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