Pavago

Pavago

Pavago specializes in connecting small and medium-sized businesses with skilled offshore talent from Pakistan and Latin America, offering a streamlined recruitment process that enables the hiring, onboarding, and payment of remote employees at signific...

IT Services
Founded 2023

Description

  • Deploy and integrate pre-trained and fine-tuned ML and LLM models into production applications.
  • Build scalable inference APIs and back-end services that connect AI models to business logic.
  • Implement vector search and retrieval systems and design Retrieval-Augmented Generation (RAG) pipelines.
  • Build and maintain ETL pipelines for ingesting, cleaning, transforming, and processing structured and unstructured data.
  • Automate preprocessing, labeling, validation, and versioning workflows for datasets and AI pipelines.
  • Develop front-end interfaces for AI-powered features such as chatbots, dashboards, search, and analytics tools.
  • Containerize services and deploy workloads to Kubernetes environments.
  • Build, maintain, and monitor CI/CD pipelines, infrastructure health, latency, uptime, and operational costs.
  • Implement observability, security controls, and compliance best practices for AI systems.
  • Collaborate with product, engineering, and data teams to productionize models, scope features, and document systems.

Requirements

  • 3+ years of software engineering experience with exposure to AI/ML systems.
  • Strong proficiency in Python and JavaScript/TypeScript.
  • Hands-on experience with AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, or OpenAI APIs.
  • Experience building scalable APIs and back-end systems.
  • Front-end development experience using React, Next.js, Vue, or similar frameworks.
  • Experience deploying machine learning models into production systems.
  • Strong SQL skills and experience with cloud data warehouses.
  • Familiarity with Docker, Kubernetes, and CI/CD workflows.
  • Experience integrating APIs, vector databases, and AI inference services.
  • Experience building and scaling AI-powered SaaS applications (preferred).
  • Hands-on experience with embeddings, fine-tuning, and RAG pipelines (preferred).
  • Familiarity with MLOps platforms such as MLflow, Kubeflow, Vertex AI, or SageMaker (preferred).
  • Experience with serverless architectures and microservices (preferred).
  • Knowledge of prompt engineering and AI workflow optimization (preferred).
  • Experience optimizing inference latency and AI infrastructure costs (preferred).
  • Familiarity with monitoring model drift, evaluation metrics, and AI observability practices (preferred).
  • Full-time availability with working hours aligned to U.S. client business hours, with flexibility for deployments, experimentation cycles, and sprint schedules.

Interested in this position?

Apply directly on the company website

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