Senior Artificial Intelligence (AI) Developer

Qualifications we are looking for:

• Bachelor’s degree in Computer Science, Data Science, Mathematics, or a related technical field (preferred, not required).
• 4+ years of software development experience, with at least 2 years focused on applied AI/ML engineering.
• Strong proficiency in Python and experience with ML libraries such as Scikit-learn, PyTorch, TensorFlow, or HuggingFace Transformers.
• Hands-on experience working with LLM APIs (OpenAI, Azure OpenAI, Anthropic, or similar) and prompt engineering techniques.
• Experience building RAG pipelines, vector search integrations (Pinecone, pgvector, Weaviate), or AI agent frameworks (LangChain, LlamaIndex, or equivalent).
• Familiarity with cloud AI/ML services on Azure, AWS, or GCP (Azure Machine Learning, SageMaker, or Vertex AI).
• Working knowledge of RESTful API development, containerization (Docker), and CI/CD pipelines.
• Solid understanding of data fundamentals, including SQL, data wrangling, and feature engineering.
• Strong problem-solving, rapid prototyping, and cross-functional communication skills.

Nice to have:

• Experience in HR technology, PEO, or regulated industries.
• Experience building with the n8n orchestration platform.
• Relevant certifications such as AWS Machine Learning, Azure AI Engineer, or Google ML Engineer.

Job duties:

• Design, develop, test, and deploy AI/ML models and applications, including NLP pipelines, predictive models, recommendation engines, and intelligent automation workflows.
• Build and integrate LLM-powered features using APIs such as OpenAI, Azure OpenAI, or Anthropic, implementing RAG patterns and AI agent workflows.
• Develop and maintain data pipelines that support model training, fine-tuning, evaluation, and real-time or batch inference.
• Integrate AI solutions into existing enterprise platforms and APIs, ensuring reliable, secure, and performant deployment.
• Write well-structured, tested, and documented Python code, and participate in peer code reviews.
• Collaborate with business analysts and stakeholders to translate requirements into AI use cases, and participate in Agile sprint ceremonies.
• Monitor deployed models and pipelines in production, diagnose performance issues, and implement improvements.
• Conduct research and rapid prototyping to evaluate new AI frameworks, tools, and techniques.
• Document AI solution designs, data flows, API contracts, and operational runbooks.
• Follow responsible AI guidelines around model fairness, data privacy, and explainability.