Lead Python Engineer

Lead Python Engineer – Backend & AI Integrations

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About Darwix AI

Darwix AI is one of India’s fastest-growing GenAI SaaS companies powering real-time decision intelligence for enterprise revenue teams. Our platform transforms frontline performance through:

Transform+:

Sherpa.ai:

Store Intel:

We serve market leaders across BFSI, real estate, and retail—including IndiaMart, Wakefit, GIVA, Sobha, and Bank Dofar. Our stack processes thousands of voice conversations daily, powers real-time dashboards, and delivers high-stakes nudges that directly impact multi-crore revenue pipelines.

We are building at the intersection of voice AI, backend scale, and real-time analytics. You will play a key role in shaping the tech foundation that drives this mission.

Role Overview

We’re looking for a Lead Python Engineer to architect, own, and scale the core backend systems that power Darwix AI’s GenAI applications. You’ll work at the confluence of backend engineering, data pipelines, speech processing, and AI model integrations—supporting everything from real-time call ingestion to multi-tenant analytics dashboards.

You will lead a high-performing engineering pod, collaborate with product, AI, and infra teams, and mentor junior engineers. This is a high-impact, ownership-first role with direct influence over product velocity, system performance, and enterprise reliability.

Key Responsibilities

1. Backend Architecture & Infrastructure

• Design and maintain scalable APIs and backend systems using Python (FastAPI)
• Optimize data flow for speech-to-text transcription, diarization outputs, and call scoring workflows
• Build and maintain modular service components (STT, scoring engine, notification triggers)
• Manage asynchronous job queues (Celery, Redis) for large batch processing
• Ensure high availability, security, and scalability of backend systems across geographies

2. AI/ML Integration & Processing Pipelines

• Integrate with LLMs (OpenAI, Cohere, Hugging Face) and inference APIs for custom use cases
• Handle ingestion and parsing of STT outputs (WhisperX, Deepgram, etc.)
• Work closely with the AI team to productionize model outputs into usable product layers
• Manage embedding pipelines, RAG workflows, and retrieval caching across client tenants

3. Database & Data Engineering

• Design and maintain schemas across PostgreSQL, MongoDB, and TimescaleDB
• Optimize read/write operations for large call data, agent metrics, and dashboard queries
• Collaborate on real-time analytics systems used by enterprise sales teams
• Implement access controls and tenant isolation logic for sensitive sales data

4. Platform Reliability, Monitoring & Scaling

• Collaborate with DevOps team on infrastructure orchestration (Docker, Kubernetes, GitHub Actions)
• Set up alerting, logging, and auto-recovery protocols for uptime guarantees
• Drive version control and CI/CD automation for releases with minimal regression
• Support benchmarking, load testing, and latency reduction initiatives

5. Technical Leadership & Team Collaboration

• Mentor junior engineers, review pull requests, and enforce code quality standards
• Collaborate with product managers on scoping and technical feasibility
• Break down large tech initiatives into sprints and delegate effectively
• Take ownership of technical decisions and present trade-offs with clarity

Required Skills & Experience

• 3–8 years of hands-on backend engineering experience, primarily in Python
• Strong grasp of FastAPI, REST APIs, job queues (Celery), and async workflows
• Solid experience with relational and NoSQL databases: PostgreSQL, MongoDB, Redis
• Familiarity with working on production systems involving large-scale API calls or streaming data
• Prior experience integrating 3rd-party APIs (e.g., OpenAI, CRM, VoIP, or transcription vendors)
• Working knowledge of Docker, CI/CD pipelines (GitHub Actions preferred), and basic infra scaling
• Experience working in high-growth SaaS or data-product companies

Bonus Skills (Preferred, Not Mandatory)

• Experience with LLM applications, vector stores (FAISS, Pinecone), and RAG pipelines
• Familiarity with speech-to-text engines (WhisperX, Deepgram) and audio processing
• Prior exposure to multi-tenant SaaS systems with role-based access and usage metering
• Knowledge of OAuth2, webhooks, event-driven architectures
• Experience with frontend collaboration (Angular/React) and mobile APIs
• Contributions to open-source projects, technical blogs, or developer communities