Our client is looking for a Full-Stack AI Engineer to join their team on a full-time, remote basis. This role focuses on designing, building, and deploying AI-powered applications that integrate machine learning models into scalable, reliable, and user-friendly production systems. The successful candidate will bridge software engineering and applied machine learning, working across back-end services, front-end interfaces, and ML pipelines to deliver practical AI solutions aligned with business objectives. The position requires availability during U.S. client business hours, with some flexibility for deployments and sprint cycles.
Key Responsibilities
AI Model Integration: Deploy and fine-tune machine learning and large language models using frameworks such as OpenAI, Hugging Face, TensorFlow, and PyTorch. Develop scalable APIs with FastAPI, Flask, or Node.js to support model inference. Implement vector search solutions like Pinecone, Weaviate, or FAISS to enable retrieval-augmented generation (RAG).
Data Engineering & Pipelines: Build ETL pipelines for ingesting, cleaning, and transforming diverse data types including text, images, and structured data. Automate data labeling, preprocessing, and versioning workflows using tools like Airflow, Prefect, or Dagster. Manage datasets in cloud data warehouses such as Snowflake, BigQuery, or Redshift.
Application Development (Full-Stack): Create front-end user interfaces with React, Next.js, or Vue to showcase AI-driven features such as chatbots, dashboards, and analytics. Design back-end services and microservices that connect AI models to business logic. Ensure interfaces are responsive, intuitive, and secure.
Infrastructure & Deployment: Containerize machine learning services using Docker and deploy them on Kubernetes clusters. Automate CI/CD pipelines for continuous model updates and application releases. Monitor system latency, cost, and model drift using MLflow, Weights & Biases, or custom dashboards.
Security & Compliance: Ensure AI systems comply with data privacy regulations including GDPR, HIPAA, and SOC 2. Implement security measures such as rate limiting, access control, and secure API endpoints.
Collaboration & Iteration: Work closely with data scientists to transition prototypes into production-ready solutions. Partner with product teams to define AI features that meet business needs. Document systems thoroughly to ensure reproducibility and knowledge sharing.
Required Qualifications
- At least 3 years of software engineering experience with exposure to AI/ML projects.
- Proficiency in Python (including PyTorch and TensorFlow) and JavaScript/TypeScript (React, Node.js).
- Proven experience deploying machine learning models into production environments.
- Strong SQL skills and familiarity with cloud data warehouses.
- Ability to balance performance, cost, and usability in AI system design.
Preferred Qualifications and Benefits
- Experience building and scaling AI-powered SaaS products.
- Knowledge of large language model fine-tuning, embeddings, and retrieval-augmented generation pipelines.
- Familiarity with MLOps tools and practices such as Kubeflow, MLflow, Vertex AI, or SageMaker.
- Understanding of microservices, serverless architectures, and cost-optimized inference strategies.
- A curious and adaptable mindset, staying current with emerging AI and LLM technologies.
A typical day includes refining model APIs to optimize latency and accuracy, developing front-end components to expose AI features, maintaining data pipelines for training and fine-tuning, deploying updates via CI/CD workflows, and collaborating with cross-functional teams to prioritize impactful AI capabilities. Thorough documentation and knowledge transfer are essential to ensure solutions remain scalable and reproducible.
Success in this role will be measured by the timely deployment of AI features, maintaining application uptime of 99.9% or higher, keeping inference latency below 500ms on key endpoints, reducing manual workflows through AI automation, ensuring stable model performance, and achieving positive user adoption and satisfaction.
The interview process involves an initial phone screen, a video interview with a recruiter, a technical assessment requiring deployment of a small ML model with API and front-end integration, followed by client interviews with the engineering team, and concludes with an offer and background verification.
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