Overview

Congratulations, you have taken the first step towards bagging a career-defining role.

Qualifications

• Build and execute evaluation pipelines using ground truth datasets.
• Measure model performance using Precision, Recall, F1 Score, and other evaluation metrics.
• Benchmark model and prompt performance across multiple iterations.
• Continuously improve classifier accuracy through structured experimentation.
• Design, develop, and optimize prompts for enterprise-scale classification use cases.
• Perform regression testing to validate prompt quality and consistency.
• Treat prompts as reusable, version-controlled engineering assets.
• Experiment with structured outputs, few-shot prompting, and instruction tuning techniques.
• Curate and maintain high-quality labeled datasets for training and evaluation.
• Work with annotation tools to improve data quality using analyst feedback.
• Develop and enhance components within the AI-powered classification pipeline.
• Write clean, scalable, and production-ready Python code.
• Support packaging, validation, and deployment of ML models across cloud, self-hosted, and air-gapped environments.
• Contribute to inference infrastructure and deployment automation.
• Collaborate with senior engineers to improve AI platform reliability and scalability.
• Work closely with AI researchers, Product Engineering, and Security teams.
• Participate in technical discussions, architecture reviews, and experimentation initiatives.
• Contribute innovative ideas that enhance the AI capabilities of the ARMOR Platform.
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 2–3 years of experience in Machine Learning, NLP, AI Engineering, or Software Development.
• Strong understanding of NLP concepts including tokenization, embeddings, Named Entity Recognition (NER), and text classification.
• Hands-on knowledge of AI evaluation metrics including Precision, Recall, F1 Score, and Confusion Matrix.
• Exposure to evaluation frameworks such as RAGAS, DeepEval, MLflow, or similar tools.
• Hands-on experience working with Large Language Models using OpenAI, Anthropic, or similar APIs.
• Working knowledge of LangChain, LangGraph, or equivalent LLM orchestration frameworks.
• Understanding of Retrieval-Augmented Generation (RAG), embeddings, and retrieval concepts.
• Curiosity to investigate prompt failures such as hallucinations, output drift, and instruction-following limitations.
• Strong Python programming skills with experience writing clean, maintainable, and testable code.
• Familiarity with Git, CI/CD concepts, and collaborative software development practices.
• Ability to develop evaluation scripts, automate testing, and generate structured model evaluation reports.

Nice to have

• Knowledge of Vector Databases and similarity search.
• Exposure to model fine-tuning or transfer learning techniques.