Vodex.ai

Role Description As we onboard more customers and expand our platform, we're investing heavily in the intelligence that powers our AI agents. You'll help build the next generation of machine learning systems that improve conversation quality, decision making, and operational efficiency at scale. This is an opportunity to work across the entire ML lifecycle—from defining business problems and designing experiments to deploying, monitoring, and continuously improving production models. You'll have the freedom to influence both our ML platform and the products it enables. Key Responsibilities - Design, build, and maintain production machine learning systems that power customer-facing AI capabilities. - Train, evaluate, and optimize machine learning models, selecting the right approach—from traditional statistical methods to modern deep learning architectures—based on business needs and operational constraints. - Partner with product managers, engineering teams, and business stakeholders to translate real-world problems into measurable machine learning solutions. - Design experiments, evaluate model performance, and use data-driven insights to influence product direction. - Build scalable feature engineering, training, and inference pipelines for both batch and real-time workloads. - Work closely with platform and backend engineers to integrate ML models into production services with reliability, observability, and maintainability in mind. - Continuously improve model quality through monitoring, retraining, and iterative experimentation. - Mentor other engineers and help establish engineering best practices around code quality, testing, documentation, and operational excellence. Our Engineering Culture - We're a small engineering team where machine learning engineers own solutions end-to-end—from understanding the business problem through experimentation, deployment, monitoring, and continuous improvement. - We value: - Building models that solve real customer problems rather than optimizing benchmark scores. - Strong engineering fundamentals alongside strong machine learning expertise. - Data-driven decision making and measurable business impact. - Simple, maintainable solutions over unnecessary complexity. - Collaborative technical discussions and continuous learning. - High standards for testing, monitoring, and production reliability. Our Technology Stack - Languages: Python, SQL - Machine Learning: PyTorch, TensorFlow, Keras - Cloud: Google Cloud Platform (GCP) - Data: BigQuery, Kafka, Spark, modern data processing pipelines - ML Operations: Automated training pipelines, model monitoring, retraining, experimentation - Development: GitHub Actions, automated testing, CI/CD What We're Looking For - 5–7 years of applied machine learning experience using Python. - A strong understanding of machine learning fundamentals, modern deep learning techniques, and when to apply different modeling approaches. - Building, deploying, and maintaining production ML systems in fast-moving environments. - Designing experiments and analyses that influence product decisions and business outcomes. - Working with deep learning frameworks such as PyTorch, TensorFlow, or Keras, with an understanding of how they work beyond simply using their APIs. - Building scalable data pipelines and working with large datasets using technologies such as BigQuery, Kafka, Spark, Hadoop, or similar platforms. - Applying MLOps best practices including automated testing, model versioning, monitoring, retraining, and production observability. - Quickly understanding new business domains and translating ambiguous problems into practical machine learning solutions. - Working collaboratively within agile engineering teams and adapting to changing priorities. - Experience building and operating machine learning workloads on Google Cloud Platform. Nice to Have - Experience with any of the following is a bonus: - Large Language Models (LLMs) and generative AI applications. - Conversational AI, speech technologies, or voice applications. - Recommendation systems, ranking models, or personalization. - Production feature stores and online inference systems. - Distributed model training and optimization. - Building evaluation frameworks for AI systems. You'll Enjoy This Role If You... - Like solving real business problems using machine learning rather than building models in isolation. - Enjoy taking ownership of ML systems from experimentation through production. - Care about model reliability, maintainability, and measurable business impact. - Prefer working closely with product and engineering teams to build end-to-end solutions. - Want to influence the direction of both the ML platform and the products it powers. - Enjoy working in a small team where your ideas and contributions have a direct impact.

