About Volga Partners  Volga Partners is a global leader in generative AI (GenAI) innovation, building and deploying advanced AI solutions that drive real-world business transformation. We design intelligent systems, custom large language model (LLM) applications, and next-generation automation that enable organizations to operate smarter, faster, and more efficiently.  Our mission is to make GenAI practical, scalable, and impactful. We go beyond implementation—specializing in refining and enhancing LLMs with high accuracy across complex, high-value domains including finance, healthcare, legal, and technology. By tailoring models to specific use cases, we enable our partners to achieve measurable outcomes and unlock new growth opportunities.  With a global workforce of 3,000+ professionals across multiple countries, Volga Partners brings together diverse perspectives, cultures, and expertise. This diversity fuels innovation and allows us to deliver solutions that are both globally relevant and locally effective.  At Volga Partners, you’ll join a collaborative, forward-thinking team solving complex problems, exploring new frontiers in artificial intelligence, and shaping the future of intelligent technology.  Overview  We are hiring a mid-level AI Engineer to build and ship AI-powered features end-to-end.  This is not a junior role — candidates must have hands-on experience delivering production-ready AI solutions and the ability to independently execute in a fast-paced environment.  You will work closely with founders and product teams to develop intelligent systems such as copilots, chatbots, and automation tools, while ensuring performance, scalability, and reliability.    Key Responsibilities  - Build and ship AI-powered features (chatbots, copilots, automation tools)   - Design and implement RAG pipelines using embeddings and vector search   - Write and optimize prompts and system instructions   - Integrate LLM APIs (OpenAI, Anthropic, etc.) into production systems   - Develop backend services (Python / Node.js) to support AI workflows   - Work with real-world data (documents, logs, APIs)   - Improve system reliability through evaluations, testing, and iteration   - Debug issues such as hallucinations, latency, and cost inefficiencies   - Collaborate closely with founders and product team on product direction   Requirements  - 2+ years of experience in Machine Learning or AI-related roles   - Strong coding skills in Python or JavaScript   - Hands-on experience working with LLM APIs   - Experience building RAG pipelines or agent-based systems   - Solid understanding of ML fundamentals (fine-tuning is a plus)   - Proven ability to build and ship production-ready systems   Tech Stack  - Python, Node.js   - OpenAI / Anthropic APIs   - Vector databases (Pinecone, Chroma)   - React / Next.js   - AWS / Vercel   - LangChain / LlamaIndex   - Prompt engineering   - Embeddings + vector search   - API design and backend systems     Nice to Have  - Experience with LLM evaluation, fine-tuning, or model optimization   - Exposure to agent frameworks or multi-step reasoning systems   - Domain experience in finance, healthcare, legal, or other high-accuracy environments  What we Offer  - Salary: $2,500 – $3,500 USD/month (based on experience and alignment with role expectations)  - Opportunity to own and shape a meaningful internal product  - Collaborative, low-bureaucracy environment  - Exposure to cutting-edge AI and automation technologies

• Architect and operate data-centric systems handling large-scale datasets on AWS • Focus on Snowflake optimization including data modeling, performance tuning, and cost efficiency • Design and orchestrate complex workflows using Apache Airflow • Build data transformations with dbt including modeling, testing, and documentation • Implement both real-time and batch data pipelines leveraging AWS services like S3 • Manage core cloud infrastructure through Infrastructure as Code using Terraform • Ensure engineering excellence through CI/CD pipelines with GitHub Actions • Conduct comprehensive automated testing (unit, linting, integration, end-to-end) • Utilize observability tools like New Relic • Collaborate through Git/GitHub workflows

• Provide technical leadership and architectural guidance across five Core product teams, serving as the primary backend staff engineer for this area of the product. • Float across teams where support is needed: stepping in on high-priority projects, unblocking engineers, and ensuring work gets over the line without dropping the thread on your broader responsibilities. • Design, evolve, and support backend systems and architecture primarily in Ruby on Rails, PostgreSQL, REST, and related technologies, with a focus on performance and reliability at scale. • Get ahead of technical problems — proactively identifying, forecasting, and mitigating risk before it becomes someone else's emergency. • Ensure strong observability across Core systems, including Datadog dashboards, monitoring, and alerting. • Influence long-term technical strategy, balancing short-term delivery with platform health, scalability, and sustainability. • Create clarity around technical decisions, trade-offs, and direction in ambiguous or high-impact situations. • Set and uphold high standards for code quality, security, privacy, observability, and documentation through direct contribution, review, and mentorship. • Mentor senior backend engineers and, over time, help frontend-focused engineers build stronger Ruby on Rails skills. • Act as a force multiplier, enabling teams and individuals without overshadowing or sidelining others.

• Conhecer progressivamente os produtos Versat ERP e Versat CRM. • Aprender a metodologia de trabalho da equipe e as boas práticas “ágeis”. • Participar ativamente das cerimônias ágeis e entender o fluxo de trabalho. • Formar-se no Framework Versat utilizando C#, para contribuir no desenvolvimento e correção de funcionalidades sob supervisão. • Analisar tarefas simples junto ao time e entregar soluções alinhadas aos planejamentos. • Apoiar na correção de erros e melhorias de funcionalidades já existentes. • Aplicar boas práticas de codificação, recebendo feedback em revisões de código. • Manter ou ajustar partes do código conforme orientações da equipe sênior. • Elaborar passos básicos de testes para validar suas entregas. • Comunicar avanços, dúvidas e mudanças de forma clara e oportuna. • Desenvolver seus OKRs individuais, com acompanhamento da equipe.