Angular Lead

About Us Artemis ABA Inc. is a leading provider of behavioral health solutions built on the Salesforce enterprise cloud. Our mission is to simplify ABA therapy operations through secure, scalable, and intuitive mobile technology. Position Overview We are seeking a highly skilled AI / ML Software Engineer with hands-on experience in designing and deploying end-to-end LLM-powered systems, RAG pipelines, and agentic AI architectures. The ideal candidate combines strong Python development skills with advanced understanding of Large Language Models, LangChain, vector databases, prompt optimization, and multi-agent orchestration frameworks. Key Responsibilities Design, develop, and deploy RAG (Retrieval-Augmented Generation) systems integrating vector databases, embedding models, and custom retrievers. Architect and fine-tune custom LLMs (via parameter-efficient tuning, LoRA, or domain-specific training). Create and manage agentic AI workflows—multi-step reasoning systems powered by tools such as LangChain, CrewAI, or OpenDevin. Implement prompt engineering and prompt-chaining techniques to optimize LLM behavior and reliability. Build and maintain scalable LangChain-based applications that interact with databases, APIs, and business systems. Collaborate with data scientists to design and deploy ML / AI pipelines leveraging AWS, Azure, or GCP cloud infrastructure. Develop, test, and optimize APIs that integrate AI reasoning and autonomous decision-making capabilities. Monitor and improve model performance, latency, and token efficiency using observability tools like LangFuse, Phoenix, or OpenDevin dashboards. Document architectural decisions, experiment metrics, and system flows for technical transparency. Required Skills and Experience 4 years in Python software development, with strong understanding of asynchronous programming and API design. Solid experience deploying LLM applications using frameworks like LangChain, LlamaIndex, or Haystack. Proficiency working with vector databases (FAISS, Pinecone, Weaviate, Milvus, Qdrant) and embedding models (OpenAI, sentence-transformers, Cohere, HuggingFace). Experience designing autonomous agent systems, connecting LLMs with Python functions, tools, or APIs. Strong command of prompt engineering, including contextual grounding, role prompting, and few-shot examples. Familiarity with RAG pipeline architecture (retriever, ranker, generator layering). Understanding of custom model fine-tuning techniques (RLHF, LoRA, QLoRA, PEFT). Experience with cloud-based model deployment (AWS Sagemaker, Azure ML, Vertex AI). Working knowledge of AI observability, evaluation frameworks (LangFuse, Traceloop, Phoenix). Preferred Qualifications Experience building autonomous agents using frameworks like LangGraph, CrewAI, OpenDevin, or AutoGPT. Experience deploying multi-agent collaboration systems or tool-using AI. Background in knowledge graph integration, context window optimization, and embedding search tuning. Contribution to open-source AI / LLM projects. Strong understanding of data governance, AI ethics, and model interpretability. Technical Environment Languages : Python Frameworks : LangChain, LlamaIndex, Django, FastAPI Databases : PostgreSQL, MySQL, and vector DBs (Pinecone, FAISS, Qdrant, Weaviate) Cloud : AWS (S3, Lambda, RDS, SageMaker), Azure, or GCP AI Stack : OpenAI, Anthropic Claude, Meta Llama, Mistral, HuggingFace Hub Architecture : RAG, multi-agent orchestration, microservices, serverless