Senior Software Engineer (AI/ML) - Part-time | Remote

This is a remote position.

• Design and build autonomous or semi-autonomous AI agents that can plan, reason, and interact with tools, APIs, or external systems
  
• Implement agentic frameworks (e.g., LangChain, LlamaIndex, CrewAI, or custom orchestration systems)
  
• leverage existing industry capabilities to deliver virtual assistant capabilities on top of xquic content including voice interactions
  
• Optimize reasoning and retrieval pipelines using embeddings, vector databases, and prompt engineering
  
• Develop, train, and fine-tune ML models using frameworks like PyTorch, TensorFlow, or scikit-learn
  
• Work on data preprocessing, feature engineering, and model evaluation for NLP, computer vision, or predictive tasks
  
• Build ML pipelines for training, deployment, and monitoring in production environments
  
• Collaborate with engineering teams to integrate AI components into backend systems and APIs
  
• Ensure scalable, maintainable codebases with CI/CD, observability, and cloud-native design (AWS/GCP/Azure)
  
• Contribute to technical architecture and design reviews for AI-driven features and platforms
  
• Stay current with the latest in LLMs, agent frameworks, and model architectures
  
• Prototype and evaluate new approaches for reasoning, tool use, and adaptive behavior in agents
  
• Share learnings and mentor peers in ML and AI development best practices.
  

Requirements

• Bachelors Degree in Computer Science or related fields
  
• 5 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