You're an AI Engineer with 2+ years of experience who understands that context engineering is
becoming the most important skill an AI engineer can develop. You're passionate about building
intelligent systems that solve real-world problems and excited about mastering the art of
providing the right context to unlock LLM potential in complex, dynamic applications.
Responsibilities
- Build and integrate LLM-powered features including document generation, intelligent
assessments, and conversational agents
Design voice AI pipelines for real-time speech processing and multi-language supportMaster context engineering - dynamically managing memory, retrieval, and informationflow across complex agent trajectories
Create agentic workflows that can understand context and generate domain-specificoutputs
Integrate with LLM APIs (OpenAI, Anthropic, etc.) and optimize model performance forproduction use
Collaborate with engineering teams to seamlessly integrate AI capabilities intoapplications
Qualifications
Required
2+ years of hands-on experience building AI systems in production environmentsStrong expertise in Python and LLMs, prompt engineering, and API integrations(OpenAI, Anthropic, etc.)
Proficiency in core AI / ML libraries (transformers, LangChain, PyTorch / TensorFlow)Experience building RAG systems and working with vector databasesUnderstanding of agentic AI systems and multi-agent architecturesKnowledge of speech technologies (STT / TTS) and NLP frameworksFamiliarity with cloud platforms (AWS / Azure) and MLOps practicesNice to Have
Experience with voice AI applications and real-time audio processingHealthcare domain knowledge or experience with compliance requirementsBackground in fine-tuning LLMs or multilingual NLP modelsPython FastAPI experience for building AI-powered APIsExperience with multi-agent frameworks like LangGraph, CrewAI, or AutoGen