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 deploy GenAI solutions (RAG, agents, assistants) for enterprise clients, turning AI challenges into measurable results in weeks while balancing speed, cost, and governance.
Build and deploy AI-powered document processing systems using Python, NLP, OCR, and LLMOps in a production Kubernetes environment.
Build and deploy generative AI agents, RAG systems, and ML models end-to-end using Python, LangChain, and Azure OpenAI to drive healthcare analytics and business impact.
Build and deploy agentic AI systems and LLM-powered applications for a global insurer, focusing on RAG, multi-agent orchestration, and robust MLOps pipelines.
Design and build autonomous AI agents and multi-agent systems using RAG architectures with vector databases, LangChain, and LangGraph to automate business processes and integrate with CRM, media, and analytics platforms.
Builds AI agents and RAG systems to automate marketing workflows, integrate APIs via MCP, and deliver generative insights for ad-tech and media clients.
Senior Data Engineer builds and maintains scalable data pipelines, cloud infrastructure, and ML models on Google Cloud using Python, SQL, dbt, and Databricks.
Build autonomous AI agents and RAG systems to automate marketing workflows, integrating LLMs, vector databases, and cloud platforms like GCP/AWS.
Designs and maintains AI-augmented data extraction pipelines using Pydantic, Scrapy, and LLM agents to autonomously scrape and validate structured data from websites while ensuring compliance and reusability.
Build and maintain cloud data pipelines and ML solutions on GCP, AWS, and Azure using Python, SQL, dbt, and API-first architectures.
Build and deploy AI-powered full-stack apps using Angular, Docker, and LLMs; optimize models and engineer prompts to solve real client problems.
DevOps Engineer to design, deploy, and maintain AI infrastructure using Kubernetes, Docker, and LLMOps tools like Ollama and LangChain, ensuring scalable, secure systems for logistics and industrial clients.
DevOps Engineer building and scaling AI infrastructure (LLMOps) with Kubernetes, Docker, and NVIDIA CUDA on Linux, automating deployments and integrating Python/Java services.
Build and deploy production-grade LLM-based applications using Python/Java/TypeScript, integrating with OpenAI, Claude, and open-source models while optimizing cost, latency, and security.
Build and optimize AI agents using LangChain/LangGraph for backend systems, focusing on scalable architectures and agentic AI frameworks.
Build and maintain a production platform that collects, processes, and enriches social media content using Python, ETL pipelines, and LLM APIs for classification and summarization.
Build production-ready GenAI and agentic AI systems for global enterprises using Python, LLM APIs, and cloud platforms like AWS/Azure/GCP.
Build enterprise-grade GenAI applications and autonomous multi-agent systems using Python, LLM APIs, and vector databases, then deploy them to cloud platforms for Fortune 500 clients.
Build and lead cloud-native data pipelines for regulatory reporting in investment banking, migrating legacy systems to AWS and integrating AI-driven workflows using Python, Kafka, and Kubernetes.
Design and implement AI-driven process reinvention solutions for clients using frameworks like crewAI and LangGraph, integrating data pipelines and process automation tools.
We couldn't check your fit for this role — add a CV to your profile to see it next time.