Artificial Intelligence Engineer

GoDo Financial Services

About the Role:

We are hiring AI Engineers to build internal AI products and to add AI into existing company software. Work includes voice AI (meeting notetaker, transcription, summaries), LLM-based tools, and agents that take actions inside our systems.

You will ship end-to-end: prototype, APIs, a usable UI, and production-ready features. Strongest skill should be applied AI. Backend and frontend need to be solid enough to build and ship, not specialist-level.

Role: AI Engineer. Experience: 1–2 years. Location: India. Employment type: Full-time.

What you will do:

• Notetaker: Build internal AI tools such as a meeting notetaker: capture audio, transcribe, summarize, extract action items, and make them searchable.
• Voice AI: Work with speech and voice models: STT, TTS, speaker diarization, batch and near-real-time pipelines.
• LLMs and agents: Design and ship LLM features: RAG, tool-calling agents, structured extraction, and multi-step workflows.
• Product integration: Integrate AI into existing APIs, web apps, and internal services (assistants, automations, copilots).
• Full stack: Own simple full-stack slices: backend endpoints, data stores, background jobs, and a clean frontend for internal tools and product UI.
• Model choice: Compare models and vendors (hosted APIs vs open-source), and choose based on quality, latency, and cost.
• Quality and safety: Add logging, basic evals, fallbacks, and practical safety (PII, wrong answers, access control).
• Delivery: Turn a vague product idea into a scoped MVP, then harden it for real use.
• Cloud: Build and run these systems on AWS.

What you need to know:

Must have:

• Experience: 1–2 years of professional software or AI engineering. Strong internships or real shipped projects count.
• LLMs: Hands-on LLM work: prompting, function/tool calling, RAG, embeddings. LangChain / LlamaIndex or equivalent custom pipelines.
• Voice: Practical speech/voice experience, or a clear ability to get there quickly (Whisper or similar STT, audio pipelines).
• Backend: Python and/or Node.js, REST APIs, async jobs, Postgres or similar.
• Frontend: React or similar, enough to build internal tools and product screens.
• Tooling: Git, Linux, Docker, and third-party AI APIs (OpenAI, AWS, or self-hosted models).
• AWS: Working knowledge of AWS (for example S3, Lambda, EC2, ECS, SQS, or equivalent services you have actually used).
• Communication: Clear written English. Able to scope and ship without a fully written spec.

Nice-to-haves:

• Real-time voice (WebRTC, websocket audio, live captions).
• Agents that call tools and write back to systems, not chat-only demos.
• Vector databases (pgvector, Pinecone, Qdrant, or similar).
• FastAPI / Express / Nest, websockets, background workers.
• Prompt versioning, eval harnesses, cost/latency tracking.
• Deeper AWS experience (Bedrock, Transcribe, SageMaker, IAM, CloudWatch).

Education:

Bachelor’s degree in Computer Science or a related field.