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varicent
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Мексика
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SeniorУдалённоПолная занятость

Senior Gen AI Developer (Remote, Mexico)

Оценка ИИ

Отличная позиция в компании-лидере рынка с современным стеком технологий и фокусом на инновации. Полная удаленка и работа над передовыми ИИ-продуктами делают вакансию очень привлекательной для опытных разработчиков.


Вакансия из Quick Offer Global, списка международных компаний
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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует не только сильных навыков бэкенд-разработки (Python/TypeScript), но и практического опыта внедрения GenAI в продакшн. Высокая планка ответственности из-за отсутствия выделенного отдела QA и необходимости работать в часовом поясе EST.

Анализ зарплаты

Медиана85 000 $
Рынок65 000 $ – 110 000 $
Оценка ИИ

Зарплата для Senior AI ролей в Мексике при работе на международные компании обычно выше локального рынка. Указанный диапазон соответствует уровню Senior разработчика в технологическом секторе Латинской Америки.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Gen AI Developer position at Varicent. With over 4 years of experience in software engineering and a deep passion for productionizing AI systems, I am excited about the opportunity to contribute to your market-leading Sales Performance Management solutions. My background in building scalable backend services using Python and TypeScript, combined with my experience in developing automated testing frameworks, aligns perfectly with Varicent's end-to-end engineering culture.

I am particularly drawn to this role because of your focus on moving beyond prototypes to production-grade GenAI features. I have extensive experience with RAG architectures, prompt engineering, and LLMOps, which I believe will be instrumental in enhancing your product's AI capabilities. Furthermore, I am fully prepared to align my schedule with the EST time zone to ensure seamless collaboration with your global team. I look forward to the possibility of bringing my experimentation mindset and technical expertise to Varicent.

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Присоединяйтесь к лидеру рынка SPM и создавайте будущее корпоративного ИИ уже сегодня!

Описание вакансии

At Varicent, we’re not just transforming the Sales Performance Management (SPM) market—we’re redefining how organizations achieve revenue success. Our cutting-edge SaaS solutions empower revenue leaders globally to design smarter go-to-market strategies, maximize seller performance, and unlock untapped potential. Varicent stands at the forefront of innovation, celebrated as a market leader in the 2025 Forrester Wave Report for SPM2023 Ventana Research Revenue Performance Management (RPM) Value IndexGartner Peer Insights2024 Gartner SPM Market Guide, and G2.Our solutions are trusted by a diverse range of global industry leaders like T-Mobile, ServiceNow, Wawanesa Bank, Shaw Industries, Moody's, Stryker and hundreds more. Here’s why you’ll thrive at Varicent:

  • Innovate with Purpose: Build impactful solutions for customers worldwide.
  • Join Excellence: Work in a diverse, collaborative, and innovative team.
  • Shape the Future: Lead in redefining revenue optimization.
  • Grow Together: Unlock your potential in a supportive environment.

Join us at Varicent—where your talent and ambition meet limitless opportunities for success!

At Varicent, we’re driven by the belief that great people power great companies. As our Senior Software Developer, GenAI, you’ll contribute to building and scaling production-grade AI systems that directly impact how our customers use and experience our products.

You’ll contribute to the delivery of GenAI-powered features, collaborate closely with technical leads, and play a key role in maintaining technical quality, reliability, and scalability across a core product area. This role is ideal for a strong individual contributor who enjoys hands-on development, learning new technologies, and turning ideas into high-quality production systems.

Overview

• Build and improve AI-enabled product features from prototype to production. You’ll work within an end-to-end engineering team that owns build quality, release readiness, and ongoing improvements.

What You’ll Do:

• Build and maintain product features and supporting backend components, including services, integrations, and internal workflows.

• Write production-ready code and lightweight prototypes to validate ideas quickly.

• Create automated tests and proactively fix issues to keep releases stable, as engineers own quality without a dedicated QA function.

• Support proof-of-concepts and experiments by setting up test cases, reviewing results, and improving areas where quality is weak.

• Document systems and workflows so others can run, maintain, and extend what you build.

GenAI Competencies You’ll Practice:

• Translate business problems into clear AI tasks and implement end-to-end solutions with guidance from technical leads.

• Prepare and maintain data and test cases used to measure output quality.

• Apply common AI application patterns such as prompting, using internal knowledge sources, and calling external tools or APIs.

• Measure quality using repeatable evaluation methods, including automated checks and structured human review when required.

• Contribute to production readiness through versioning, monitoring, basic performance and cost awareness, and usage tracking.

