FULL TIME ROLE
Founding AI Engineer
LLM Application Developer and AI Agent Builder
Location Bangalore, comfortable working in EST timezone | Employment Type Full Time |
Primary Focus AI product engineering, LLM workflows, agents, and research automation | Seniority Senior, high ownership |
About OpenGrowth Ventures
OpenGrowth Ventures (OGV) is a venture-building platform designing and launching multiple AI-native companies simultaneously, rather than betting on a single product. One of our active ventures is AlphaStreet, an AI-native intelligence platform for understanding complex markets — connecting fragmented data, surfacing patterns, and enabling faster strategic decisions.
Joining OGV means working within a venture-building ecosystem: exposure to how new ideas are evaluated, how early systems get built, and how customer insight becomes shipped product. The team is small and senior leadership is closely involved, so the technical decisions you make on architecture and product direction genuinely shape the foundation — not one feature inside a large org, but the actual groundwork.
Role Overview
We are looking for a Founding AI Engineer who can build practical AI-powered product features from prototype to production. This person should be strong across frontend, backend, data workflows, and LLM integrations, while also being comfortable using modern AI development tools to move faster.
The right candidate is not just a traditional developer. They should be comfortable building with AI-assisted development tools (e.g., Claude, Cursor, GitHub Copilot) for research, coding, debugging, prototyping, and technical exploration, while still reviewing outputs carefully and shipping clean production-quality code.
What This Person Will Work On
- Full stack product development: Build user-facing features, dashboards, workflows, internal tools, and responsive web experiences.
- Backend engineering: Build Python services, APIs, authentication flows, database models, background jobs, and integrations.
- Frontend engineering: Develop clean interfaces using React, Next.js, TypeScript, Tailwind, and reusable components.
- LLM product features: Integrate AI models into real workflows using prompts, structured outputs, tool calling, retrieval, and evaluation.
- Agent workflows: Build systems where AI can research, summarize, compare, extract, validate, and complete multi-step tasks.
- Research automation: Create pipelines that collect, clean, structure, and convert information into useful product outputs.
- Production readiness: Support deployment, monitoring, logging, error handling, performance, and basic security practices.
Key Responsibilities
- Own features end to end, from product requirements and design handoff to implementation and release.
- Build reliable APIs and data models for AI workflows, saved outputs, users, projects, and system activity.
- Connect frontend experiences to backend services, AI agents, data sources, and third-party APIs.
- Implement clear product states such as loading, progress, errors, empty states, streaming responses, and review flows.
- Use AI coding tools thoughtfully to speed up development without compromising quality, security, or maintainability.
- Communicate technical tradeoffs clearly with product, design, and business stakeholders.
Core Skills — Must Have
| Backend | Python, FastAPI or Django REST, REST APIs, async jobs, authentication, API security basics |
| Frontend | React, Next.js, TypeScript, Tailwind, component architecture, responsive UI, product polish |
| AI and LLMs | OpenAI or Anthropic APIs, prompting, structured outputs, tool calling, RAG, embeddings, evaluations |
| Agents | Multi-step workflows, tool use, source-based outputs, validation, human review, agent run logs |
Core Skills — Nice to Have
| Data | PostgreSQL, Redis, pgvector or vector search, data modeling, indexing, metadata, caching |
| DevOps | Docker, GitHub Actions, AWS or Vercel, environment setup, logging, monitoring, deployments |
| AI Tools | Comfort with Claude, Cursor, ChatGPT, GitHub Copilot for building, debugging, prototyping, refactoring, and research |
Minimum Qualifications
- 5+ years of experience building production web applications across frontend and backend.
- Strong hands-on experience with React, Next.js, TypeScript, and modern frontend development.
- Strong Python backend experience with FastAPI, Django REST, or similar frameworks.
- Experience building APIs, database schemas, background jobs, and third-party integrations.
- Hands-on experience integrating LLM APIs into real product workflows.
- Comfortable using AI-assisted development tools as part of the development workflow.
- Ability to work independently in a fast-moving, early-stage product environment.
Nice to Have
- Experience building AI agents, research automation tools, copilots, or workflow automation products.
- Experience with LangChain, CrewAI, LlamaIndex, OpenAI Agents SDK, or similar frameworks.
- Experience with embeddings, vector databases, RAG pipelines, semantic search, or document processing.
- Experience with streaming responses, WebSockets, async job progress, or real-time dashboards.
- Experience with data ingestion from APIs, feeds, documents, websites, or structured research sources.
- Experience in startups, 0 to 1 product environments, or rapid R&D teams.
Ideal Working Style
- Builds quickly, but keeps the codebase clean and understandable.
- Uses AI tools actively, but does not blindly trust generated code.
- Asks good questions when requirements are unclear and proposes practical technical options.
- Can move from rough concept to working prototype, then improve it into a stable product feature.
How to Apply
Send your resume along with links to relevant work — GitHub, deployed projects, or anything showcasing AI-integrated product features you've built — to chandana.budhiraja@opengrowth.com/monisha.asnani@opengrowth.com. A short note (2-3 sentences) on the most interesting AI or agent workflow you've shipped is more useful to us than a cover letter.
What to expect: a recruiter screen, a practical technical exercise, a deeper technical/system-design conversation, and a final conversation with the founding team. We aim to move through the full process within 2-3 weeks.