DeepWork Portfolio Careers

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
• RapidMiner
• 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!