Full-Stack AI Engineer at Pavago

About the Role
Our client is seeking a Full-Stack AI Engineer to design, build, and deploy AI-powered applications that bridge modern software engineering with applied machine learning. This role focuses on taking AI solutions from prototype to production — ensuring systems are scalable, reliable, secure, and optimized for real-world business impact.

• The ideal candidate combines strong full-stack engineering skills with hands-on experience integrating LLMs, machine learning models, vector databases, and AI workflows into production environments. You will work closely with product, engineering, and data teams to build intelligent applications that improve automation, user experience, and operational efficiency.
• This is a highly technical, execution-focused role for someone comfortable owning AI systems end-to-end — from infrastructure and APIs to front-end experiences and deployment pipelines.

Responsibilities

AI Model Integration & Deployment

•  Deploy and integrate pre-trained and fine-tuned ML/LLM models using platforms such as OpenAI, Hugging Face, TensorFlow, and PyTorch
•  Build scalable inference APIs using FastAPI, Flask, Node.js, or similar frameworks
•  Implement vector search and retrieval systems using Pinecone, Weaviate, FAISS, or ChromaDB
•  Design and optimize Retrieval-Augmented Generation (RAG) pipelines for AI-powered applications
•  Monitor model accuracy, latency, and operational performance in production environments

Data Engineering & AI Pipelines

•  Build ETL pipelines for ingesting, cleaning, transforming, and processing structured and unstructured datasets
•  Automate data preprocessing, labeling, validation, and versioning workflows
•  Manage datasets and pipelines using Airflow, Prefect, Dagster, or similar orchestration tools
•  Store and manage datasets in cloud data warehouses such as BigQuery, Snowflake, or Redshift
•  Optimize pipelines for scalability, reliability, and cost efficiency

Full-Stack Application Development

•  Build front-end interfaces in React, Next.js, or Vue for AI-powered features such as chatbots, dashboards, search, and analytics tools
•  Develop scalable back-end services and microservices that connect AI models to business logic
•  Ensure applications are responsive, secure, intuitive, and production-ready
•  Design APIs and services that support high concurrency and scalable AI workloads

Infrastructure, DevOps & Deployment

•  Containerize services using Docker and deploy workloads to Kubernetes environments
•  Build and maintain CI/CD pipelines for application and model deployments
•  Monitor infrastructure health, inference latency, system uptime, and operational costs
•  Implement observability and monitoring using MLflow, Weights & Biases, Datadog, Prometheus, or custom dashboards
•  Optimize AI inference performance and infrastructure costs across environments

Security & Compliance

•  Ensure AI systems comply with GDPR, HIPAA, SOC 2, and other applicable data privacy standards
•  Implement secure authentication, access controls, rate limiting, and API security best practices
•  Maintain secure handling of sensitive user and business data

Collaboration & Product Development

•  Work closely with data scientists to productionize experimental models and prototypes
•  Partner with product and engineering teams to scope and prioritize AI-driven features
•  Contribute to architecture discussions and technical planning
•  Document workflows, APIs, infrastructure, and AI systems for maintainability and reproducibility

What Makes You a Perfect Fit

•  Strong engineer with hands-on experience across both software development and applied AI/ML
•  Comfortable moving quickly from experimentation to production deployment
•  Analytical problem solver who balances scalability, latency, usability, and cost
•  Curious and adaptable, constantly exploring emerging AI frameworks, tools, and workflows
•  Ownership-driven with the ability to independently execute complex technical initiatives
•  Strong communicator capable of collaborating across technical and non-technical teams

Required Experience & Skills

•  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

Ideal Experience & Skills

•  Experience building and scaling AI-powered SaaS applications
•  Hands-on experience with embeddings, fine-tuning, and RAG pipelines
•  Familiarity with MLOps platforms such as MLflow, Kubeflow, Vertex AI, or SageMaker
•  Experience with serverless architectures and microservices
•  Knowledge of prompt engineering and AI workflow optimization
•  Experience optimizing inference latency and AI infrastructure costs
•  Familiarity with monitoring model drift, evaluation metrics, and AI observability practices

What Does a Typical Day Look Like?
A Full-Stack AI Engineer’s day revolves around building and optimizing production-grade AI systems. You will:

•  Develop and refine APIs that expose AI and LLM functionality
•  Build front-end interfaces that surface AI-powered workflows to end users
•  Maintain and optimize ETL pipelines for AI model training and inference
•  Deploy updates through CI/CD pipelines and monitor production performance
•  Troubleshoot latency, scaling, or infrastructure bottlenecks
•  Collaborate with product and data teams to prioritize impactful AI features
•  Document systems and workflows to ensure scalability and maintainability

In essence: you are responsible for turning AI capabilities into reliable, scalable, and user-friendly production applications.

Key Metrics for Success (KPIs)

•  Successful deployment of AI-powered features on schedule
•  Application uptime ≥ 99.9%
•  Inference latency maintained below target thresholds
•  Reliability and scalability of AI systems in production
•  Reduction in manual workflows through automation and AI integration
•  Stable model performance and monitoring accuracy over time
•  Positive adoption and usage of AI-driven features by end users
•  Infrastructure and inference cost optimization improvements