Senior AI/ML Engineer – R01571293

Brillio; Bengaluru; On-site; INR 1,200,000 - 2,500,000; Full time

Job summary

Brillio in Bengaluru seeks experienced data science professionals to design and deploy advanced ML models. You will perform rigorous EDA, validate outputs, and collaborate cross-functionally to integrate AI solutions into workflows.

Responsibilities include producing robust forecasts, applying probabilistic models, and ensuring data quality with tools like Great Expectations. Strong Python/SQL skills and DL experience are essential for production-grade delivery.

Qualifications

• Proficiency in Python and SQL for data manipulation and modeling.
• Experience with classical ML and deep learning tools.
• Strong statistical testing and regression knowledge.
• Ability to build and interpret predictive models.
• Experience with model deployment and orchestration.

Responsibilities

• Design and develop ML models to solve business problems.
• Perform EDA and statistical analyses to derive insights.
• Deploy AI/ML models on GCP with scalable production readiness.
• Collaborate with teams to define data needs and validate outputs.
• Utilize KubeFlow and BentoML for orchestration and monitoring.
• Forecast using methods like ARIMA and exponential smoothing.
• Apply probabilistic graph models to improve accuracy.
• Maintain data quality and track model performance.

Skills

Python

SQL

ML algorithms

Deep learning frameworks

Statistical analysis

Classification

Forecasting

Probabilistic graphs

Model deployment

Distance metrics

Data quality frameworks

Education

Bachelor’s degree in CS/DS/Math

Tools

KubeFlow

BentoML

PySpark

SAS

SPSS

R

Job description

Experience Range

With at least 2 to 4 years of hands‑on experience in advanced data science, machine learning, and AI engineering roles

Key Responsibilities

• Design and develop advanced machine learning models using classic algorithms and deep learning techniques to address complex business challenges and deliver measurable improvements
• Conduct comprehensive exploratory data analysis (EDA) and statistical analysis, including hypothesis testing, regression, and classification, to extract actionable insights from large datasets
• Implement, optimize, and deploy AI/ML models on Google Cloud Platform (GCP), ensuring scalability, reliability, and efficient integration into production environments
• Collaborate with cross‑functional teams to define data requirements, validate model outputs, and integrate AI solutions seamlessly into existing workflows
• Utilize KubeFlow and BentoML for efficient model orchestration, deployment, and monitoring, ensuring robust operational performance
• Perform rigorous forecasting using methods such as exponential smoothing, ARIMA, and ARIMAX to support data‑driven business planning
• Apply probabilistic graph models and advanced statistical methods to enhance predictive accuracy and interpretability of AI solutions
• Maintain high standards for data quality and model performance using frameworks like Great Expectations and Evidently AI, tracking key metrics and outcomes

Required Skills

• Proficiency in Python and SQL for data manipulation, analysis, and model development
• Hands‑on experience with classic machine learning algorithms and deep learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet
• Expertise in statistical analysis including hypothesis testing, T-Test, Z-Test, and regression (linear, logistic)
• Experience with classification techniques such as decision trees and support vector machines (SVM)
• Knowledge of forecasting methods including exponential smoothing, ARIMA, and ARIMAX
• Ability to implement and interpret probabilistic graph models
• Familiarity with tools for model deployment and orchestration such as KubeFlow and BentoML
• Competence in computing and analyzing distance metrics (Hamming, Euclidean, Manhattan)
• Experience with data quality frameworks such as Great Expectations and Evidently AI

Preferred Skills

• Experience with GenAI and Agentic AI technologies
• Hands‑on expertise with PySpark, SAS, or SPSS for large‑scale statistical computing
• Proficiency in R and R Studio for statistical modeling and data visualization

Desired Qualifications

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
• Certification in machine learning or data science from recognized platforms such as TensorFlow Developer Certificate or Google Professional Machine Learning Engineer
• Certification in cloud technologies, for example Google Cloud Certified – Professional Data Engineer