About the job
Job Title: Full-Stack AI Engineer
Position Type: Full-Time, Remote
Working Hours: Align with U.S. client business hours, with flexibility for model deployments, experimentation cycles, and sprint schedules.
About the Role:
We are in search of a talented Full-Stack AI Engineer who will be responsible for designing, building, and deploying cutting-edge AI-powered applications. This pivotal role involves merging software engineering with applied machine learning, ensuring seamless integration of models into scalable, reliable, and user-friendly production systems. The ideal candidate will proficiently blend back-end services, front-end interfaces, and machine learning pipelines to deliver impactful, business-oriented AI solutions.
Key Responsibilities:
AI Model Integration:
- Deploy pre-trained and fine-tuned ML/LLM models using frameworks such as OpenAI, Hugging Face, TensorFlow, and PyTorch.
- Wrap models within APIs (FastAPI, Flask, Node.js) for scalable inference.
- Implement vector search integrations (Pinecone, Weaviate, FAISS) to enable retrieval-augmented generation (RAG).
Data Engineering & Pipelines:
- Construct ETL pipelines for the ingestion, cleaning, and transformation of various data types including text, images, or structured datasets.
- Automate data labeling, preprocessing, and versioning using tools such as Airflow, Prefect, or Dagster.
- Manage and store datasets effectively within cloud data warehouses like Snowflake, BigQuery, or Redshift.
Application Development (Full-Stack):
- Develop intuitive front-end user interfaces in frameworks like React, Next.js, or Vue to showcase AI-powered features such as chatbots, dashboards, and analytics.
- Design robust back-end services and microservices to connect AI models with business logic.
- Ensure user interfaces are responsive, intuitive, and secure.
Infrastructure & Deployment:
- Utilize Docker to containerize ML services and deploy them within Kubernetes clusters.
- Automate CI/CD pipelines for efficient model updates and application releases.
- Monitor key parameters such as latency, cost, and model drift using MLflow, Weights & Biases, or custom dashboards.
Security & Compliance:
- Guarantee that AI systems adhere to data privacy standards including GDPR, HIPAA, and SOC 2.
- Implement security measures such as rate limiting, access control, and secure API endpoints.
Collaboration & Iteration:
- Collaborate with data scientists to transition prototypes into production.
- Engage with product teams to define AI features that align with business objectives.
- Document systems to ensure reproducibility and facilitate knowledge transfer.

