Lead principal agentic rag engineer – python & ai platforms

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 Fast API / Django / similar
Integrate LLM APIs (Open AI, 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 Agent Ops 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, Open Telemetry, or ELK-based stacks
Cloud, Dev Ops & 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 Gen AI 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 Fast API, 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:
Lang Chain
Lang Graph
Llama Index
Auto Gen / Crew AI
Cloud & Platform Engineering
Strong experience with
GCP , including:
Vertex AI
GKE / Compute Engine
Cloud Functions
Cloud Storage, Pub/Sub
Hands-on Dev Ops 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 Bridge AI
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