AI/ML Engineer

Role Overview

AI/ML Engineer

Key Responsibilities

• Fine-tune reasoning-based LLMs using Chain-of-Thought datasets and techniques.
• Utilize multiple LLMs (ChatGPT, Claude, Gemini, etc.) and understand their strengths and limitations.
• Optimize LLM interactions through advanced prompt engineering, few-shot CoT, and iterative refinement.
• Leverage Retrieval-Augmented Generation (RAG) to combine external context with LLM reasoning.
• Use platforms like Hugging Face/GitHub to manage, fine-tune, and deploy pretrained models.
• Implement agentic AI workflows (LangGraph, CrewAI, or custom orchestration systems).
• Develop scalable backend architectures using FastAPI, Uvicorn, Celery, and async pipelines.
• Build intelligent automation pipelines involving scraping, entity extraction, and enrichment.
• Apply best practices in LLM engineering, including:
• Model training workflows (train/val/test splits, dataset curation)
• Fine-tuning and instruction-tuning with open-source models
• Reasoning task evaluation (factuality, coherence, step accuracy)
• Stay on the cutting edge of AI advancements, from open-weight LLMs to novel prompting strategies.

Required Skills & Expertise

Excellent communication skills — ability to articulate AI concepts clearly.

Hands-on experience in:

• Prompt engineering for LLM-based applications
• Agentic AI frameworks (LangGraph, CrewAI, or custom-built)
• Chain-of-Thought reasoning, few-shot learning, and task decomposition
• Retrieval-Augmented Generation (RAG) pipelines
• Web scraping tools (Playwright, Octoparse, BeautifulSoup)
• System architecture (FastAPI, Uvicorn, Celery, async queues)
• AI/ML platforms (Hugging Face, Transformers, GitHub-based models)
• ML Frameworks: PyTorch (preferred), TensorFlow, or Keras
• Core fundamentals: Data structures, statistics, linear algebra, and optimization

Bonus:

• Familiarity with reasoning datasets (GSM8K, HotpotQA, OpenBookQA)
• Experience with evaluation metrics for generative models (BLEU, ROUGE, EM, custom validators)
• Comfortable working across APIs, scraping flows, and backend systems

Required Background

• 3 to 5 years of work experience in AI/ML, backend engineering, or NLP-heavy roles
• A portfolio of real-world projects — shipped products, agents, scrapers, or research tools

Why Join Us?

• Be part of a stealth-stage, AI-first company solving real-world problems
• Build reasoning-aware systems powered by LLMs and automation
• Work at the intersection of trend intelligence, agents, and research
• Ship fast, own what you build, and have a voice in product direction
• Competitive salary

If you’re passionate about building smart systems that think, we’d love to connect.