• Follow privacy, security, and compliance requirements in collaboration with Legal and Security teams.

How We Work:

• We balance exploration with delivery — test ideas quickly, then productionize what works.

• “Done” means quality goals are met, followed by continuous improvement in accuracy, cost, and performance.

• The team owns systems end-to-end, including build, release, and operational reliability.

What You Bring:

• 4+ years of overall software engineering experience, with at least 2+ years building and shipping production systems in product teams.

• 4+ years of hands-on technical experience writing production-quality code.

• Strong software engineering fundamentals, including system design and scalable architecture.

• Proficiency in Python and/or TypeScript (or similar), building APIs, services, and data pipelines.

• Experience with cloud platforms (AWS, GCP, or Azure), CI/CD pipelines, automated testing, and observability.

• An experimentation mindset — able to define metrics, build evaluation frameworks, and iterate quickly in ambiguous problem spaces.

• Strong interest in learning and applying GenAI technologies in practical, production environments.

Nice to Have:

• Hands-on experience with GenAI/LLMs, including prompt engineering, retrieval/RAG, embeddings, tool or function calling, fine-tuning, or evaluation pipelines.

• Familiarity with LLMOps practices such as experiment tracking, version control for prompts or models, and cost or latency optimization.

• Experience with tools such as LangChain/LangGraph, OpenAI, Anthropic, Azure OpenAI, and vector databases like Pinecone, FAISS, or Milvus.

• Contributions to open source, technical blogs, or conference talks.

Education:

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

*Note:

  • This role requires employees to work within Eastern Standard Time (EST) business hours. While we are open to candidates from outside the EST time zone, please be prepared to adjust your working hours to align with this time zone. Flexibility will be essential to ensure seamless collaboration with the team and stakeholders.
  • This position is fully remote, offering you the flexibility to work from anywhere. We embrace a results-driven work culture, focusing on performance and collaboration over location. As part of our team, you’ll have the opportunity to build a work-life balance that suits you, while staying connected with a diverse, global team through virtual tools and regular online communication. Whether you're working from home, a co-working space, or another location of your choice, we’re committed to supporting you with the resources and autonomy needed to succeed in a remote environment.

🍎 *Benefits

  • Market Leading Compensation Package.
  • Wellness Programs to Support Health and Wellbeing.
  • Working with the latest tools and technologies in a fast-paced environment.
  • Remote Work Flexibility.
  • Comprehensive Employee Insurance Coverage: Medical, Dental, Vision, Life Insurance.
  • Annual Time Off: Time off is provided in accordance with applicable legislative requirements.
  • Global Connected Culture: Hubs in Romania, UK, US, Canada.
  • Dynamic Work Culture: Thrive in our innovative and multicultural environment.
  • Grow with Us: Continuous development opportunities.

Varicent is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. If you require accommodation at any time during the recruitment process please email accomodations@varicent.com

Varicent is also committed to compliance with all fair employment practices regarding citizenship and immigration status. By applying for a position at Varicent and/or by using this portal, you declare and confirm that you have read and agree to ourJob Applicant Privacy Notice and that the information provided by you as part of your application is true and complete and includes no misrepresentation or material omission of fact

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Создайте идеальное резюме с помощью ИИ-агента

Создайте идеальное резюме с помощью ИИ-агента

Навыки

  • Python
  • TypeScript
  • Generative AI
  • LLM
  • RAG
  • AWS
  • Azure
  • GCP
  • CI/CD
  • LangChain
  • Pinecone
  • Vector Databases
  • API Development
  • System Design

Возможные вопросы на собеседовании

Проверка опыта работы с основным стеком и понимания архитектуры ИИ-приложений.

Расскажите о вашем опыте перевода GenAI-прототипа в стабильное продакшн-решение. С какими основными трудностями вы столкнулись?

Варицент делает упор на RAG и использование внутренних знаний.

Как бы вы спроектировали систему оценки качества (evaluation pipeline) для RAG-системы, чтобы минимизировать галлюцинации модели?

Инженеры в компании сами отвечают за качество кода.

Как вы подходите к тестированию асинхронных сервисов и интеграций с внешними LLM API, учитывая их вероятностную природу?

Важна экономическая эффективность решений.

Какие стратегии оптимизации стоимости и задержек (latency) вы применяли при работе с крупными языковыми моделями?

Проверка навыков системного дизайна.

Опишите архитектуру масштабируемого конвейера обработки данных для подготовки эмбеддингов в векторной базе данных.

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