Full-Stack AI Engineer

About the Role

The role requires a strong foundation in both full-stack development and applied AI, as well as excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders. You will be responsible for deploying pre-trained and fine-tuned ML/LLM models, building ETL pipelines for data ingestion and processing, and designing front-end interfaces that surface AI-powered features.

Pavago is committed to innovation and is looking for professionals who are passionate about AI and its applications. The company offers a collaborative and dynamic work environment, with opportunities for professional growth and development.

What You Will Do

• Deploy pre-trained and fine-tuned ML/LLM models using OpenAI, Hugging Face, TensorFlow, and PyTorch
• Build ETL pipelines for ingesting, cleaning, and transforming text, image, or structured data
• Design front-end interfaces using React, Next.js, or Vue to surface AI-powered features
• Implement vector search integrations for retrieval-augmented generation (RAG)
• Develop back-end services and microservices to connect models to business logic
• Ensure responsive, intuitive, and secure interfaces for end users
• Containerize ML services with Docker and deploy to Kubernetes clusters
• Automate CI/CD pipelines for model updates and application releases
• Monitor latency, cost, and model drift with MLflow, Weights & Biases, or custom dashboards
• Collaborate with data scientists to productionize prototypes
• Partner with product teams to scope AI features aligned with business needs

What We Are Looking For

• 3+ years of experience in software engineering with exposure to AI/ML
• Strong foundation in Python (PyTorch, TensorFlow) and JavaScript/TypeScript (React, Node.js)
• Experience deploying ML models into production systems
• Strong SQL and experience with cloud data warehouses (Snowflake, BigQuery, Redshift)
• Analytical problem-solving skills and the ability to balance performance, cost, and usability
• Excellent communication skills and the ability to work collaboratively with cross-functional teams
• Experience with microservices, serverless architectures, and cost-optimized inference
• Knowledge of MLOps practices (Kubeflow, MLflow, Vertex AI, SageMaker)

Nice to Have

• Experience with LLM fine-tuning, embeddings, and RAG pipelines
• Familiarity with containerization using Docker and deployment to Kubernetes clusters
• Knowledge of cloud-based AI platforms (AWS SageMaker, Google Cloud AI Platform, Azure Machine Learning)
• Experience with agile development methodologies and version control systems (Git, GitHub)

Benefits and Perks

• Competitive salary and benefits package
• Opportunities for professional growth and development
• Collaborative and dynamic work environment
• Flexible working hours and remote work options
• Access to cutting-edge AI tools and technologies
• Recognition and reward for outstanding performance
• Comprehensive health insurance and wellness programs
• Generous paid time off and vacation days

• Tip: Make sure to highlight your experience with AI-powered applications and your ability to bridge the gap between software engineering and applied machine learning.
• Tip: Be prepared to discuss your experience with ML/LLM models, ETL pipelines, and front-end interfaces, and how you have applied these skills in previous roles.
• Tip: Show a passion for innovation and a willingness to learn and adapt to new AI tools and technologies.
• Tip: Emphasize your analytical problem-solving skills and your ability to communicate complex technical concepts to non-technical stakeholders.
• Tip: Be prepared to discuss your experience with collaboration tools and version control systems, and how you have worked with cross-functional teams in the past.
• Tip: Highlight your knowledge of MLOps practices and your experience with cloud-based AI platforms.