Job description
We're building the next generation of enterprise AI infrastructure — and we need you to help make it intelligent, fast, and reliable. As an AI Retrieval & Agent Platform Engineer , you'll be a core contributor to our AI Factory , designing the retrieval and agent connectivity layer that powers our AI-driven decision-making at scale. If you're passionate about RAG pipelines, vector databases, and agent tooling — this is your role. How You’ll Make an Impact (responsibilities of role) Vector DB & Hybrid Retrieval Stand up and tune vector databases (Pinecone/Weaviate/Qdrant/AWS-native) for similarity search at scale. Design hybrid retrieval combining vector semantic search with graph context and business logic filters; implement re-ranking. Manage embedding lifecycle (choice, diversity, refresh cadence, cold-start strategies). RAG & Contextualization Build RAG pipelines pulling structured/unstructured context; implement chunking, metadata, and guardrails. Integrate graph-derived context windows for multi-hop reasoning in agent workflows. Agent Connectivity (MCP) & Tooling Implement MCP-based tool discovery/invocation for agent ↔ system interaction. Wrap enterprise systems (Snowflake/MongoDB/SharePoint/ERP) as reusable tools/skills with clear schemas/capabilities. Represent tool capabilities & dependencies as graph processes for orchestration; collaborate with graph team. Observability & Feedback Loops Instrument agent KPIs (latency, accuracy, relevance, cost/execution); implement tracing across retrieval/graph layers. Build dashboards and automated feedback loops (e.g., low relevance → retraining/embedding refresh; failures → rule updates). Optimize cloud architecture for performance, cost, security; maintain SLOs. Cloud & Performance Deploy and scale retrieval services, vector stores, and agent endpoints on AWS (IAM, VPC, S3, Lambda, EKS/ECS, DynamoDB). Conduct performance profiling, caching strategies, and cost optimization (e.g., batch upserts, ANN index selection, sharding). What You Bring (required qualification and skill sets) Bachelor’s/Master’s in CS , Data Science, Engineering, or related field. 3–10 years in IR/Retrieval systems, vector DBs, or agent platform engineering . Hands-on with Pinecone/Weaviate/Qdrant (at least one in production), embeddings, ANN indexes, and hybrid ranking. Experience building RAG pipelines and contextualization strategies with LLMs. Strong Python 3.10 + backend engineering skills (FastAPI, FastMCP) Own CI/CD pipelines via GitLab CI and containerized deployments Exposure to Neo4j / AWS Neptune Expertise in bedrock/AWS AgentCore Familiarity with graph queries (Cypher/Gremlin/SPARQL) to leverage semantic context. AWS infrastructure knowledge and provisioning IAC as Preferred Qualifications Experience with MCP or equivalent agent–tool interoperability patterns ; skill registries and capability discovery. Observability stack: OpenTelemetry, Prometheus/Grafana, distributed tracing; KPI-driven optimization. Knowledge of LangChain/LlamaIndex, vector re-ranking, prompt caching, and safety/guardrail mechanisms. Exposure to Neo4j/Neptune/TigerGraph; event streaming (Kafka/Kinesis) for ingestion/update triggers. Hands-on with LangGraph, LlamaIndex, re-ranking, prompt caching, and guardrail mechanisms
This job post has been translated by AI and may contain minor differences or errors.
