AI Engineering Lead

Role: AI Engineering Lead

Type: Full-time · Core Platform Team

Location: Delhi NCR (immediate joiners preferred)

Compensation: ₹40-60 LPA + meaningful early-stage equity

Experience: 4–7 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 AI Engineering Lead 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 iteration

• Build scalable backend systems in Python - APIs, async workflows, data pipelines

• Establish engineering standards and best practices across the AI platform

• Lead execution across junior engineers: code reviews, unblocking, technical guidance

• Set up evaluation frameworks to measure accuracy, latency, cost, and reliability

• Debug complex issues across layers: data quality, model behavior, infrastructure, system design

• Collaborate cross-functionally with product, founders, and customers on roadmap and technical decisions

REQUIREMENTS

Must-Haves

• 4-7 years total software engineering experience

• 2-3+ years building production AI/ML systems (not just research or experiments)

• Strong backend engineering skills: Python, APIs, async systems, distributed architectures

• Proven track record of end-to-end ownership - shipped systems independently from idea to production

• LLM systems: RAG, prompting, agents, evaluation, embeddings, retrieval

• LLM orchestration: LangChain, LangGraph, LlamaIndex, or equivalent

• Cloud AI platforms: AWS Bedrock, Vertex AI, Azure OpenAI, or equivalent

• Vector databases and retrieval systems: Pinecone, Weaviate, Qdrant, or similar

• Data pipelines: ETL, batch/stream processing

• System design and scalability thinking - build for production, not local prototypes

• Strong analytical and debugging skills: root cause analysis, not quick fixes

• Team-oriented mindset: mentor others, prioritize company impact, low ego

Preferred

• Experience from high-ownership environments: startups or fast-moving product companies

• LLM fine-tuning or training experience

• Familiarity with data platforms: Trino, Spark, Airflow, dbt

• Open-source contributions or published technical writing

WHY JOIN US

• High ownership: own systems end-to-end without constant oversight

• High leverage: everything you build gets reused; every pattern you set gets replicated

• Core team: direct impact on company direction and technical decisions

• Hard problems: build production AI systems at scale, not demos

• Early stage: shape the engineering culture, tech stack, and product direction