Lead AI Solutions Engineer

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Kindred

Experteer Overview As Lead AI Solutions Engineer you will design and scale a Retrieval-Augmented Generation (RAG) platform that underpins self-service analytics apps. You will integrate diverse data sources, build modular RAG architectures, and collaborate with cross-functional teams to deliver AI-driven data insights. This role shapes how the organization accesses and utilizes data through intelligent assistants and automation. You’ll operate at the intersection of data engineering, ML, and platform engineering to drive practical, scalable AI solutions. Compensation / Benefits • Design, enhance and maintain scalable RAG architectures for data retrieval and generation • Develop APIs and microservices to integrate RAG with data sources (HR systems, legal repositories, SharePoint, etc.) • Manage and optimize LibreChat UI and Open WebUI pipelines for backend model development • Work with large language models (GPT, Claude family, LLaMA) and embeddings (Sentence Transformers) including fine-tuning • Build data pipelines to support LLM querying, semantic search, and metadata retrieval; maintain Airflow pipelines • Ensure data governance, security, and compliance across applications • Manage vector databases (PGVector, FAISS) including indexing and similarity search optimizations • Collaborate with Platform Eng, deploy on Kubernetes (Kindred Cloud); integrate SSO (Azure) and SailPoint • Create unit/integration tests; validate LLM accuracy; deploy in AWS with monitoring • Produce technical docs and deliver training for self-service capabilities Tasks • 5+ years in AI/ML engineering, data engineering, or backend development • Proficiency in Python (LangChain, LangGraph, agent-based LLM workflows) and FastAPI emphasizing scalability • Experience integrating with LLM providers (OpenAI, Anthropic) for inference/evaluation • Experience with LLM-based architectures, RAG, and NLP techniques • Experience using MLflow or similar for tracking models and prompts • Proficiency in SQL, No-SQL, Redshift and data warehousing concepts • Experience integrating structured and unstructured data sources for AI apps • Knowledge of dbt, metadata management, semantic search • Cloud deployment in AWS or Kindred Cloud; Kubernetes, Terraform, Helm, ArgoCD Key requirements •

Kindred

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