Senior AgenticRAG Engineer

Senior AgenticRAG Engineer – Python & Multi-Cloud

About the Company

BridgeAI is a technology company that helps real estate agencies manage and automate their customer interactions through an AI-powered CRM and communication tools. Our platform integrates WhatsApp, email, and website chatbots to qualify leads, schedule property viewings, and simplify agency workflows.

About the Role

We are seeking a Senior AgenticRAG Engineer to design, build, and operationalize the next generation of Retrieval-Augmented Generation (RAG) platforms for autonomous AI agents. This is a hands-on engineering role with a strong focus on Python-based backend development, full-stack integration, and cloud-native deployment.

You will work at the intersection of Generative AI, AgenticOps, and LLMOps, enabling scalable, observable, and safe execution of RAG workflows and intelligent agent pipelines. While GCP will be your primary platform, your designs should accommodate multi-cloud deployments (AWS, Azure, etc.).

Responsibilities

Backend & RAG Engineering (Python)

• Build modular backend services for RAG pipelines, agent memory/state management, vector database interactions, and prompt orchestration.
• Develop scalable APIs for agent task execution, human-in-the-loop interventions, logging, and telemetry.
• Integrate LLM APIs (OpenAI, Claude, Gemini) and open-source models (Llama, Mistral) using frameworks like LangChain, LangGraph, or LlamaIndex.
• Implement model versioning, deployment, rollback, and simulation/testing of agentic behavior.

Full-Stack & Observability

• Design web UIs (React, Next.js, Vue, or similar) for real-time monitoring, behavior auditing, decision logs, and alerts.
• Enable role-based access, interactive prompt testing, and prompt chaining visualization.
• Build structured logging, telemetry, token usage dashboards, and feedback pipelines using Prometheus, Grafana, OpenTelemetry, or ELK stack.

DevOps & Cloud Infrastructure

• Automate deployment pipelines for RAG and agent workloads using Docker, Kubernetes, Terraform, and CI/CD tools (GitHub Actions, Bitbucket, Jenkins).
• Ensure secure API design, sandboxing, and operational guardrails for safe agent execution.
• Optimize cloud resource usage and design for scalability and cost-efficiency on GCP, with multi-cloud readiness.

Collaboration & Leadership

• Work cross-functionally with AI/ML scientists, DevOps/AgentOps engineers, and product managers to define SLOs and operational best practices.
• Mentor engineers on Pythonic architecture, RAG workflows, and cloud-native full-stack design.

Requirements

Core

• 5+ years of Python development experience, with strong backend or full-stack expertise.
• Solid experience with modern web frameworks (FastAPI, Django, Flask) and frontend frameworks (React, Vue, Svelte, or similar).
• Hands-on with LLM integration frameworks like LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI.
• Experience building and maintaining RAG pipelines, vector DB workflows, and retrieval-based architectures.
• Proficient with GCP services (Vertex AI, Compute Engine, Cloud Functions, Kubernetes Engine, Cloud Storage, Pub/Sub).
• Strong DevOps/infra skills: Docker, Kubernetes, Terraform, CI/CD tools, monitoring/observability.
• Understanding of secure API design, authentication/authorization, and agent sandboxing best practices.

Preferred / Nice to Have

• Experience with multi-cloud architectures (AWS, Azure) or serverless frameworks (Lambda, Cloud Functions).
• Knowledge of Responsible AI practices, agent guardrails, prompt auditing, and evaluation workflows.
• Contributions to open-source frameworks or AI infrastructure tooling.
• Experience building agent workflow orchestration UIs or full-stack monitoring dashboards.

What You’ll Get

• Opportunity to shape the RAG and AgenticOps infrastructure stack powering autonomous AI agents.
• High-impact role at the intersection of LLMOps, AgentOps, and cloud-native engineering.
• Access to modern development tools, GPU/LLM infrastructure, and flexible cloud environments.
• Competitive compensation, flexible work policies, and learning/development support.

Job Type: Full-time

Pay: ₹1,300,000.00 - ₹3,000,000.00 per year

Benefits:

• Work from home