Design, build, and scale intelligent systems — from machine learning pipelines to advanced AI agents capable of reasoning, planning, and automation.

Requirements

Bachelor's degree in Computer Science or related fields
3+ years of experience
Strong programming skills in Python (mandatory); proficiency with PyTorch, TensorFlow, or transformers-based models
Experience in building or integrating AI agents (LangChain, LlamaIndex, CrewAI, custom frameworks)
Strong grasp of ML model lifecycle — data processing, model training, evaluation, deployment, and monitoring
Experience with cloud platforms (AWS/GCP/Azure) and containerization (Docker, Kubernetes)
Familiarity with API integrations, microservices, and asynchronous systems
Strong understanding of vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) and retrieval architectures
Solid software engineering fundamentals — testing, version control, and system design
Experience with LLM fine-tuning, prompt optimization, or RAG (Retrieval-Augmented Generation) systems
Familiarity with multi-agent systems and coordination mechanisms
Knowledge of MLOps tools (MLflow, Kubeflow, Sagemaker, Vertex AI)
Experience in AI system observability, evaluation metrics, and continuous learning loops
Exposure to reinforcement learning or self-improving agent architectures

Benefits

Design, build, and scale intelligent systems — from machine learning pipelines to advanced AI agents capable of reasoning, planning, and automation.

Bachelor's degree in Computer Science or related fields
3+ years of experience
Strong programming skills in Python (mandatory); proficiency with PyTorch, TensorFlow, or transformers-based models
Experience in building or integrating AI agents (LangChain, LlamaIndex, CrewAI, custom frameworks)
Strong grasp of ML model lifecycle — data processing, model training, evaluation, deployment, and monitoring
Experience with cloud platforms (AWS/GCP/Azure) and containerization (Docker, Kubernetes)
Familiarity with API integrations, microservices, and asynchronous systems
Strong understanding of vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) and retrieval architectures
Solid software engineering fundamentals — testing, version control, and system design
Experience with LLM fine-tuning, prompt optimization, or RAG (Retrieval-Augmented Generation) systems
Familiarity with multi-agent systems and coordination mechanisms
Knowledge of MLOps tools (MLflow, Kubeflow, Sagemaker, Vertex AI)
Experience in AI system observability, evaluation metrics, and continuous learning loops
Exposure to reinforcement learning or self-improving agent architectures