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Python Backend Developer + Gen AI
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- Master's degree
- AWS
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API Connectivity expert and App developer
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Python Backend Developer + Gen AI

Simpliigence -
Remote

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Job details

Contractual / Temporary
₹3,38,724.32 - ₹40,00,000.00 a year
11 hours ago

Qualifications

Spring Boot
Cloud infrastructure
Law
MCP
Kubernetes
Java
Master's degree
Microservices
RBAC
AWS
Docker
PostgreSQL
Neo4j
SDKs
RabbitMQ
Back-end development
REST
APIs
S3
AI
Python
Spring framework

Full job description

job Responsibilities:

10+ Years

As a Expert Python Developer, you will own the technical implementation of the platform's orchestration layer. You will build the lightweight Python SDK that serves as the primary interface between domain-specific agents (like Lease Abstraction and CAM Reconciliation) and core platform services. Your work will ensure that agents operate within secure, ephemeral sandboxes (Firecracker MicroVMs) while enforcing strict multi-tenant isolation and identity management via WorkOS.

Key Responsibilities

Platform Control Plane & AI Gateway: Architect and maintain the central platform Control Plane and API Gateway using FastAPI, handling critical functions such as LLM routing, telemetry, and per-tenant cost metering.
Agentic SDK Engineering: Lead the development of the Tango Python SDK, abstracting complex LLM routing and providing a standardized interface for "Human-in-the-Loop" (HITL) escalations and agent overrides.
Durable Workflow Orchestration: Implement and manage long-running, multi-stage agentic workflows using Temporal to ensure reliable execution of complex real estate processes.
Multi-Tenant Backend Services: Build scalable, secure RESTful APIs for the Admin Console, managing complex CRUD operations and ensuring strict logical data isolation across tenants.
Infrastructure & Security Integration: Collaborate with DevSecOps to deploy backend services via Kubernetes and integrate WorkOS for B2B identity, SSO, and fine-grained Role-Based Access Control (RBAC).
Data Architecture & MCP: Interface with specialized data stores including PostgreSQL, GraphDB (Neo4j), and Vector Databases (ChromaDB) using the Model Context Protocol (MCP) to ensure agents consume data securely without direct database access.
LLM Gateway & Cost Controls: Develop and operate the LLM gateway layer (LiteLLM): provider routing and failover (AWS Bedrock, Anthropic), rate limiting, token budget enforcement, runaway-loop kill-switches, and per-run/per-tenant cost metering.
Production Operations: Participate in SLO definition, incident response, and DR runbook authoring; design for RTO/RPO targets from day one rather than retrofitting.
Data & Ingestion Pipelines: Build document ingestion pipelines (S3, queues, OCR handoff), schema evolution, and the data contracts through which agents consume tenant data securely , including integration with the client's existing data warehouse and Java/Spring Boot estate.
Identity & Access Integration: Integrate WorkOS for agent identity, on-behalf-of (OBO) user flows, SSO/SCIM for enterprise tenants, and audit-grade attribution of every agent action and tool call.

Required Skills & Experience

Backend Mastery: Expert-level proficiency in Python and FastAPI; significant experience building scalable, production-grade RESTful APIs and microservices.
AI Ecosystem & Agentic Intelligence: Deep experience with LangChain and LangSmith; proven track record building multi-step reasoning chains and tool-use protocols for autonomous agents.
Cloud & Infrastructure: Strong experience with AWS (Bedrock, ECS, S3) and containerization (Docker/Kubernetes). Understanding of Firecracker MicroVMs for agent sandboxing is a major plus.
Orchestration & Messaging: Hands-on experience with Temporal for workflow orchestration and RabbitMQ or AWS SQS for event-driven architectures.
Security & Identity: Proven experience implementing OAuth 2.0 and integrating enterprise identity providers like WorkOS.
Database Proficiency: Practical experience with relational (PostgreSQL), Graph (Neo4j/GraphDB), and Vector (ChromaDB/PGVector) databases.
API Design: Demonstrated ability to design and version public-facing APIs and client libraries that other engineering teams depend on.

Desirable

Prodigy Framework: Familiarity with the Prodigy agentic framework is a significant advantage for this role.
Domain Expertise: Understanding of Commercial Real Estate (CRE) workflows.
Evaluation Literacy: Experience building programmatic "Evaluation Harnesses" to measure accuracy and hallucination rates in AI models.

Pay: ₹338,724.32 - ₹4,000,000.00 per year

Work Location: Remote

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