SSE - Machine Learning

Bigbasket.com · Bengaluru

  • Experience3–7 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelmid
  • Posted2 Sept 2026
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About Bigbasket.com

Bigbasket.com is hiring in Bengaluru in ecommerce retail. This role looks for around 3+ years of experience.

Skills

  • exploratory data analysis
  • NLP
  • recommender systems
  • machine learning algorithms
  • classification
  • regression
  • clustering
  • anomaly detection
  • pattern recognition
  • deep learning
  • embeddings
  • LLMs
  • RAG
  • agentic AI
  • search
  • personalisation
  • time series forecasting
  • predictive modelling
  • parallel processing
  • multithreading
  • multiprocessing
  • distributed computing
  • Kafka
  • Flink
  • statistical models
  • probabilistic models
  • transformers
  • PyTorch
  • scikit-learn
  • Python
  • data structures
  • algorithms
  • system design
  • computer vision
  • data governance
  • ethical AI

The role

A machine learning engineer at an e-commerce retail company builds time series forecasting and predictive modelling systems for search and personalisation, using NLP, recommender systems and deep learning. The work also applies PyTorch and Kafka to scalable production pipelines.

Full job description

Responsibilities:

Collaborate with cross-functional teams, including data scientists, engineers, and product managers, to deliver AI-driven solutions.

Ability to do exploratory data analysis, read research papers and state-of-the-art models in literature and implement them.

Design, develop, and maintain scalable machine learning systems for time series forecasting and general predictive modelling (in both CPU and GPU machines).

Requirements:

Strong experience in NLP, recommender systems, and machine learning algorithms (classification, regression, clustering, anomaly detection, pattern recognition techniques, deep learning, embeddings, LLMs/RAG/agentic AI).

Hands-on experience in building ML systems for search and personalisation use cases.

Experience designing and deploying ML solutions at large scale (billions of records).

Experience leveraging parallel processing techniques (multithreading, multiprocessing, distributed computing) to build high-performance, scalable machine learning pipelines and optimise large-scale data processing workloads.

Familiarity with real-time data streaming technologies such as Kafka and Flink.

Experience working with machine learning algorithms and technologies.

Experience working on time series problems, implementing existing methods in general, and the ability to develop new solutions (statistical and probabilistic models, deep learning, and transformers).

Experience working with PyTorch, scikit-learn and time series Python libraries for model training and evaluation experiments.

Experience in Python and the ability to write production-level code.

Critical thinking and strong technical knowledge in data structures, algorithms and system design.

Potential to innovate novel machine learning methods at industry standards and publish at international conferences.

Exposure to natural language processing and computer vision algorithms.

Knowledge of data governance and ethical AI principles.