Destaque

Full-Stack AI Engineer

Pavagovia Glassdoor

RemotoBrPlenoCLT12 dias atrás

Salário Estimado

R$ 7.722,00 - R$ 11.583,00

Tecnologias

JavaScript TypeScript React Vue Node Python Java Go Docker Kubernetes API Machine Learning IA Data Science TensorFlow PyTorch

0de 100

Regular

Score da Vaga

Descrição da Vaga

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

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Informações

NívelPleno

ContratoCLT

LocalBr

RemotoSim

MoedaBRL

Publicada12 dias atrás

FonteGlassdoor

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