• 4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
• Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
• B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
• Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
• Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
• Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.

Nice to have: hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.

Not a fit if: your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.

• 5+ years of experience in DevOps/Cloud Engineering, including ownership of production environments
• Strong hands-on experience with AWS and Terraform
• Experience with Kubernetes (preferably EKS), Docker and Helm
• Experience with CI/CD tools: GitHub Actions, GitLab CI or similar
• Strong Python skills: writing automation and tooling, and reading, debugging and supporting backend services
• Experience integrating applications with identity providers via SSO (OIDC/SAML), e.g., Okta or Microsoft Entra ID
• Understanding of AI agent architecture and its components (LLM APIs, vector databases, agent frameworks) from an infrastructure perspective
• Ability to work independently and communicate directly with client stakeholders

Nice to have:

• Familiarity with AI platforms and tools: AWS Bedrock, LangChain/LangGraph, vector databases (e.g., ChromaDB), Langfuse
• Experience with FastAPI
• Experience in pharma, life sciences or other regulated industries
• AWS certifications

• 5–8 years of experience working in laboratory settings, with a strong understanding of laboratory and R&D workflows
• At least 5 years of hands-on experience with Revvity Signals ELN / Signals Notebook
• Strong experience configuring Signals ELN, including templates, workflows, system settings, roles, and permissions
• Ability to translate scientific and business requirements into effective ELN configurations
• Experience working closely with Business Analysts and scientific users
• Strong troubleshooting, communication, and stakeholder management skills

Nice to have:

• Hands-on experience with Revvity Synergy
• Familiarity with Signals APIs, integrations, or data migration activities

— 2+ years of product design for web and mobile interfaces

— Confident Figma skills: components, auto layout, design systems

— Research experience: interviews, usability tests

— Portfolio with case studies

— Experience designing B2B systems or AI interfaces is a plus

— 2+ years of 1C development or integrations

— Data exchange experience: REST, OData, HTTP services, XML and JSON

— Experience with IS ESF and electronic invoices

— Understanding of accounting in Kazakhstan

— Python or JavaScript is a plus

— Attention to detail and accuracy

— Fluent Russian; Kazakh is a big plus

— Confident with spreadsheets and online tools

— Interest in AI; data annotation experience is a plus

— Suitable for students

— 1+ year of working with LLMs in production

— Ability to write system prompts, few-shot examples and structured outputs

— Understanding of model limitations: hallucinations, context window, cost

— Basic Python for experiments and automation

— Fluent Russian; Kazakh and English are a plus

— 1+ year of Python, confident work with data (pandas, SQL)

— Understanding of how LLMs work and where they fail

— Experience building test sets and quality metrics, including LLM-as-a-judge

— Familiarity with evals tools (promptfoo, Langfuse, Braintrust or similar) is a plus

— Attention to detail and love for numbers

— 2+ years of development in Python or TypeScript

— Experience with LLM APIs (Claude, OpenAI or similar): tool use, structured output, streaming

— Understanding of agent patterns: agent loop, tool calling, context and memory management

— Experience with MCP, LangGraph, Agent SDKs or custom orchestrators is a plus

— Reliable code: error handling, retries, logging

— 1+ year of creating content for social media, blogs or YouTube

— Ability to explain tech topics in simple terms

— Short-form video shooting and editing skills (Reels, Shorts)

— Interest in AI and development, experience with AI tools for content

— Portfolio with work samples