AI/ML Engineer

SynapOne; Bengaluru; On-site; INR 800,000 - 1,200,000; Full time

Job summary

A leading data science company in Bengaluru is seeking a hands-on Data Scientist/Machine Learning Engineer to translate business challenges into scalable solutions. This role demands strong analytical skills and the ability to productionize models effectively. The candidate will work with cross-functional teams to build and maintain data-driven solutions, covering diverse use cases like forecasting and anomaly detection. Success in this position requires proficiency in ML libraries, strong SQL skills, and familiarity with deployment techniques.

Qualifications

• Strong querying and data manipulation skills in SQL.
• Experience with model deployment using REST APIs or batch jobs.
• Familiarity with Docker and CI/CD for ML workflows.

Responsibilities

• Convert business problems into ML problem statements.
• Perform EDA, feature engineering, and selection.
• Deploy models using APIs or batch pipelines.

Skills

XGBoost

LightGBM

TensorFlow

PyTorch

SQL

Statistics

Supervised ML

Unsupervised ML

Time-series fundamentals

Model deployment

Docker

CI/CD

Education

Bachelor’s or master’s degree in computer science

Tools

Airflow

MLflow

Weights & Biases

Job description

Please contact 7976457434 or [e-mail]

Interview Rounds:

• 2 – Technical Rounds
• 1 – Managerial/HR round

Role Overview

We are looking for a hands‑on Data Scientist / Machine Learning Engineer who can translate business problems into scalable data science and ML solutions. The role requires strong analytical thinking, solid ML fundamentals, and the ability to productionize models in real‑world environments.

You will work closely with product, engineering, and business stakeholders to build, deploy, and maintain data‑driven solutions across forecasting, recommendation, classification, and anomaly detection use cases.

Key Responsibilities

Data Science & Modeling

• Understand business problems and convert them into ML problem statements
• Perform EDA, feature engineering, and feature selection
• Build and evaluate models using:
  • Time‑series forecasting
  • Anomaly detection and recommendation systems
• Apply model evaluation techniques (cross‑validation, bias‑variance tradeoff, metrics selection)
• Productionize ML models using Python‑based pipelines
• Build reusable training and inference pipelines
• Implement model versioning, experiment tracking, and retraining workflows
• Deploy models using APIs or batch pipelines
• Monitor model performance, data drift, and prediction stability

Data Engineering Collaboration

• Work with structured and semi‑structured data from multiple sources
• Collaborate with data engineers to:
  • Build feature pipelines
  • Ensure data quality and reliability

Stakeholder Communication

• Present insights, model results, and trade‑offs to non‑technical stakeholders
• Document assumptions, methodologies, and limitations clearly
• Support business decision‑making with interpretable outputs

Required Skills (Mandatory Skills)

Core Technical Skills

• ML Libraries: XGBoost, LightGBM, TensorFlow / PyTorch (working knowledge)
• SQL: Strong querying and data manipulation skills
• Statistics: Probability, hypothesis testing, distributions
• Modeling: Supervised & unsupervised ML, time‑series basics

ML Engineering Skills (Mandatory Skills)

• Experience with model deployment (REST APIs, batch jobs)
• Familiarity with Docker and CI/CD for ML workflows
• Experience with ML lifecycle management (experiments, versioning, monitoring)
• Understanding of data leakage, drift, and retraining strategies

Cloud & Tools (Any One Stack is Fine)

• Workflow tools: Airflow, Prefect, or similar
• Experiment tracking: MLflow, Weights & Biases (preferred)

Good to Have

• Experience in domains like manufacturing, supply chain, fintech, retail, or consumer tech
• Exposure to recommendation systems, forecasting, or optimization
• Knowledge of feature stores and real‑time inference systems
• Experience working with large‑scale or noisy real‑world datasets

Educational Qualification

• Bachelor’s or master’s degree in computer science, Statistics, Mathematics, Engineering, or related fields