Lead Principal Agentic RAG Engineer – Python & AI Platforms

Bridge AI; India; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Bridge AI is seeking a senior hands-on Agentic RAG Engineer to design, build, and operate the Retrieval-Augmented Generation platforms powering our autonomous AI agents.

This lead-by-example role requires designing architecture, writing Python, shipping to production, and mentoring engineers while directing RAG and agent memory systems. Work is multi-cloud with a focus on GCP and observability.

Qualifications

• 5+ years of strong Python engineering experience.
• Backend or full-stack development background.
• Experience with FastAPI, Django, Flask, or similar.
• Comfort contributing to frontend systems when needed.

Responsibilities

• Design and build production-grade RAG pipelines.
• Implement retrieval strategies and vector DB integrations.
• Develop agent memory, state management, and prompt orchestration.
• Build scalable Python services with FastAPI/Django.
• Integrate OpenAI, Claude, Gemini and open-source models.
• Maintain deployment pipelines; ensure production readiness.

Skills

Python engineering

Backend development

Full-stack development

React / Next.js / Vue

FastAPI / Django / Flask

GCP

Docker

Terraform

CI/CD tooling

Observability

Tools

LangChain

LangGraph

AutoGen / CrewAI

Job description

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

• 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:
• Human-in-the-loop interventions
• Define patterns for:
• 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:
• Decision and reasoning audits
• Prompt testing and visualization
• Implement structured logging and telemetry for:
• 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
• Terraform
• 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
• 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
• AutoGen / CrewAI
• Strong experience with GCP, including:
• GKE / Compute Engine
• Cloud Functions
• Docker
• 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 BridgeAI