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AI Engineer
AI EngineerRavian AI • Pushkar, IN
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AI Engineer

AI Engineer

Ravian AI • Pushkar, IN
11 hours ago
Job description

About Ravian AI

Ravian AI is building device-native AI systems that can think, decide, and act on behalf of users. Our platform goes beyond web-based agents, enabling true end-to-end automation directly on devices. We’re working with enterprises and consumers to unlock productivity and decision intelligence at scale.

The Role

You will design and build production-grade, multi-agent AI systems and the backends that power them. You’ll work across FastAPI services, WebSocket event streams, orchestration logic (Autogen / LangGraph), and device execution layers to deliver agents that complete real tasks—autonomously, safely, and repeatably.

What you’ll do (Responsibilities)

  • Design & build backends : Develop high-performance APIs with FastAPI, implement WebSockets for streaming I / O, and own service-level reliability (timeouts, retries, backoff, circuit breakers).
  • Agentic orchestration : Implement multi-agent workflows using Autogen, LangGraph, or similar frameworks; handle tool calling, memory, state, guardrails, and safe-actions.
  • Productionize AI : Turn models + prompts into idempotent workflows with input validation, schema contracts, evaluation harnesses, and offline / online metrics.
  • Observability & safety : Ship deep tracing, structured logs, and red-teaming hooks. Build guardrails to prevent bad actions and ensure reversible operations.
  • Performance engineering : Reduce latency, control costs, and design fallbacks (models, tools, or policies) for robustness under failure.
  • Collaboration : Partner with product, design, and customers to scope problems, define SLAs, and iterate quickly from prototype → pilot → production.
  • Quality bar : Write crisp tests, automate CI / CD, and maintain documentation others can rely on.

Must-Have Qualifications (Requirements)

  • 3–5 years of software / AI engineering in production environments.
  • Strong backend skills with FastAPI (or similar), WebSockets, async Python, and at least one cloud(AWS / Azure / GCP).
  • Hands-on with multi-agent systems using Autogen, LangGraph, or equivalent—not just hello-world notebooks.
  • Proven record of having built and shipped AI projects / products used by real users (internal or external).
  • Solid understanding of LLM tooling (prompting, tool-use, RAG, evals, safety / guardrails).
  • Comfortable with data models, queues, caches, relational / NoSQL storage, and containerization.
  • Nice-to-Have (Bonus)

  • Experience with device control (browser automation, OS-level actions, mobile / desktop app automation).
  • Knowledge of retrieval systems, vector stores, and evaluation frameworks.
  • Experience with observability stacks (OpenTelemetry, Prometheus / Grafana, ELK).
  • Prior publications, OSS contributions, or GitHub repos demonstrating deep work.
  • We are NOT considering

  • Freshers.
  • Candidates with
  • Applicants who haven’t shipped AI systems (toy demos / usual projects don’t count).
  • Candidates whose background is only classical ML model training without production agentic experience or have deep learning experience.
  • Tech you’ll touch

    Python, FastAPI, WebSockets, Autogen, LangGraph, Celery / Queues, SQL / NoSQL, Docker, AWS (Lambda / ECS / S3), OpenTelemetry, CI / CD.

    How we hire

  • Intro call (30 mins) : What you’ve shipped.
  • Technical deep dive (60–90 mins) : Walkthrough of a system you built, live reasoning on agentic design trade-offs.
  • Practical exercise (take-home or live) : Build or critique a small multi-agent flow time line 2-3 days.
  • Founder discussion : Product sense, speed, and ownership.
  • References & offer.
  • Apply - exactly like this (don’t skip)

    Send an email to Lokesh@ravian.ai and Surya@ravian.ai # mention both

    with the subject :

    Subject : Application — AI Engineer — Your Name

    Attach / Include :

  • Cover letter (1 page max) addressing :
  • A production AI system you shipped, your role, scale, and business impact.
  • Multi agent system you have build
  • Why device-native agents excite you.
  • Resume (PDF) with links to GitHub / Portfolio.
  • GitHub (top 2–3 repos that show relevant code).
  • Papers published (if any) or technical blog posts.
  • Salary expectation (fixed).
  • Clarify if you identify as Data Scientist or ML Engineer with production multi-agent systems experience.
  • Note : If your background is limited to training classical ML models without production agentic work, please do not apply.

  • Compensation ₹8–12 LPA (INR) based on experience and fit.
  • Potential performance-based upside.
  • NOTE : Please ensure you must have a clear understanding of Ravian AI, its mission, and the applications it builds before applying or engaging further. Familiarity with the company’s device-native AI systems, which enable end-to-end automation and decision intelligence, is essential for alignment and meaningful contribution .

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