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.
Design and deliver AI-ready data platforms, ML pipelines, and GenAI-specific data flows for clients using cloud ecosystems like AWS, Azure, and GCP.
Design and build AI-ready data platforms and ML pipelines for analytics, GenAI, and RAG systems on AWS/Azure/GCP, ensuring production-grade delivery and MLOps practices.
Build full-stack web apps and integrate AI features using modern stacks (React, Node.js/Python/.NET, SQL/NoSQL) while leveraging AI coding assistants daily.
Build full-stack web apps and integrate AI features using modern stacks, AI coding assistants, and LLM APIs to deliver enterprise solutions.
Build and deploy enterprise-grade LLM applications using RAG, AI Agents, and vector databases like Milvus/Qdrant. Develop Python-based AI workflows and APIs for real-world production use.
Lead training, alignment, and optimization of large language models using RLHF, SFT, and quantization; build reward models, red-team models, and optimize inference pipelines in Python/C++/Rust.
Leads quality engineering for an enterprise AI platform, building test frameworks for Agentic AI workflows and LLM-based systems using Python, Java, and modern CI/CD stacks.
Designs and builds scalable cloud data pipelines, warehouses, and analytics platforms using Python, SQL, Spark, and cloud tools like BigQuery and Snowflake, with optional Gen AI integration.
Build and deploy AI models (LLMs, ML) for healthcare insights, recruiter automation, and personalized experiences using Python, PyTorch, LangChain, and vector databases.
Build and deploy AI models (LLMs, ML) and data pipelines for healthcare insights, recruiter automation, and personalized digital experiences using Python, PyTorch/TensorFlow, and LangChain.
Builds and scales Python/FastAPI backend services that integrate LLMs, RAG pipelines, and vector databases for an AI-driven healthcare market-research platform.
Builds enterprise GenAI apps with Python, LangGraph, and RAG pipelines, integrating LLMs and vector search for AI agents and chatbots.
Fine-tune and deploy private AI models (LLMs, vision) using Python, Docker, and cloud platforms; maintain scalable, secure AI solutions with prompt engineering and responsible-AI practices.
Protect Absa’s AI systems by designing secure AI architectures, detecting AI-specific threats, and running offensive security tests like prompt injection and agent manipulation.
Build and deploy ML models in NLP, computer vision, and generative AI, integrating them into production apps via cloud APIs and frameworks like PyTorch and LangChain.
Builds and deploys production AI systems like voice hardware, conversational agents, and RAG pipelines using Python, FastAPI, and ML frameworks.
Build, deploy, and monitor ML/AI models and GenAI systems using Python, TensorFlow, PyTorch, and MLOps tooling like MLflow and Kubernetes.
Build and scale AI agents and ML pipelines using Python, PyTorch/TensorFlow, and frameworks like LangChain; integrate LLMs, vector DBs, and cloud-native systems.
Build and deploy multi-agent AI systems using LLM APIs (Bedrock, OpenAI, Mistral), LangGraph/LangChain, and AWS serverless tools; focus on execution, not design.
Build and deploy LLM/NLP models using Hugging Face, LangChain, and cloud AI services; implement RAG pipelines and vector search for AI-driven chatbots and Q&A systems.
We couldn't check your fit for this role — add a CV to your profile to see it next time.