AI Engineer

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 < 3 years of relevant experience. 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 and # 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 .