Role Description We're in the middle of one of the most exciting phases of our platform's evolution. As we onboard more customers and expand our platform, we're investing heavily in scaling our backend for reliability, performance, and operational excellence. You'll help build the systems and engineering foundations that enable us to grow with confidence. You'll join while the platform architecture is still taking shape. The systems and patterns you build today will become the foundation the engineering team builds upon for years to come. This is an opportunity to own core platform components—not maintain someone else's legacy system. Key Responsibilities - Design and build backend services in TypeScript running on Cloud Run and GKE. - Own critical request paths powering live phone calls, building systems that meet strict latency and reliability objectives. - Design reliable event-driven systems with idempotent processing, safe concurrency, retry handling, and resilient work queues. - Build compliance-critical services that enforce calling windows, frequency limits, Do Not Call policies, and maintain immutable audit trails. - Improve platform reliability through structured logging, distributed tracing, metrics, SLOs, graceful deployments, and safe rollbacks. - Integrate with external providers including voice, SMS, payments, and authentication services. - Build comprehensive automated tests covering both business logic and production-critical workflows. Our Engineering Culture - We're a small engineering team where every engineer owns services end-to-end—from design and implementation through deployment, monitoring, and production support. - We value: - Simple, maintainable solutions over unnecessary complexity. - Strong ownership and accountability. - Data-driven debugging through logs, metrics, and traces. - Thorough code reviews and collaborative technical design. - Automated testing and continuous delivery. - Building systems that are easy for the next engineer to understand. Our Technology Stack - Programming Language: TypeScript (Node.js) - Cloud Platform: Google Cloud Platform (Cloud Run, GKE, Secret Manager, Artifact Registry) - Database: PostgreSQL (currently using Supabase, with plans to migrate to Cloud SQL) - Observability & Monitoring: OpenTelemetry, Structured Logging, Metrics - CI/CD: GitHub Actions - Integrations: Vodex.ai, VAPI, Stripe, Google APIs, DocuSign, and SMS providers What We're Looking For - Engineers who have spent the last several years building and operating production backend systems. - Experience with: - Designing and operating production backend services using TypeScript, Node.js, or similar technologies. - Writing efficient SQL and understanding transactions, indexing, concurrency, and database performance. - Building distributed systems that handle retries, idempotency, eventual consistency, and high levels of concurrent traffic. - Running applications on a modern cloud platform (GCP preferred, AWS or Azure also welcome), including containers, autoscaling, secrets, IAM, and deployment pipelines. - Instrumenting applications with logs, metrics, and traces to diagnose production issues effectively. - Building software that is easy to understand, well tested, and maintainable over time. - Making thoughtful engineering trade-offs while balancing correctness, simplicity, and delivery speed. - We value engineers who understand why systems fail—not just how to fix them. Nice to Have - Experience with any of the following is a bonus: - Voice, telephony, or real-time communication systems. - AI applications involving live interactions. - Regulated industries such as fintech, healthcare, insurance, or debt collections. - Zero-downtime migrations between platforms or databases. - Kubernetes and long-running worker systems. - Infrastructure as Code. - Multi-tenant SaaS platforms. You'll Enjoy This Role If You... - Like owning systems from design through production. - Enjoy solving infrastructure and platform problems rather than building user interfaces. - Care deeply about correctness, observability, and operational excellence. - Prefer straightforward, maintainable code over clever abstractions. - Want to influence architecture rather than inherit it. - Enjoy working in a small team where decisions are made quickly and your work has visible impact.

Role Description As a Voice Bot Prompt Writer, you will play a crucial role in crafting engaging and effective prompts for voice-enabled applications, ensuring a seamless and user-friendly conversational experience. Your responsibilities will involve: - Prompt Creation: Develop conversational prompts that guide users through interactions with voice bots in a natural and user-friendly manner. - Design prompts that are clear, concise, and tailored for effective communication in a voice-based environment. - User-Centric Approach: Understand user behaviors, preferences, and expectations to create prompts that enhance the overall user experience. - Anticipate user needs and design prompts that facilitate smooth and intuitive voice interactions. - Tone and Style: Establish and maintain a consistent and appropriate tone for the voice bot, aligning with the brand or application's identity. - Adapt the writing style to suit different scenarios and user demographics. - Persona Development: Contribute to the development of the voice bot's persona by creating prompts that reflect a cohesive and engaging personality. - Ensure that the voice bot's responses align with the intended persona and brand identity. - Scripting: Write scripts for various voice bot scenarios, including greetings, responses to user queries, error handling, and more. - Collaborate with developers to integrate scripts seamlessly into the voice bot's functionality. - Localization: Adapt prompts for different languages and cultural nuances to create a globally inclusive user experience. - Collaborate with localization teams to ensure accurate and culturally sensitive translations. - Testing and Optimization: Conduct thorough testing of voice bot prompts to identify areas for improvement. - Collaborate with the development and quality assurance teams to optimize prompt performance based on user feedback and data analysis. - Documentation: Maintain detailed documentation of voice bot prompt guidelines, best practices, and any specific requirements for developers and stakeholders. Qualifications - Education: Bachelor's degree in linguistics, communication, creative writing, or a related field. - Experience: Fresher. - Communication Skills: Exceptional verbal and written communication skills in English with a keen understanding of conversational nuances. - Creativity: A creative mindset with the ability to infuse personality and warmth into voice bot interactions. - Adaptability: Ability to adapt writing style and prompts for different industries, applications, and user demographics. - Technical Understanding: Basic understanding of voice bot technologies, natural language processing, and voice user interface (VUI) design principles.