Point your AI agent at freehire and let it find you a job. A CLI and an MCP server over the whole job API — no browser.
Build and optimize LLM-powered backend systems for scalable AI-driven search and agentic workflows, focusing on retrieval pipelines, orchestration, and evaluation.
Build and scale face-recognition systems using PyTorch/TensorFlow, own end-to-end ML pipelines on AWS, and lead fairness analysis for biometric models in production.
Build LLM-powered agents to automate code generation, testing, reviews, and deployment, integrating AI into Lattice’s software toolchain.
Build and productionize LLM-powered AI agents and orchestration systems using NLP, retrieval-augmented generation, and agent workflows to enhance customer-experience analytics.
Designs and builds AI agents and integrates AI capabilities into scalable Java backends using modern development practices.
Lead the design and maintenance of Databricks-based ETL pipelines and data models for a fintech platform, transforming raw financial data into insights for investors and AI workloads.
Principal Data Engineer designs and builds scalable cloud data pipelines using Snowflake, Databricks, and AWS, while integrating AI agents for automated data quality and transformation workflows.
Build and optimize scalable data pipelines, ML workflows, and AI systems on Databricks using Spark, Delta Lake, MLflow, and Mosaic AI.
Designs and maintains data pipelines and vector databases to feed AI systems with clean, real-time data for RAG and agent memory.
Build and maintain backend services in Ruby on Rails and Python for client projects, focusing on scalable APIs, databases, and AI integrations.
Build and deploy Python-based AI solutions, integrating LLMs and RAG architectures with vector databases for enterprise clients in a hybrid Warsaw role.
Build and deploy Python-based AI solutions, integrating LLMs and RAG architectures with vector databases for production use in a global consulting firm undergoing AI transformation.
Senior Python developer building AI/GenAI solutions, integrating LLMs and RAG pipelines, and moving POCs to production in a financial IT environment.
Lead Python Developer builds and maintains Python apps on Azure, using RAG architecture and event-based patterns to create scalable AI solutions in a Krakow office.
Build and automate cloud infrastructure for clients using IaC (Terraform/Pulumi), Kubernetes, and CI/CD pipelines; design secure, scalable systems on AWS/GCP with Go, Python, or Node.js.
Build and deploy Generative AI systems for financial applications, including RAG pipelines, vector databases, and AI agents, using Python, AWS Bedrock, and LangGraph.
Build and maintain secure, scalable cloud infrastructure for client projects using AWS/GCP, Kubernetes, Terraform, and CI/CD pipelines.
Build and maintain secure, scalable cloud infrastructure for client products using AWS/GCP, Kubernetes, Terraform, and CI/CD pipelines.
Build and deploy AI/ML pipelines using Python, FastAPI, and cloud services like GCP to serve vector-based models and microservices.
Build and run the production infrastructure for Ingersoll Rand’s GenAI program, automating CI/CD, observability, and reliability for LLM-powered apps on GCP and Snowflake.
We couldn't check your fit for this role — add a CV to your profile to see it next time.