About the Role

We are hiring a senior, hands-on Agentic RAG Engineer to design, build, and operate the Retrieval-Augmented Generation platforms that power our autonomous AI agents.

This is a lead-by-example role:

You design the architecture
You write the Python
You ship to production
You mentor engineers by building real systems

You will lead the technical direction of RAG and agent memory systems, while remaining deeply involved in implementation, observability, and operational readiness.

GCP is our primary platform, but all designs should be multi-cloud capable.

Key Responsibilities

RAG & Backend Engineering (Python-First)

Design and build production-grade RAG pipelines
Implement:
Retrieval strategies
Vector database integrations
Agent memory and state management
Prompt orchestration and chaining
Build scalable Python services using FastAPI / Django / similar
Integrate LLM APIs (OpenAI, Claude, Gemini) and open-source models (Llama, Mistral)
Implement model/version rollout, rollback, and simulation testing

Agentic Systems & Workflow Design

Build and operate multi-step agent workflows
Enable:
Tool calling
Human-in-the-loop interventions
Safe agent execution patterns
Define patterns for:
Prompt versioning
Context management
Token and cost control
Collaborate closely with AgentOps to ensure production-safe execution

Full-Stack & Observability

Design and contribute to internal UIs for:
Agent execution monitoring
Decision and reasoning audits
Prompt testing and visualization
Implement structured logging and telemetry for:
Retrieval quality
Agent decisions
Token usage and latency
Work with Prometheus, Grafana, OpenTelemetry, or ELK-based stacks

Cloud, DevOps & Production Readiness

Own deployment pipelines for RAG and agent services
Work hands-on with:
Docker
Kubernetes
Terraform
CI/CD pipelines
Ensure secure API design, auth, sandboxing, and operational guardrails
Optimise for scalability, performance, and cloud cost efficiency on GCP

Technical Leadership & Team Enablement

Act as technical lead for Agentic RAG engineering
Set architectural standards and best practices
Review code and designs with a high bar
Mentor engineers in:
Pythonic system design
RAG correctness and evaluation
Production-grade GenAI systems
Partner with Product and Platform leads on roadmap and delivery

Required Skills & Experience

Core Engineering

5+ years of strong Python engineering experience
Proven backend or full-stack development background
Experience with FastAPI, Django, Flask, or similar frameworks
Comfort contributing to frontend systems (React / Next.js / Vue) when needed

RAG, LLMs & Agentic Systems

Hands-on experience building RAG pipelines in production
Strong understanding of:
Vector databases and retrieval strategies
Prompt chaining and context handling
Agent workflows and tool invocation
Experience with frameworks such as:
LangChain
LangGraph
LlamaIndex
AutoGen / CrewAI

Cloud & Platform Engineering

Strong experience with GCP, including:
Vertex AI
GKE / Compute Engine
Cloud Functions
Cloud Storage, Pub/Sub
Hands-on DevOps skills:
Docker
Kubernetes
Terraform
CI/CD tooling
Understanding of secure APIs, auth, and sandboxing patterns

Nice to Have

Multi-cloud experience (AWS, Azure)
Experience with Responsible AI, guardrails, and eval frameworks
Contributions to open-source AI or infrastructure projects
Experience building internal tooling or monitoring dashboards for AI systems

What Success Looks Like

RAG systems are accurate, observable, and cost-efficient
Agent failures are explainable and debuggable
Engineers follow clear, scalable RAG patterns you defined
Product teams trust agent outputs in production
You are the technical authority for RAG at BridgeAI

What This Role Is (and Is Not)

Deeply hands-on technical leadership

Python-first engineering

Production-grade RAG ownership

Mentorship through real code

Not a research-only role

Not a people-manager-only role

Not a demo or prototype position

Lead Principal Agentic RAG Engineer – Python & AI Platforms