Quantum World Technologies Inc.
Position: AI Architect
Location:
Marysville, Ohio (Onsite 3 days a week)
Job Description Design multilayered AI solutions balancing compute efficiency, contextual fidelity, and algorithmic adaptability for retrieval, reasoning, planning, and tool use.
Develop advanced RAG pipelines leveraging vector databases (e.g. ChromaDB, Milvus, FAISS) and embedding strategies for contextual accuracy.
Integrate AI capabilities with enterprise systems via REST and GraphQL APIs, ensuring secure and scalable interoperability.
Establish best practices for algorithm selection and layering, combining neural models, symbolic reasoning, and tool‑based agents for optimal performance.
Collaborate with cross‑functional teams to embed AI agents into business workflows, aligning with compliance and governance standards.
Implement structured output validation and schema enforcement using Pydantic, FastAPI, and JSON Schema for robust data integrity.
Optimize compute resources and latency trade‑offs across cloud, hybrid, and edge environments for high‑performance AI workloads.
Define observability baselines, telemetry, tracing, evaluation metrics, and rollout strategies for safe iterative deployments.
Required Skills
LangChain ecosystem: LangChain, LangGraph, prompt templates, agent orchestration patterns
AIML frameworks: OpenAI API, HuggingFace Transformers, TensorFlow, PyTorch – experience with finetuning and inference optimization
Data context management: SQLAlchemy, PostgreSQL, JSON Schema mapping, feature engineering, contextual pipelines
Vector databases & semantic search: ChromaDB, Milvus, FAISS, embedding optimization, similarity search strategies
Algorithmic design: strong understanding of algorithm layering, retrieval, reasoning, planning, hybrid AI approaches, compute‑aware model selection
API integration & security: REST, GraphQL, OAuth, enterprise‑grade security practices
DevOps & CI/CD: Git, Docker, Azure DevOps (or equivalent), FastAPI, Uvicorn, containerized deployments, automated pipelines
Agentic capabilities: reasoning adaptation, tool calling, Multi‑Agent coordination (ReAct agents, Supervisor)
Performance optimization: distributed compute strategies, GPU/T‑GPU utilization, quantization, pruning, distillation, caching, batching
Mandatory Skills
Architectural diagrams
#J-18808-Ljbffr
Marysville, Ohio (Onsite 3 days a week)
Job Description Design multilayered AI solutions balancing compute efficiency, contextual fidelity, and algorithmic adaptability for retrieval, reasoning, planning, and tool use.
Develop advanced RAG pipelines leveraging vector databases (e.g. ChromaDB, Milvus, FAISS) and embedding strategies for contextual accuracy.
Integrate AI capabilities with enterprise systems via REST and GraphQL APIs, ensuring secure and scalable interoperability.
Establish best practices for algorithm selection and layering, combining neural models, symbolic reasoning, and tool‑based agents for optimal performance.
Collaborate with cross‑functional teams to embed AI agents into business workflows, aligning with compliance and governance standards.
Implement structured output validation and schema enforcement using Pydantic, FastAPI, and JSON Schema for robust data integrity.
Optimize compute resources and latency trade‑offs across cloud, hybrid, and edge environments for high‑performance AI workloads.
Define observability baselines, telemetry, tracing, evaluation metrics, and rollout strategies for safe iterative deployments.
Required Skills
LangChain ecosystem: LangChain, LangGraph, prompt templates, agent orchestration patterns
AIML frameworks: OpenAI API, HuggingFace Transformers, TensorFlow, PyTorch – experience with finetuning and inference optimization
Data context management: SQLAlchemy, PostgreSQL, JSON Schema mapping, feature engineering, contextual pipelines
Vector databases & semantic search: ChromaDB, Milvus, FAISS, embedding optimization, similarity search strategies
Algorithmic design: strong understanding of algorithm layering, retrieval, reasoning, planning, hybrid AI approaches, compute‑aware model selection
API integration & security: REST, GraphQL, OAuth, enterprise‑grade security practices
DevOps & CI/CD: Git, Docker, Azure DevOps (or equivalent), FastAPI, Uvicorn, containerized deployments, automated pipelines
Agentic capabilities: reasoning adaptation, tool calling, Multi‑Agent coordination (ReAct agents, Supervisor)
Performance optimization: distributed compute strategies, GPU/T‑GPU utilization, quantization, pruning, distillation, caching, batching
Mandatory Skills
Architectural diagrams
#J-18808-Ljbffr