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Build and own a full-stack AI platform for autonomous building intelligence, integrating LLMs and streaming APIs with enterprise-grade security and multi-tenancy on AWS.
Hi, Please find the job description below Role: Python/AI Full Stack Developer Location: Remote Job Description • The Senior Full-Stack Engineer is a hands-on, deeply technical position responsible for designing,…
Build and scale AI agents and ML pipelines using Python, PyTorch/TensorFlow, and frameworks like LangChain; integrate LLMs, vector DBs, and cloud-native systems.
Designs secure CI/CD pipelines and hardens GCP infrastructure while operationalizing Vertex AI workloads in multi-tenant cloud environments.
Designs secure CI/CD pipelines and manages GKE environments using Terraform, Vertex AI, and GCP security tools for multi-tenant, cross-cloud setups.
Build and maintain a cloud-agnostic data and ML platform for scalable, reproducible model training and deployment across multiple products, ensuring reliability, cost-efficiency, and EU compliance.
Build and maintain .NET backend services and AI/ML features for a real-estate platform, including gRPC APIs, vector search, and LLM-powered personalization integrated with SQL Server, MongoDB, and Elasticsearch.
Lead a multi-tier AI platform (React frontend, Spring Boot backend, FastAPI agentic services) while mentoring engineers and owning architecture, reliability, and vendor-neutral patterns.
Lead a multi-tier AI platform (React frontend, Spring Boot backend, FastAPI agentic services) for an adtech agency, owning architecture, mentoring engineers, and driving LLM integrations while ensuring reliability and scalability.
Lead front-end architecture and development for AI-driven digital experiences using React, Next.js and TypeScript, while guiding teams and integrating AI-powered workflows and APIs.
Design and build cloud-native data pipelines on GCP, enabling AI/ML initiatives and analytics for a global banking team using PySpark, Airflow, BigQuery, and Vertex AI.
Design and optimize cloud-native systems on GCP, building real-time data pipelines and secure backends with Node.js, Next.js, Firebase, Vertex AI, and Terraform.
Design and build LLM-powered multi-agent systems for digital banking using Google ADK and LangGraph, integrating RAG pipelines, tool calling, and robust orchestration for production deployment.
Lead Google Cloud engagements, design cloud-native solutions, and oversee delivery for global clients using GCP services like AI, Data & Analytics, and Workspace.
Build and maintain data pipelines on Google Cloud using BigQuery, Dataflow, Pub/Sub, and Airflow to process batch and streaming data for analytics and reporting.
Build and maintain scalable cloud data pipelines and backend services to automate marketing analytics and support digital consultants with Python, GCP, and big-data tools.
Build and maintain a universal data platform that integrates enterprise data sources into automated pipelines, ensuring high-quality data for AI agents, LLMs, and RAG workflows using Python, SQL, and GCP services.
Build and scale a cloud-native microservices platform for AI-powered document data infrastructure, integrating LLMs and processing pipelines in Python on GCP.
Senior AI Engineer builds and deploys consumer-facing AI systems on GCP: ML pipelines, LLMs, RAG, and recommendation engines using Python, BigQuery, Vertex AI, and vector search.
Build and deploy AI solutions on GCP for a global consumer-goods and retail company, using Python, BigQuery, Vertex AI, and LLMs to power customer analytics, personalization, and marketing systems.
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