Machine Learning Engineer
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Location - Chennai / Mumbai / Pune / Hyderabad / Bangalore (Hybrid)
Notice Period - Immediate joinerto 30days
What You will do:
Play the role of Data Analyst / ML Engineer
Collection, cleanup, exploration and visualization of data
Perform statistical analysis on data and build ML models
Implement ML models using some of the popular ML algorithms
Use Excel to perform analytics on large amounts of data
Understand, model and build to bring actionable business intelligence out of data that is available in different formats
Work with data engineers to design, build, test and monitor data pipelines for ongoing business operations.
Basic Qualifications :
Experience: 4+ years.
Hands-on development experience playing the role of Data Analyst and/or ML Engineer.
Experience in working with excel for data analytics
Experience with statistical modelling of large data sets
Experience with ML models and ML algorithms
Coding experience in Python.
Nice to have Qualifications
Experience with wide variety of tools used in ML
Experience with Deep learning.
Benefits
Competitive salary.
Hybrid work model.
Learning and gaining experience rapidly.
Reimbursement for basic working set up at home.
Insurance (including a top up insurance for COVID).
Job ID: 144779067
Skills:
MLops, Cloud Services, Sql, Python, GenAI, RAG, Feature engineering
Skills:
BigQuery, Machine Learning, Google Cloud Platform, Cloud Storage, MLops, Python, Cloud Functions, GitHub Actions, Cloud Run, Model Monitoring, Vertex AI, Observability
Skills:
Mvs, Tensorflow, Numpy, Git, Pytorch, Opencv, Python, SLAM, pipeline optimization, object detection models, COLMAP, large-scale data processing, segmentation techniques, 3D vision techniques, SfM
Skills:
Google Cloud Platform, Tensorflow, Nltk, Pytorch, MLops, Pandas, Azure, Python, AWS, Gensim, Scikit-learn, MLflow, Retrieval-Augmented Generation, Large Language Models
Skills:
Pytorch, Python, Deep Learning, Attention mechanisms, Transformer architectures, Fine-tuning and adapting LLMs, Evaluation in production, Retrieval-augmented generation, MLOps tooling, Prompt engineering, Distributed multi-GPU training
