- As an AI ML Technology Architect you will lead the end to end design of agentic AI systems for the platform
- You will own the architecture of multi agent workflows the orchestration backbone agent communication patterns evaluation frameworks safety guardrails and reusable design patterns that the engineering team will build against
- This is a hands on architect role not a purely advisory or documentation led role
- The expectation is that you can design review prototype debug and guide implementation of production grade agentic AI systems
- You will work closely with founders product leaders engineers and customer facing teams to translate enterprise problems into scalable AI agent capabilities
- Architect production grade multi agent AI systems using LangGraph AutoGen CrewAI or equivalent orchestration frameworks
- Design stateful agent workflows
- Define agent capabilities for data discovery profiling scoring enrichment intelligence extraction and contextual reasoning across enterprise data estate
- Build and guide the design of structured data agents that can introspect live databases infer schema meaning and generate ER level understanding
- Design document intelligence pipelines for large scale extraction from unstructured data like PDFs Word documents emails call transcripts and semi structured enterprise content using tools such as Azure Document Intelligence AWS Textract LlamaParse or equivalent technologies
- Architect vector database and retrieval pipelines including chunking strategies embedding model selection metadata design hybrid search retrieval tuning and domain specific RAG patterns
- Define agent evaluation methodology covering accuracy precision recall recall k regression testing drift detection hallucination checks and robustness testing for non deterministic AI outputs
- Establish AI safety and trust patterns including semantic guardrails jailbreak protection prompt injection data exfiltration prevention toxic output mitigation policy based response control and secure tool use design
- Architect agent communication and message queuing patterns using RabbitMQ Apache Kafka or equivalent messaging platforms for scalable and resilient agent to agent task communication
- Hands on experience designing and shipping LLM powered or agentic AI systems in production not limited to notebooks PoCs or isolated demos
- Demonstrated experience with multi agent orchestration in production using frameworks such as LangGraph AutoGen CrewAI LangChain or equivalent technologies
- Proven experience building SQL or structured data agents that can connect to live databases inspect schemas infer semantic meaning and generate relationship level understanding
- Strong working knowledge of RAG vector databases embedding models chunking strategies hybrid retrieval metadata filtering prompt engineering and LLM evaluation
- Deep knowledge of Pinecone Milvus or Qdrant specifically around hybrid search sparse dense reranking models Cohere BGE and dynamic chunking strategies
- Experience deploying open source models Llama Gemma via vLLM or Ollama to optimize throughput and cost
- Good to Have
- Experience with knowledge graphs ontologies semantic data models or enterprise metadata models
- Open source contributions in the AI ML data engineering or agentic AI ecosystem
- Experience with MLOps LLMOps model monitoring observability and production AI governance
- Exposure to custom model training fine tuning or domain adaptation though the platform will primarily build on API based and open source LLMs
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