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Mid-level ML role in Bangalore with a recognizable brand and broad candidate pool.
Low because core ML/AI, NLP, and production deployment skills are readily transferable across industries.
High due to explicit 3–9 years and many mandatory production ML, deployment, and tool requirements.
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Design, develop, and deploy scalable ML, deep learning, NLP, Generative AI, and LLM-based solutions with ownership from ideation through production and monitoring.
Develop production-ready code using Python, PySpark, and related frameworks, including building end-to-end ETL pipelines and deploying models using tools like Docker, MLflow, FastAPI.
Manage and monitor deployed ML models to ensure performance, including time series forecasting systems, collaborating with cross-functional teams, and aligning solutions with business requirements.
3 to 9 years of experience in Data Science or Machine Learning roles.
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or related field.
Strong expertise in Python, PySpark, SQL, and ML/DL frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
Experience in production-level ML deployment and monitoring with knowledge of Docker, MLflow, FastAPI, and cloud-native infrastructures; familiarity with Hadoop and databases like PostgreSQL, MongoDB, YugaByte.
Experienced in applying modern AI/ML techniques including Generative AI, Large Language Models, Langchain, LangGraph, MCP, and A2A within multi-agent frameworks.
Proficient in building and operationalizing ML solutions end-to-end, including feature engineering, model evaluation, and deployment in complex production environments.
Able to communicate technical insights effectively to both technical and business stakeholders and work collaboratively across functions.