Machine Learning Engineer

Machine Learning Engineer

Location:

Hyderabad, India – (5 days/onsite)

Experience:

2–5 Years
Company:  Circuitry.ai

About Circuitry.ai
Circuitry.ai is an Enterprise AI SaaS company building advanced AI systems for the U.S. SLM sector.
Production-grade RAG architectures
Fine-tuned LLM systems
Knowledge Graph–driven AI
Agentic AI frameworks
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 — not demos.

Important Note
This role requires real, hands-on ML experience with large structured datasets and end-to-end ML pipelines.
Real production datasets
End-to-end ML pipelines
Model deployment experience

This role is not GenAI-focused. GenAI exposure is a plus, but not required.

Role Overview
We are seeking a Machine Learning Engineer with strong expertise in traditional ML algorithms and structured data systems.
Structured data processing
Feature engineering
Model training & evaluation
Deployment & automation

Mandatory Requirements
Experience
2+ years of hands-on ML experience
Worked with datasets containing 20–30M+ structured records
Built and trained ensemble models

Experience with algorithms: XGBoost, Random Forest, Gradient Boosting, Logistic Regression, SVM

Core Responsibilities
Data Preparation
Perform data sanity checks
Clean data using SQL before moving to Pandas
Structured cleaning and preprocessing in Pandas
Handle missing values, outliers, and transformations
Exploratory Data Analysis (EDA)
Perform deep EDA
Identify patterns, correlations, and distributions
Present insights clearly
Feature Engineering (Critical)
Design structured feature engineering
Derived features and aggregations
Feature transformations
Time-based features (if applicable)
Encoding strategies
Model Training
Train ensemble models
Train neural networks (MLP / basic deep learning)
Run AutoML experiments
Compare and benchmark models effectively
Evaluation
Work with Accuracy, Precision/Recall, ROC-AUC, RMSE, and business KPIs
Interpret confusion matrices
Explain results clearly to stakeholders
Deployment & Automation
Deploy models into production
Automate training and evaluation pipelines
Understand model behavior in production

Tools & Exposure (Good to Have)
AutoML frameworks
KNIME
Rapid Miner
Collaboration with Data Engineering teams
Automation mindset (pipelines, scripting, reproducibility)

Ideal Candidate
Enjoys working with structured data
Strong in feature engineering
Thinks in terms of data pipelines
Understands model trade-offs
Has deployed at least one production model
Understands why models work — not just how to train them

If you’re excited about building real production ML systems and taking ownership end-to-end, we’d love to connect!