Hi! We are an AI company and we are looking for passionate and skilled AI Engineers.

We are Hiring! Apply now by sending us your resume, GitHub profile link / portfolio via email (careers@operonn.com). This platform has limited seats to apply, so make sure to send us an email for applying. (Our website: https://operonn.com)

We're early-stage, founder-led, and deliberate about what we build.

We work with other businesses, helping them to improve their ROI using AI. We don't build just "chatbots". Our mission is to deliver AI to industries and businesses that would actually help them streamline their operations and enhance their revenue.

You'll work alongside the founder on production AI systems shipped to early customers. Scoped deliverables, direct mentorship, real ownership of the slice you take on.

Responsibilities:

• LLM application development. Build and maintain components of our agent and orchestration layer — tool routing, structured outputs, schema-validated generation, prompt versioning.
• Retrieval engineering. Implement and tune RAG pipelines over operational data — chunking, embedding, reranking, provenance tracking. Measure retrieval quality with honest metrics.
• Evaluation infrastructure. Build golden sets, regression harnesses, and LLM-as-judge frameworks. Catch regressions before they reach customers.
• Observability & telemetry. Instrument latency, cost, and quality metrics per LLM call. Trace failures end-to-end.
• Integration support. Contribute to ERP-side connectors and ingestion pipelines — FastAPI services, async workers, event handling.
• Documentation. Maintain technical documentation for every component you ship — readable by engineers who join after you.
• Code review participation. Review peer PRs, defend your own, take feedback that makes the system better.

Skills Required

Must-Have

• Strong Python with hands-on experience in async patterns and production-grade code
• FastAPI or an equivalent Python web framework
• At least one shipped end-to-end LLM project — RAG, agent, fine-tune, or applied ML system that survived real users
• Working knowledge of vector databases (Qdrant, pgvector, or Weaviate) and embedding models
• Familiarity with at least one orchestration framework: LangChain, LangGraph, or LlamaIndex
• Git fluency, Docker awareness, Linux comfort
• Demonstrable ability to read technical papers and apply only what matters

Good-to-Have

• Open-source contributions of any scale
• TypeScript or Next.js for cross-stack work
• Cloud exposure — AWS (Lambda, ECS, RDS), GCP (Cloud Run, BigQuery), or Firebase
• Distributed systems or event-driven architecture background
• Fine-tuning, PEFT, or domain-adaptation experience
• Exposure to enterprise software environments — ERP, CRM, or ticketing systems

Eligibility

• Final-year BTech, recent graduate, or Masters student in Computer Science, AI, or a related field
• Self-taught engineers with verifiable portfolios are equally welcome
• Available 20–25 hours per week for a minimum 3-month commitment

Engagement Structure

• Mode: Async-first, fully remote
• Cadence: Weekly 1:1 with the founder, weekly scoped goals, code review on every PR
• Mentorship: Direct founder mentorship — no proxy managers, no layered review chains
• Output measurement: Shipped deliverables and their measurable impact, not hours logged

Compensation

Compensation is project / milestone-based, structured as follows:

• Each engagement window (typically 4–6 weeks) is scoped into clearly defined deliverables agreed in writing before work begins
• Each milestone carries a fixed payout, released on completion and founder sign-off
• Performance bonuses are available for exceptional delivery — early completion, scope exceeded, or measurable production impact
• A live-product reference are guaranteed on successful completion of the engagement
• Compensation is benchmarked against the Indian AI internship market and adjusted at intake based on skill depth and prior production experience

How to Apply

Send to careers@operonn.com:

1. GitHub link or portfolio
2. One paragraph: the hardest engineering problem you've solved in an LLM or ML project, and how you measured success
3. Optional: a short note on what you'd want to learn or build during this engagement

We read every application. We reply within 7 days. The selection process:

1. Application review — within 7 days of submission
2. Working session with the founder — 30 minutes, technical conversation, no leetcode
3. Take-home exercise — 2–3 hours on a scoped Operonn problem, paid on submission
4. Offer & onboarding — milestone scoping and engagement letter