On-Site
Full-Time
Mumbai, Maharashtra
India
About the Role
GOQii is a global health-tech company on a mission to improve health, well-being, and longevity. We combine the power of human coaches, real-time data, and AI to drive lasting behavior change. Our ecosystem spans across preventive health, diagnostics, insurance, and wellness—impacting lives at scale.
Backed by top investors and partners, we're now building the next wave of AI-first products that shape the future of digital health and human performance
About the Role
We are on the hunt for highly skilled and curious AI Engineers to join our next-gen AI product team. You will be working at the bleeding edge of applied AI—developing LLM-powered systems, agentic architectures, and AI-first applications using the best from OpenAI, Google (Gemini), Meta (LLaMA 3), Anthropic (Claude), Mistral, and more.
If you’re excited by RAG, prompt engineering, multi-agent orchestration, fine-tuning, and everything in between — we want to talk to you.
What You’ll Do
Build and fine-tune LLMs (OpenAI GPT-4o, Claude 3, Gemini 1.5, LLaMA 3, Mistral, etc.) for specialized use cases
Architect RAG (Retrieval-Augmented Generation) pipelines with custom vector databases
Design and deploy Agentic Systems using LangChain, CrewAI, AutoGen, and more
Implement multi-agent orchestration with planning, memory, and tool use
Integrate AI with structured systems, APIs (e.g., OpenAI functions, tool calling, ReAct pattern)
Build full-stack AI flows including backend (FastAPI / Node.js), vector DBs, and client-side agents
Deploy models using GCP Vertex AI, AWS Bedrock/Sagemaker, Azure OpenAI, etc.
Collaborate with product and design teams to turn vision into deployable AI-first products
Skills We’re Looking For
Core AI/ML Skills:
Large Language Models: GPT-4o, Claude 3, Gemini, LLaMA 3, Mistral
RAG Architectures: Embeddings, Vector Stores, Hybrid Search
Prompt Engineering: Few-shot, Chain-of-Thought, Self-Refine, Function Calling
Fine-Tuning & LoRA: PEFT, QLoRA, model adapters
Open Source Models: Hugging Face, GGUF, Ollama, vLLM
Agentic Frameworks:
LangChain, CrewAI, AutoGen, Autogen Studio
Semantic Kernel, DSPy
Toolformer-style tool integration (agents that call tools/APIs)
Knowledge Graphs & Reasoning:
LLM + KG integration
Contextual memory + multi-hop reasoning
Knowledge-based planning (Plan & Solve / ReAct)
Infrastructure & Deployment:
Google Vertex AI, AWS Bedrock, Azure AI, RunPod, Modal
FastAPI, Docker, Python AsyncIO
Vector DBs: FAISS, Pinecone, Weaviate, Qdrant
Nice to Have:
Multi-modal experience (e.g., Vision + LLM, Speech-to-Text, Whisper, Gemini multimodal)
Speech AI (TTS, STT, Whisper, OpenAI Voice Engine)
WebRTC or voice agent integration
Real-time or streaming data use cases
AI agents in frontends (Next.js + WebLLM, AgentJS, LangGraph)
You Should Have
2–5 years of experience building real-world AI applications (not just research)
Hands-on experience with LLMs, RAG, fine-tuning, and/or agentic workflows
Strong Python and API integration skills
Excellent understanding of AI model limitations and strengths
Fast learner who can work with minimal supervision in a fast-moving environment
Bonus Points
GitHub/open-source contributions in AI/LLMs
Built your own AI agent or app
Contributions to LangChain, Hugging Face, CrewAI, etc.
Startup experience or founder mindset
Why Join Us?
Work on AI-first products that matter — in health, productivity, and human longevity
Collaborate with an elite product + AI + engineering team
Fast-track your growth with real autonomy and full-stack impact
Work with the latest models and tools as part of your job
How to Apply
Send your profile (CV + GitHub or portfolio) to [email protected] or apply via LinkedIn.
Backed by top investors and partners, we're now building the next wave of AI-first products that shape the future of digital health and human performance
About the Role
We are on the hunt for highly skilled and curious AI Engineers to join our next-gen AI product team. You will be working at the bleeding edge of applied AI—developing LLM-powered systems, agentic architectures, and AI-first applications using the best from OpenAI, Google (Gemini), Meta (LLaMA 3), Anthropic (Claude), Mistral, and more.
If you’re excited by RAG, prompt engineering, multi-agent orchestration, fine-tuning, and everything in between — we want to talk to you.
What You’ll Do
Build and fine-tune LLMs (OpenAI GPT-4o, Claude 3, Gemini 1.5, LLaMA 3, Mistral, etc.) for specialized use cases
Architect RAG (Retrieval-Augmented Generation) pipelines with custom vector databases
Design and deploy Agentic Systems using LangChain, CrewAI, AutoGen, and more
Implement multi-agent orchestration with planning, memory, and tool use
Integrate AI with structured systems, APIs (e.g., OpenAI functions, tool calling, ReAct pattern)
Build full-stack AI flows including backend (FastAPI / Node.js), vector DBs, and client-side agents
Deploy models using GCP Vertex AI, AWS Bedrock/Sagemaker, Azure OpenAI, etc.
Collaborate with product and design teams to turn vision into deployable AI-first products
Skills We’re Looking For
Core AI/ML Skills:
Large Language Models: GPT-4o, Claude 3, Gemini, LLaMA 3, Mistral
RAG Architectures: Embeddings, Vector Stores, Hybrid Search
Prompt Engineering: Few-shot, Chain-of-Thought, Self-Refine, Function Calling
Fine-Tuning & LoRA: PEFT, QLoRA, model adapters
Open Source Models: Hugging Face, GGUF, Ollama, vLLM
Agentic Frameworks:
LangChain, CrewAI, AutoGen, Autogen Studio
Semantic Kernel, DSPy
Toolformer-style tool integration (agents that call tools/APIs)
Knowledge Graphs & Reasoning:
LLM + KG integration
Contextual memory + multi-hop reasoning
Knowledge-based planning (Plan & Solve / ReAct)
Infrastructure & Deployment:
Google Vertex AI, AWS Bedrock, Azure AI, RunPod, Modal
FastAPI, Docker, Python AsyncIO
Vector DBs: FAISS, Pinecone, Weaviate, Qdrant
Nice to Have:
Multi-modal experience (e.g., Vision + LLM, Speech-to-Text, Whisper, Gemini multimodal)
Speech AI (TTS, STT, Whisper, OpenAI Voice Engine)
WebRTC or voice agent integration
Real-time or streaming data use cases
AI agents in frontends (Next.js + WebLLM, AgentJS, LangGraph)
You Should Have
2–5 years of experience building real-world AI applications (not just research)
Hands-on experience with LLMs, RAG, fine-tuning, and/or agentic workflows
Strong Python and API integration skills
Excellent understanding of AI model limitations and strengths
Fast learner who can work with minimal supervision in a fast-moving environment
Bonus Points
GitHub/open-source contributions in AI/LLMs
Built your own AI agent or app
Contributions to LangChain, Hugging Face, CrewAI, etc.
Startup experience or founder mindset
Why Join Us?
Work on AI-first products that matter — in health, productivity, and human longevity
Collaborate with an elite product + AI + engineering team
Fast-track your growth with real autonomy and full-stack impact
Work with the latest models and tools as part of your job
How to Apply
Send your profile (CV + GitHub or portfolio) to [email protected] or apply via LinkedIn.