Machine Learning Engineer.
This mandate is run by SYMPHONI HR, a Mumbai-based executive search firm, est. 2003. Applications are email-verified and reach the search team running the role — confidentially, always.
About the organisation.
Our client is a leading global professional services firm specializing in accounting, finance, and advisory solutions. They are committed to driving innovation through advanced technology and data-driven insights to solve complex business challenges for their diverse international clientele.
About the role.
We are seeking a skilled Machine Learning Engineer to join a dynamic team in Bengaluru. In this hybrid role, you will design, develop, and deploy scalable machine learning models and end-to-end pipelines to support critical financial and business operations.
What you will do.
- Design, train, evaluate, and optimize machine learning models to address complex business requirements.
- Build and implement scalable, production-grade ML pipelines covering data ingestion, feature engineering, and deployment.
- Deploy models at scale using AWS services to ensure high availability and low-latency performance.
- Develop ETL pipelines to process large-scale structured and unstructured datasets for model training.
- Maintain operational excellence by monitoring, debugging, and continuously improving ML model performance.
- Stay updated with the latest AI research and drive the adoption of cutting-edge methodologies.
- Create clear technical documentation and communicate model trade-offs to both technical and non-technical stakeholders.
What you bring.
- Bachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field.
- 5–6.6 years of professional experience in machine learning engineering or applied ML.
- Strong proficiency in Python and experience with frameworks such as PyTorch, TensorFlow, or JAX.
- Solid understanding of supervised/unsupervised learning, deep learning, NLP, and recommendation systems.
- Hands-on experience with MLOps practices, including CI/CD for ML, model versioning, and automated retraining.
- Proficiency in SQL and large-scale data platforms like Spark, Hadoop, or Redshift.
- Strong software engineering fundamentals, including data structures, algorithms, and system design.
- Proven experience with AWS cloud infrastructure.
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