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
