• Identify issues, propose improvements and support successful implementation
• Work closely with Product Owners, Business Analysts, developers and client stakeholders
• Prepare solution documentation and implementation artefacts

Conversational AI Consultant, Developer

• Break down customer problems into work you and the team can execute on.
• Independently complete a majority of identified tasks from start to finish with high quality.
• Use your knowledge of product to mentor others.
• Use your knowledge of Engineering best practices to ask the right questions, solve problems and build great software.
• Clearly communicate technical concepts with others in the organization (Technical Communication, Support, Product and Cloud).
• When you don’t know the solution, seek out the appropriate people in the organization for advice.
• Performs all job responsibilities in alignment with the core values, mission and purpose of the organization.
• Adheres to the highest moral, ethical and legal standards to deliver an environment that promotes respect, innovation and creativity.
• Supports and promotes a positive, inclusive workplace one in which the talents and strengths of our increasingly diverse workforce are welcomed, further developed and manifested in our work.

Software Engineer II, AI Initiatives

• Break down customer problems into work you and the team can execute on.
• Independently complete tasks from start to finish with high quality.
• Ability to communicate technical concepts to stakeholders.
• Use your knowledge of Engineering best practices to ask the right questions, solve problems and build great software with a high level of quality.
• Produce designs for new and existing features.
• Clearly communicate technical concepts with others in the organization (Technical Communication, Support, Product and Cloud).
• Perform all job responsibilities in alignment with the core values, mission and purpose of the organization.
• Adhere to the highest moral, ethical and legal standards to deliver and environment that promotes respect, innovation and creativity.
• Support and promote a positive, inclusive workplace one in which the talents and strengths of our increasingly diverse workforce are welcomed, further developed and manifested in our work.

Senior Software Engineer, AI Initiatives

• Lead the design and development of PoCs and prototypes that demonstrate the potential of emerging AI technologies in content generation, assessment, and innovative AI-driven personalised learning experiences.
• Translate conceptual ideas into working demos contributing to the development of both AI components for innovative concepts, while also building and demonstrating applications with fully integrated front-end interfaces and back-end systems.
• Act as a hands-on technical lead across R&D efforts, setting standards for code quality, scalability, and experimentation.
• Guide decisions and mentor junior peers through complex challenges.
• Partner closely with product managers, designers, researchers, and business stakeholders to shape use cases and validate feasibility.
• Facilitate conversations with non-technical audiences, translating their needs into PoC ideas, and R&D results into business impact.
• Stay ahead of trends in ML, GenAI, LLMs, and AI tooling to identify opportunities for innovation.
• Evaluate new models, frameworks, libraries, and platforms for rapid prototyping and experimentation.
• Share insights and learnings with the broader AI community to foster continuous improvement.

Lead Specialist, AI Scientist

• Develop document-processing pipelines with ML/LLM-based extraction and agentic workflows
• Build and extend Python microservices for document ingestion, classification, extraction, validation, and post-processing
• Develop autonomous AI agents using tool calling, structured outputs, and advanced reasoning
• Prepare evaluation datasets and define ground-truth target values for extracted data
• Evaluate extraction results, analyze failures, and improve prompts, schemas, and pipeline logic
• Build automated testing and regression frameworks for prompts, models, agents, and extraction workflows
• Optimize AI systems for accuracy, latency, throughput, reliability, and cost
• Work with asynchronous processing infrastructure using AWS services such as ECS, SQS, SNS, RDS, and S3
• Use AI-assisted development tools such as Claude Code, Cursor, and similar solutions while following engineering, testing, and code-review best practices
• Prototype new AI solutions in collaboration with engineering teams

Artificial Intelligence / Machine Learning Engineer

• Set the technical direction for AI engineering across the organization — establishing architecture principles, technology choices, and long-term capability roadmap.
• Own the design of the most complex, strategic AI initiatives — from early ideation through production delivery and ongoing evolution.
• Define and enforce engineering standards for AI systems: security, scalability, evaluation, observability, and governance.
• Lead enterprise AI integration strategy — connecting AI models to Snowflake, AWS, ThoughtSpot, and broader data ecosystems via MCP and custom integrations.
• Act as the primary technical authority on LLM systems, agentic architectures, RAG, and emerging AI frameworks within the organization.
• Drive cross-functional alignment between engineering, data, product, and business teams on AI strategy and priorities.
• Champion AI governance at the organizational level — data access, PII, security, cost controls, and responsible deployment.
• Lead code reviews and technical design reviews across teams; elevate the overall quality and consistency of AI engineering practice.
• Present technical vision and AI strategy to senior leadership and external stakeholders.
• Build and scale the AI engineering function — mentoring principals-in-training, defining levelling criteria, and contributing to hiring and team structure.
• Represent Verisk AI engineering externally — thought leadership, technical writing, or industry engagement where relevant.

Machine Learning Engineer jobs in Poland

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