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 Originally posted on Himalayas