Full-Stack AI Engineer

Job Title: Full-Stack AI Engineer

Position Type: Full-Time, Remote

Working Hours: U.S. client business hours (with flexibility for model deployments, experimentation cycles, and sprint schedules)

About the Role:

Our client is seeking a Full-Stack AI Engineer to design, build, and deploy AI-powered applications. This role requires bridging software engineering with applied machine learning, ensuring that models are integrated into production systems that are scalable, reliable, and user-friendly. The Full-Stack AI Engineer combines back-end services, front-end interfaces, and machine learning pipelines to deliver practical, business-driven AI solutions.

Responsibilities:

AI Model Integration:

- Deploy pre-trained and fine-tuned ML/LLM models (OpenAI, Hugging Face, TensorFlow, PyTorch). - Wrap models in APIs (FastAPI, Flask, Node.js) for scalable inference. - Implement vector search integrations (Pinecone, Weaviate, FAISS) for retrieval-augmented generation (RAG). Data Engineering & Pipelines:

- Build ETL pipelines for ingesting, cleaning, and transforming text, image, or structured data. - Automate data labeling, preprocessing, and versioning with Airflow, Prefect, or Dagster. - Store and manage datasets in cloud warehouses (Snowflake, BigQuery, Redshift). Application Development (Full-Stack):

- Build front-end UIs in React, Next.js, or Vue to surface AI-powered features (chatbots, dashboards, analytics). - Design back-end services and microservices to connect models to business logic. - Ensure responsive, intuitive, and secure interfaces for end users. Infrastructure & Deployment:

- 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. Security & Compliance:

- Ensure AI systems comply with data privacy standards (GDPR, HIPAA, SOC 2). - Implement rate limiting, access control, and secure API endpoints. Collaboration & Iteration:

- Work with data scientists to productionize prototypes. - Partner with product teams to scope AI features aligned with business needs. - Document systems for reproducibility and knowledge transfer. What Makes You a Perfect Fit:

- Strong coder with a foundation in both full-stack development and applied ML/AI. - Comfortable building prototypes and scaling them to production-grade systems. - Analytical problem solver who balances performance, cost, and usability. - Curious and adaptable, staying current with emerging AI/LLM tools and frameworks. Required Experience & Skills (Minimum):

- 3+ years in software engineering with exposure to AI/ML. - Proficiency 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. Ideal Experience & Skills:

- Built and scaled AI-powered SaaS products. - Experience with LLM fine-tuning, embeddings, and RAG pipelines. - Knowledge of MLOps practices (Kubeflow, MLflow, Vertex AI, SageMaker). - Familiarity with microservices, serverless architectures, and cost-optimized inference. What Does a Typical Day Look Like?

A Full-Stack AI Engineer’s day revolves around connecting models to real-world applications. You will:

- Review and refine model APIs, testing latency and accuracy. - Write front-end code to surface AI features in user-friendly interfaces. - Maintain pipelines that clean and prepare new datasets for training or fine-tuning. - Deploy updates through CI/CD pipelines, monitoring cost and performance post-release. - Collaborate with product and data science teams to prioritize AI features that solve real user problems. - Document workflows and results so solutions are repeatable and scalable. In essence: you ensure AI moves from prototype to production — reliable, compliant, and impactful.

Key Metrics for Success (KPIs):

- Successful deployment of AI features to production on schedule. - Application uptime ≥ 99.9% and inference latency < 500ms for key endpoints. - Reduction in manual workflows replaced by AI features. - Model performance tracked and stable (accuracy, drift, false positives/negatives). - Positive user adoption and satisfaction of AI-driven features. Interview Process:

- Initial Phone Screen - Video Interview with Pavago Recruiter - Technical Assessment (e.g., deploy a small ML model with API endpoints and basic front-end integration) - Client Interview(s) with Engineering Team - Offer & Background Verification