Machine Learning Engineer - Hyderabad - Circuitry

    Circuitry
    Circuitry Hyderabad

    1 day ago

    Technology / Internet
    Description

    Machine Learning Engineer

    Location: Hyderabad, India – (5 days/onsite)

    Experience: 2–5 Years

    Company:

    About

    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


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