Machine Learning Engineer

Machine Learning Engineer - Hyderabad, India

Circuitry.ai is seeking a skilled Machine Learning Engineer with a strong foundation in traditional ML algorithms and structured data systems for our Hyderabad, India office. This is a 5-day onsite, permanent role requiring hands-on experience with large structured datasets and end-to-end ML pipelines. We are focused on building real, production-grade AI systems, not demos. While GenAI exposure is a plus, it is not a requirement for this position.

About Circuitry.ai

Circuitry.ai is an Enterprise AI SaaS company building advanced AI systems for the U.S. SLM sector. We specialize in production-grade RAG architectures, fine-tuned LLM systems, knowledge graph-driven AI, agentic AI frameworks, and trust-centric AI systems with explainability and evidence. Our mission is to solve the Trust in AI problem for enterprises by combining LLMs, graph retrieval, structured data systems, and rigorous evaluation pipelines. We build real, production-grade AI systems.

Role Overview

This role demands real, hands-on ML experience with large structured datasets and end-to-end ML pipelines, focusing on structured data processing, feature engineering, model training & evaluation, and deployment & automation. You will work with real production datasets and build end-to-end ML pipelines, including model deployment.

Mandatory Requirements

• 2+ years of hands-on ML experience.
• Experience working with datasets containing 20-30 million+ structured records.
• Proven experience in building and training ensemble models.
• Proficiency with algorithms such as XGBoost, Random Forest, Gradient Boosting, Logistic Regression, and SVM.

Core Responsibilities

• Data Preparation: Conduct data sanity checks, clean data using SQL and Pandas, handle missing values, outliers, and perform necessary transformations.
• Exploratory Data Analysis (EDA): Perform deep EDA to identify patterns, correlations, and distributions, and present insights clearly.
• Feature Engineering (Critical): Design structured feature engineering, create derived features and aggregations, apply feature transformations, develop time-based features (if applicable), and implement effective encoding strategies.
• Model Training: Train ensemble models, neural networks (MLP/basic deep learning), run AutoML experiments, and effectively compare and benchmark models.
• Evaluation: Utilize metrics like Accuracy, Precision/Recall, ROC-AUC, RMSE, and business KPIs. Interpret confusion matrices and clearly explain results to stakeholders.
• Deployment & Automation: Deploy models into production, automate training and evaluation pipelines, and understand model behavior in production environments.

Tools & Exposure (Good to Have)

• Familiarity with AutoML frameworks, KNIME, and RapidMiner.
• Experience collaborating with Data Engineering teams.
• An automation mindset for pipelines, scripting, and ensuring reproducibility.

Ideal Candidate Profile

You enjoy working with structured data, possess strong feature engineering skills, think in terms of data pipelines, understand model trade-offs, have deployed at least one production model, and have a deep understanding of why models work, not just how to train them. If you are excited about building real production ML systems and taking ownership end-to-end, we encourage you to apply!