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 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
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