AI/ML Engineer

1 day ago

Gurugram Gurugram India CoolBoots Full time

Job Description

Position: AI/ML Engineer - GenAI Stack Hacker

Who You Are

You're a tool-native LLM hacker who ships AI systems faster than most people write specs. You build with GPT-4o, LangChain, Hugging Face, and Together.ai like it's second nature. You don't just use AI APIs you orchestrate them, fine-tune them, and make them work together intelligently.

What You'll Build

- Unified Model Router: LangChain-style orchestration layer that seamlessly switches between
- Together.ai, OpenAI, self-hosted HF models, and custom fine-tuned models
- Intelligent Cost Optimizer: ML-driven system that routes requests based on complexity, cost, speed, and quality requirements
- Storytelling AI Pipeline: Prompt templates, context managers, and narrative consistency engines specifically for interactive storytelling
- Model Evaluation Framework: Systematic testing for story quality, character consistency, and user engagement across different models
- Custom Fine-tuning Pipeline: Data prep, LoRA training, and deployment for UIXBridge-specific storytelling models

Tech Stack You'll Master

- Core: Python, FastAPI, async workflows, WebSockets
- AI Orchestration: LangChain, Semantic Kernel, custom routing logic
- Models: Transformers, Diffusers, PEFT, HuggingFace Hub, Together.ai APIs
- Infrastructure: Replicate, RunPod, modal.com for self-hosted models
- Prompt Engineering: Advanced templating, few-shot learning, chain-of-thought reasoning

You Should Know

- How to build production AI systems that don't break when APIs go down
- Prompt engineering for creative writing and narrative consistency
- Model deployment, quantization, and GPU optimization
- How to make AI feel magical in user-facing applications
- Cost optimization across multiple AI providers

Bonus Points

- Built AI storytelling tools, writing assistants, or creative agents
- Experience with RLHF, constitutional AI, or safety alignment
- Open source contributions to AI tooling or model repos

Day 1 Goals

- Audit current AI integration and identify optimization opportunities
- Build unified model calling framework with intelligent fallbacks
- Implement cost tracking and optimization across all AI providers

Apply with: GitHub showing AI experiments, deployed AI tools, or hackathon wins. Show us something cool you built with LLMs.

Please Note: This will be a part-time contractual role

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