Full - stack AI Engineer - Remote Eligible

Full-Stack Ai Engineer

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.

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?

• 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:

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