AI Software Engineer

Practice by Numbers (PBN) | Gurugram, India S

cope: AI / Conversational Products & Backend Services

About Practice By Numbers

Practice by Numbers is a dental practice management SaaS platform serving over 1,500 practices across North America — practice management software, VOIP, payment processing, and analytics. We are now expanding into AI-powered automation, building conversational AI products that change how practices interact with their patients.

About The Role

We’re looking for a Software Engineer with strong, hands-on experience in both backend product engineering and modern AI systems. The ideal candidate can design and build reliable, production-grade services while also developing, integrating, and deploying AI-powered capabilities. As this is a single opening on a small team, we need someone who can take end-to-end ownership across both areas and contribute independently throughout the product development lifecycle.

You'll work on our AI Receptionist — a multi-channel conversational AI (voice, SMS, web chat) for dental practices — and the backend services, APIs, and integrations behind it. Hands-on IC role: you own features end to end, including their production behaviour. Real patient-facing traffic under HIPAA constraints, where correctness and latency both matter.

Reports to: Lead Engineer — AI

Location: Gurugram, India — this role is open in Gurugram only

Work Mode: In-office

Working Hours: Primarily IST (10 AM – 5 PM), with some evening overlap with US teams (until 9–11 PM IST) as needed

What You'll Do

Backend

• Build and maintain backend services and RESTful APIs in Python (FastAPI / Django)
• Design schemas and write efficient PostgreSQL queries; use Redis for caching and session state
• Work with async and event-driven patterns — queues, webhooks, WebSockets, background workers
• Own the operational side: logging, metrics, alerting, debugging production issues
• Write unit and integration tests for the business logic you ship

AI & LLM

• Build and iterate on LLM-driven conversation flows: tool calling, multi-turn state, context handling
• Write and refine prompts for specific use cases, and measure the impact of changes
• Build guardrails for patient-facing interactions — no medical advice, no unverified data disclosure
• Work with RAG and knowledge-base retrieval for practice-specific questions
• Contribute to evaluation and regression testing so AI quality doesn't drift between releases
• Balance response quality, latency, and cost across LLM and voice vendors

Integrations & Data

• Integrate with practice management systems (Dentrix, Open Dental, Eaglesoft) and internal PBN APIs
• Implement secure auth flows, including OTP-based patient verification
• Follow HIPAA-compliant practices across data handling, logging, and storage Collaboration
• Work with Product Management to turn requirements into working software, surfacing edge cases early
• Participate in sprint planning, standups, code reviews, and product reviews
• Document what you build; collaborate across time zones with US-based stakeholders

Experience

Required Qualifications

• 2–6 years of professional software development experience
• Hands-on experience building backend services and APIs that ran in production
• Practical experience with LLM-based applications (GPT-4/4o, Claude, or similar) — prompt design, tool calling, handling model output in real systems. Substantial personal or open-source work counts; tutorial-level does not.
• Experience debugging and improving a system after it shipped

Technical Skills

• Strong Python — our primary language across AI and backend
• APIs: RESTful services, webhooks, third-party integrations; FastAPI or Django preferred
• Databases: PostgreSQL — schema design, indexing, query performance; Redis or similar
• Async Python (asyncio) and event-driven architectures
• Cloud: working knowledge of AWS (or GCP/Azure) — compute, storage, managed DBs, queues
• Version control, code review, and CI/CD as normal parts of your workflow

AI Domain Understanding

• Clear view of what LLMs can and cannot do reliably, and how that shapes product design
• Prompt engineering and conversation design for multi-turn interactions
• Familiarity with RAG and agentic patterns — tool use, orchestration
• Some experience evaluating and monitoring LLM systems
• Awareness of token cost and latency trade-offs

Soft Skills

• Comfort with ambiguity — you can make a reasonable call and explain it
• Clear communication with technical and non-technical stakeholders
• Able to drive your own work to completion without close supervision
• Comfortable with a fast pace and evolving requirements
• Willing to work in-office in Gurugram and overlap with US hours when needed

Preferred Experience & Skills

• Conversational AI — chatbots, voice assistants, or IVR
• Voice/telephony (Twilio, Vonage) or STT/TTS APIs (Deepgram, ElevenLabs, AssemblyAI)
• LLM orchestration frameworks (LangChain, LlamaIndex) — or a considered view on skipping them
• Healthcare / HIPAA compliance knowledge
• Observability tools (Datadog, New Relic, Sentry, Papertrail)
• Celery or similar task queues; AWS SQS or equivalent
• SaaS or B2B product company background; multi-tenant architecture
• Open-source contributions