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
