Full-Stack AI Engineer at Pavago

, , Portugal -

Full Time

Start Date

Immediate

Expiry Date

06 Aug, 26

Salary

0.0

Posted On

08 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, JavaScript, TypeScript, React, Next.js, Vue, PyTorch, TensorFlow, LangChain, Hugging Face, FastAPI, SQL, Docker, Kubernetes, RAG, MLOps

Industry

Staffing and Recruiting

Description

Job Title: Full-Stack AI Engineer Position Type: Full-Time, Remote Working Hours: U.S. client business hours (with flexibility for deployments, experimentation cycles, and sprint schedules) About the Role Our client is seeking a highly skilled Full-Stack AI Engineer to design, build, and deploy scalable AI-powered applications that solve real-world business problems. This role bridges software engineering with applied machine learning, combining front-end development, back-end systems, AI model integration, and cloud infrastructure into production-ready applications. You will work across the full product lifecycle — from experimentation and prototyping to deployment, optimization, and monitoring. The ideal candidate is both technically strong and execution-focused, capable of building AI-driven systems that are scalable, reliable, performant, and user-friendly. ResponsibilitiesAI Model Integration & LLM Systems • Deploy and integrate pre-trained and fine-tuned ML / LLM models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks • Build scalable AI inference APIs using FastAPI, Flask, Node.js, or similar technologies • Implement retrieval-augmented generation (RAG) pipelines using vector databases such as Pinecone, Weaviate, Chroma, or FAISS • Optimize prompt engineering, embeddings, and AI workflows for performance, accuracy, and cost efficiency Full-Stack Application Development • Build responsive front-end applications using React, Next.js, Vue, or similar frameworks • Develop back-end services and APIs connecting AI systems to business workflows and user-facing applications • Design scalable architectures for chatbots, AI assistants, analytics dashboards, search systems, and workflow automation tools • Ensure applications are intuitive, secure, responsive, and production-ready Data Engineering & Pipeline Development • Build ETL/ELT pipelines for ingesting, cleaning, transforming, and processing structured and unstructured datasets • Automate data preprocessing, versioning, labeling, and pipeline orchestration using Airflow, Prefect, Dagster, or similar tools • Store and manage datasets within cloud warehouses such as Snowflake, BigQuery, or Redshift • Maintain reliable data flows supporting training, inference, analytics, and AI operations Infrastructure, Deployment & MLOps • Containerize AI services using Docker and deploy workloads to Kubernetes or cloud-native environments • Build and maintain CI/CD pipelines for AI model updates and application releases • Monitor inference latency, application performance, costs, and model drift using MLflow, Weights & Biases, Prometheus, or custom dashboards • Support scalable and reliable cloud infrastructure on AWS, GCP, or Azure Security & Compliance • Ensure AI systems comply with GDPR, HIPAA, SOC 2, or relevant privacy/security standards • Implement authentication, access control, rate limiting, and secure API practices • Protect user data and AI workflows using modern security standards and best practices Collaboration & Product Development • Collaborate with product managers, designers, and data scientists to prioritize impactful AI features • Translate prototypes into production-grade systems with scalable architecture and maintainable code • Participate in sprint planning, architecture discussions, code reviews, and technical documentation • Maintain clear documentation to support reproducibility, onboarding, and long-term maintainability What Makes You a Perfect Fit • Strong software engineer with deep curiosity around AI/ML systems and emerging technologies • Comfortable moving quickly from prototype to production-grade deployment • Analytical and solutions-oriented with strong debugging and optimization skills • Able to balance performance, scalability, usability, and operational cost • Collaborative communicator who works effectively across technical and non-technical teams Required Experience & Skills • 3+ years of professional software engineering experience with AI/ML exposure • Strong proficiency in Python and JavaScript/TypeScript • Experience with AI/ML frameworks such as PyTorch, TensorFlow, LangChain, or Hugging Face • Experience deploying AI or ML models into production systems • Strong front-end experience with React, Next.js, or Vue • Strong SQL skills and experience with cloud data warehouses • Familiarity with REST APIs, microservices, and distributed systems • Experience with Docker, CI/CD workflows, and cloud infrastructure Preferred Experience & Skills • Experience building and scaling AI-powered SaaS applications • Strong understanding of embeddings, vector databases, and RAG architectures • Experience with LLM fine-tuning, evaluation, and prompt optimization • Familiarity with MLOps tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or Weights & Biases • Experience with serverless architectures and cost-optimized inference systems • Background in SaaS, automation platforms, analytics systems, or AI-driven products What Does a Typical Day Look Like? A Full-Stack AI Engineer’s day revolves around transforming AI capabilities into scalable, production-ready applications. You will: • Review and optimize AI model APIs for latency, accuracy, and reliability • Build front-end interfaces that expose AI-driven functionality to end users • Maintain and improve data pipelines supporting AI systems and analytics • Deploy updates through CI/CD workflows and monitor production performance • Collaborate with product and data science teams on AI feature prioritization • Debug infrastructure, inference, or workflow issues impacting system performance • Document architectures, workflows, and deployment processes for maintainability and scaling In essence: you ensure AI systems move beyond prototypes into secure, scalable, reliable, and impactful production applications. Key Metrics for Success (KPIs) • Successful deployment of AI features aligned with sprint timelines • Application uptime ≥ 99.9% • Inference latency maintained below target thresholds • Reduction in manual workflows through AI automation • Stable model performance and minimized drift or degradation • Positive adoption and engagement with AI-powered features • Scalable, maintainable, and cost-efficient AI infrastructure Interview Process • Initial Phone Screen • Video Interview with Pavago Recruiter • Technical Assessment (e.g., deploy an ML model with API + front-end integration) • Client Interview(s) with Engineering / Product Teams • Offer & Background Verification #AIEngineer #FullStackDeveloper #MachineLearning #LLM #ArtificialIntelligence #ReactJS #Python #OpenAI #LangChain #RAG #MLOps #RemoteJobs #SoftwareEngineering #AIJobs #NextJS #CloudEngineering

Responsibilities

Design, build, and deploy scalable AI-powered applications by bridging software engineering with applied machine learning. This includes developing full-stack interfaces, integrating LLMs, and managing MLOps pipelines for production-ready systems.