Machine Learning Engineer

6 months ago

Bengaluru, IndiaMid Level / Senior

Responsibilities

Design and build scalable recommendation and personalization models for ranking, re-ranking, user embeddings, and semantic retrieval.
Own model data preparation, training, evaluation, versioning, deployment, monitoring, continuous training, and refresh strategies.
Design and interpret A/B experiments to improve model performance and user engagement.
Collaborate with data engineers, MLOps teams, and product managers to integrate models into real-time and batch inference pipelines.
Use Databricks, MLflow, and feature stores to support experimentation and reproducibility.
Apply LLMs and AI agents to personalization workflows and ML development pipelines.
Contribute to personalization-service architecture and model-serving infrastructure.
Mentor junior data scientists and ML engineers through code reviews, technical guidance, and best-practice sharing.

At least 3–7 years of experience in machine learning, applied data science, or a related field, focused on recommendation systems or personalization.
Demonstrated experience developing and deploying ML models in production environments.
Deep understanding of ranking systems, user behavior modeling, and evaluation techniques such as NDCG, AUC, MAP, and CTR.
Proficiency in Python, PyTorch, TensorFlow, Transformers, or LightGBM.
Experience with Databricks, Spark, or similar big-data platforms for model training and data processing.
Familiarity with model versioning, feature stores, experiment tracking, and MLflow.
Strong understanding of A/B testing design, analysis, and result interpretation.
Experience with LLM-based pipelines, semantic search, or vector similarity systems such as FAISS or Vespa is a plus.
Comfort working in cloud-native environments such as AWS or GCP.
Experience with AI agents, LangChain, workflow automation, real-time inference, streaming architectures, Kafka, or Flink is nice to have.
Experience with personalization systems at scale and contributions to open-source ML tools or personalization research are nice to have.

1,001-5,000 employees