AI Retrieval & Agent Platform Engineer

**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**