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Role: Founding AI Engineer - Agents, LLM Systems & Applied Research Type: Full-time Core Platform Team Location: Delhi NCR (immediate joiners preferred) Experience: 5-7 years total 2-3+ years in production AI/ML systems Reports to: CTO ABOUT SUTRA.AI Sutra.AI is an AI transformation platform purpose-built for mid-market businesses, teams that want enterprise-grade AI outcomes without building a large AI organization. The platform orchestrates end-to-end: data opportunities projects production apps/APIs, with value tracking built in. We've been building for 3+ years, launched out of stealth recently, and already have dozens of customers with millions of dollars in measurable ROI. We're in scale-up mode - shipping fast, standardizing platform engineering patterns that compound across every customer and product line. ABOUT THE ROLE We're hiring an Founding AI Engineer to own the execution of our AI systems end-to-end. This is the execution backbone of our AI platform. You'll architect and ship production systems, set engineering standards, and work directly with founders on high-priority initiatives. You'll turn ambiguous problems into scalable solutions, independently, with minimal oversight. This is not a research role. Not a coordination role. This is a builder + owner role. The systems you build will be used by the entire team. The patterns you establish will be replicated across the organization. High-leverage by design. WHAT YOU'LL DO Design and implement production AI systems: RAG pipelines, agent frameworks, LLM orchestration, evaluation harnesses Own the full lifecycle: problem scoping prototyping production deployment monitoring and iterationBuild scalable backend systems in Python - APIs, async workflows, data pipelinesEstablish engineering standards and best practices across the AI platformLead execution across junior engineers: code reviews, unblocking, technical guidanceSet up evaluation frameworks to measure accuracy, latency, cost, and reliabilityDebug complex issues across layers: data quality, model behavior, infrastructure, system designCollaborate cross-functionally with product, founders, and customers on roadmap and technical decisions REQUIREMENTS Must-Haves B.Tech (CS) Degree from IIT5-7 years total software engineering experience2-3+ years building production AI/ML systems (not just research or experiments)Strong backend engineering skills: Python, APIs, async systems, distributed architecturesProven track record of end-to-end ownership - shipped systems independently from idea to productionLLM systems: RAG, prompting, agents, evaluation, embeddings, retrievalLLM orchestration: LangChain, LangGraph, LlamaIndex, or equivalentCloud AI platforms: AWS Bedrock, Vertex AI, Azure OpenAI, or equivalentVector databases and retrieval systems: Pinecone, Weaviate, Qdrant, or similarData pipelines: ETL, batch/stream processingSystem design and scalability thinking - build for production, not local prototypesStrong analytical and debugging skills: root cause analysis, not quick fixesTeam-oriented mindset: mentor others, prioritize company impact, low ego Preferred Experience from high-ownership environments: startups or fast-moving product companiesLLM fine-tuning or training experienceFamiliarity with data platforms: Trino, Spark, Airflow, dbtOpen-source contributions or published technical writing WHY JOIN US High ownership: own systems end-to-end without constant oversightHigh leverage: everything you build gets reused; every pattern you set gets replicatedCore team: direct impact on company direction and technical decisionsHard problems: build production AI systems at scale, not demosEarly stage: shape the engineering culture, tech stack, and product direction .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.