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What if your ML models influenced decisions at massive global scale
A global, data-driven tech platform is hiring Machine Learning Engineers to work on high-impact problems involving data, experimentation, and production-grade ML systems — not just notebooks.
CTC offered: Upto 90 LPA
Job Responsibilities
Build Machine Learning models and data pipelines to deliver insightful yet practical solutions.
Seek high quality, reliable, and scalable solutions that deliver real value to eBay customers.
Collaborate with Management,Product Management and Applied Researchers in the team on
shaping the department's vision and roadmap.
Mentor more junior peers, as well as review their work and provide frequent feedback and
advice.
Promote engineering excellence and standards within the team.
Help interview and hire additional, exceptional teammates!
Minimum Qualifications
B.Sc / B.Tech / B.E. in Computer Science, SW engineering or equivalent field with 7+ years of
industrial experience in Applied Machine Learning and Data Engineering
Experience in Production-grade coding in Python.
Industrial experience in productizing ML-Based solutions including online inference.
Experience in Big Data processing frameworks, e.g. Hadoop, Spark / PySpark, SQL including
performance tuning.
Excellent verbal and written communication and collaboration skills
Thought leadership and mentoring skills
Additional Qualifications
Open-source tool building/commits to popular threads.
Experience in Language models and KG building
Job ID: 140242259
Skills:
Machine Learning, SAP BTP, Tensorflow, MLops, Pytorch, Data Science, Databricks, Azure, Python, LLMOps, AI engineering, Scikit-learn, cloud-based data platforms, ML frameworks
Skills:
snowflake , Github, Machine Learning, Deep Learning, Tensorflow, Pytorch, MLops, Spark, Gitlab, Azure, Python, AWS, LLMOps, Ai, Agentic AI, Agentic Coding Frameworks
Skills:
, Pytorch, Python, FSDP, benchmark design, model optimization, Compression, vision-language model architectures, document AI, experiment tracking, DeepSpeed, evaluation methodology, reproducibility practices
Skills:
Gcp, Docker, Azure, Kubernetes, Python, AWS, Airflow, TFX, MLflow
Skills:
Hadoop, Aws Services, Apache Spark, Kafka, Redis, Sql, Docker, Unix Shell, Rest Apis, Kubernetes, Python, Airflow, Lang graph, Flink, Lang chain, Gen AI, ETL processes, AI agents, Big Data Engineering, Control-M, RAG pipelines, MCP tools
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