Full-Stack AI Engineer

About the Role

As a Full-Stack AI Engineer at Pavago, you will design, build, and deploy AI-powered applications using modern software engineering best practices. You will integrate Large Language Models (LLMs) and machine learning models into production, build scalable APIs, develop intelligent features like chatbots and semantic search, and manage robust data and MLOps pipelines.

Key Responsibilities

AI Application Development

• Build and deploy AI-powered applications using modern software engineering best practices.
• Integrate LLMs and machine learning models into production environments.
• Develop intelligent features including AI chatbots, semantic search, document intelligence, AI copilots, and workflow automation.
• Build scalable APIs that expose AI capabilities to applications.

LLMs, RAG & AI Integration

• Integrate models using OpenAI, Hugging Face, PyTorch, and TensorFlow.
• Build Retrieval-Augmented Generation (RAG) pipelines.
• Implement semantic search using vector databases including Pinecone, Weaviate, FAISS, and ChromaDB.
• Optimize prompt engineering and inference workflows.
• Monitor model accuracy, latency, and production performance.

Data Engineering & AI Pipelines

• Build ETL pipelines for structured and unstructured data.
• Automate data ingestion, cleaning, validation, and versioning.
• Manage workflows using Airflow, Prefect, and Dagster.
• Work with cloud data warehouses including BigQuery, Snowflake, and Amazon Redshift.
• Optimize pipelines for scalability and cost efficiency.

Full-Stack Development

• Build modern user interfaces using React, Next.js, and Vue.js.
• Develop scalable backend services using Python, FastAPI, Flask, and Node.js.
• Build APIs that support high-performance AI workloads.
• Ensure applications remain responsive, secure, and production-ready.

Infrastructure, DevOps & MLOps

• Deploy applications using Docker and Kubernetes.
• Build CI/CD pipelines for both applications and AI models.
• Monitor infrastructure using MLflow, Weights & Biases, Datadog, and Prometheus.
• Improve inference latency, infrastructure reliability, deployment automation, and cloud cost optimization.

Security & Compliance

• Build secure AI systems using modern authentication and authorization practices.
• Protect sensitive business and customer data.
• Support compliance with GDPR, HIPAA, and SOC 2.
• Implement API security, rate limiting, and access controls.

Qualifications and Requirements

• Education: BA/BSc/HND degree.
• Experience: 3+ years of software engineering experience with exposure to AI/ML systems and deploying machine learning models into production environments.
• Languages/Frameworks: Strong proficiency in Python, JavaScript/TypeScript, React, Next.js, and Vue.js.
• AI Tools: Hands-on experience with OpenAI APIs, Hugging Face, PyTorch, and TensorFlow.
• DevOps: Experience using Docker, Kubernetes, and CI/CD pipelines.