AI​/ML Engineer

Location: Bengaluru

Job Title:

ML Engineer

Experience:

3-5 years

Location:

Remote/Hybrid (Bangalore)
Job Type: Full time
Joining Timeline:
Immediate to 15 days

Please contact  or

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 product ionize 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:
Regression, classification, clustering
Time-series forecasting
Anomaly detection and recommendation systems
Apply model evaluation techniques (cross-validation, bias-variance tradeoff, metrics selection)

ML Engineering & Deployment
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:
Define data schemas
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
Programming:  Python (Num Py, Pandas, Scikit-learn)
ML Libraries:  XGBoost, Light

GBM, Tensor Flow / 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)
AWS / GCP / Azure (S3, Big Query, Sage Maker, Vertex AI, etc.)
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