Product Information Lead (Technical Publications)

Job description

The Opportunity This role sits inside the Experience Group (EXG), which brings User Experience, Front-end Engineering and Technical Publications together under one organization. Documentation is part of the experience the customer buys, and it is produced alongside design and engineering rather than behind them. User Experience is your primary partner, with Product Management, Engineering and Support close behind. We call the role information development rather than technical writing because writing is one of several skills the job needs. Understanding the customer, structuring content, directing AI and verifying what it produces matter just as much. We have retired our legacy desktop authoring and publishing stack and moved to an AI-assisted, source-controlled production model where AI does the drafting, assembly, translation and first-pass review, and people do the judgment. We are targeting 90 percent accuracy from AI-generated content across dozens of global languages, with human experts placed where accuracy is most at risk. This is a hands-on management role. You will lead a small team, and you will also be in the work: writing and editing, running reviews yourself, and operating the tools daily. We are not looking for someone who has learned to use AI on top of traditional practice. We are looking for someone who combines AI, engineering practice and UX to change how documentation gets made, and who is happy doing it with their hands on the keyboard. This role is based in Bucharest, with a hybrid work model requiring at least 2 days per week in the office (The Bridge 3, Sector 6). Key Responsibilities Lead the team and do the work Lead, coach and grow a small team of information developers, and plan their work against release dates and customer commitments. Stay in production yourself. You will write and edit alongside the team, run reviews, and be close enough to the content to know where it is weak. Help the team shift from writing every sentence to directing AI, reviewing with expertise, and taking responsibility for correctness. Know the customer and the use case Learn our customers' businesses and what they are actually trying to accomplish on our platform. This is the part we do not do enough of today, and it is central to the Work from product requirements and real-world use cases so that content answers what the customer is trying to get done, not only what a feature does. Use support tickets, customer conversations and field feedback to decide what to write next and what to retire. Bring what you learn back to Product and UX. When documentation has to explain a workaround, that is a product signal, and we expect you to raise it. Run the AI production system Operate an AI-first documentation pipeline day to day, using AI as a primary working tool rather than an occasional assistant. Build and maintain the assets that make AI output reliable: prompt libraries, style and terminology rules written as machine-readable instructions, structured source content, and grounded context drawn from specs, code, OpenAPI definitions and the product itself. Work in the pipeline that publishes our content. It runs on Google Cloud Platform, and you will partner with engineering to keep automated build, test and publishing workflows running rather than filing tickets and waiting. Keep our content usable by AI. What we publish increasingly feeds in-product assistants and support automation, so structure, metadata and factual precision are requirements, not editorial preferences. Apply judgment and authorize what ships With your manager, set the rules for which content can be AI-generated and published, which needs expert review, and which needs named human sign-off. Security, billing, regulatory, API contracts and anything a customer acts on operationally sit in the highest tier. Do the reviews that catch what AI gets wrong: factual drift from the source of truth, confident but incorrect technical detail, silent omissions, version mismatches and false consistency across languages. Sign off on published output, and keep accuracy measured: scored, sampled regularly, and reported by content type and by language. Own localization as local language adoption, not translation Deliver documentation across dozens of languages using AI-driven localization, with quality measured by whether the content works for a local reader rather than whether it matches the English word for word. Adapt terminology, examples, regulatory references, formats and tone to local market expectations, working with in-region reviewers and field teams. Maintain the terminology and glossary system that anchors both AI generation and localization, and run a sampling method that gives us confidence without a full human pass on every language. Deliver measurable outcomes Shorten the time from feature-complete to published documentation, in all languages. Reach a verified 90 percent or better accuracy on AI-generated content before human review, and reduce support tickets, integration time and time to first successful API call. Lower cost per language while expanding coverage, and report results in terms the business acts on. What Success Looks Like in the First Year First 90 days. You know the product and our customers hands-on, and one content type is running end to end on the AI pipeline. Six months. The pipeline is the default path for most new content, review and sign-off rules are in place, and accuracy is being measured. Twelve months. Multi-language publishing runs at release pace, accuracy targets are evidenced, and the team's time has shifted from production to judgment. Qualifications Senior experience in technical writing, information development or technical content, including at least 2 years leading or mentoring writers. Demonstrated production use of AI tools to do real documentation work, with solid prompt and context engineering skill. Claude and GitHub Copilot are what we use today, and we care about the capability rather than the badge. Good judgment about AI output. You can spot a confident hallucination, you know which content is too risky to automate, and you can defend where you placed the human. Genuine curiosity about customers and their businesses, and a track record of writing from real use cases rather than from feature lists. Comfort working in a source-controlled publishing workflow using Git and Markdown, and the ability to read specs and code well enough to document APIs accurately. Experience with localization or multi-language content, including managing quality across languages you do not personally speak. Excellent written English and the editorial discipline to set a standard and hold both a team and a model to it. A collaborative, tool-agnostic way of working, comfort sitting inside a UX organization, and comfort with ambiguity, because this process is being built rather than inherited. Bonus Points We do not expect one person to arrive with all of these. Strength in two or three is a good signal, and we will support you in building the rest. Hands-on Google Cloud Platform experience: hosting and storage, CI/CD and build automation, environments and access, logging, and the cost of what you run. Experience building content that powers an AI assistant, chatbot or retrieval system. Experience with structured content, content modeling or single sourcing applied to a modern toolchain. Background in telecommunications, IoT, connectivity or enterprise SaaS platforms. Accessibility and plain language practice, and Agile delivery inside engineering sprint cycles. We Are Tool Agnostic This is not a role for a tool specialist. We have moved away from legacy desktop authoring suites and from translation processes that treat language as a vendor transaction, and we expect the tools named here to change as better ones appear. We are hiring for the ability to understand the customer, direct AI to do the heavy production, work in the pipeline that delivers it, and apply expert judgment where accuracy and customer trust are at stake. Our Process 30 min Talent Team Discussion – Teams. 45 min Hiring Manager Interview – Teams. 60 min Peer Panel Interview (UX/UI & Product) - On-site. 30 min ELT Interview - Teams. Please note that this reflects the current agreed interview process and may be adjusted or updated as the recruitment process progresses. Why Aeris? Join a global leader at the forefront of IoT and connected mobility innovation. Work with strategic enterprise customers and major telecom partners across international markets. Take on a high-impact role with strong cross-functional visibility and opportunities to shape product strategy and outcomes. The base compensation range across Romania is RON 345,041 - 381,984 gross per year. Certain roles may be eligible for bonus. Additional Benefits: 25 + days Vacation; Health Insurance Private plan (EE+ Dependents); Life Insurance; Lunch Allowance; Home Internet WFH supplement. Aeris is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, gender, sexual orientation, gender identity or expression, religion, national origin, marital status, age, disability, veteran status, genetic information, or any other protected status. As part of our hiring process, Aeris may conduct background checks to verify the information provided in your application and assess your suitability for the role. The scope and type of checks will comply with the applicable laws and regulations of the country where the position is based. Additional detail will be provided via the formal application process. About Aeris Communications Inc. Aeris is the secure connectivity platform for Physical AI and intelligent IoT devices. It caters to connected cars, drones, autonomous mobility, robotics, utilities, energy, fleet management, logistics, medical devices, and manufacturing. Aeris serves over 7,000 enterprise customers and connects more than 115 million devices worldwide, including 41 million connected vehicles, supported by an ecosystem of 30+ mobile network operators. Enterprises rely on Aeris for the borderless connectivity, embedded security, observability, and proven scale that mission critical Physical AI programs demand.