Data Science & Engineering Lead
IndiaFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSPythonSQLETLMachine LearningAzureData engineeringSparkDatabricksMLOps
Requirements
- 7+ years of experience in data science, machine learning, or data engineering roles with proven leadership responsibilities.
- Strong expertise in supervised and unsupervised learning, deep learning, and neural network architectures (CNNs, RNNs, GANs, Transformers).
- Hands-on experience with ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.
- Proven experience building and deploying production-grade ML systems using MLOps platforms (Databricks, SageMaker, Azure ML).
- Strong knowledge of big data processing frameworks such as Apache Spark, EMR, and AWS Glue.
- Expertise in designing ETL pipelines and data workflows using Airflow, DBT, and Airbyte.
- Solid understanding of lakehouse architecture (Delta, Iceberg) and data quality frameworks.
- Strong proficiency in cloud platforms (AWS or Azure), including core services such as IAM, networking, Lambda, Kafka, and API Gateway.
- Experience with SQL and NoSQL databases, with strong skills in data modeling for OLTP and OLAP systems.
- Familiarity with LLM ecosystems and AI tooling such as LangChain, Hugging Face, or OpenAI APIs.
- Strong leadership and mentoring skills.
- Bachelor’s degree in a relevant field (Master’s or PhD preferred).
Responsibilities
- Lead the design, development, and deployment of advanced machine learning models across supervised, unsupervised, deep learning, NLP, and computer vision use cases.
- Architect and optimize scalable data pipelines for batch and streaming data processing using modern frameworks and tools.
- Build and maintain lakehouse architectures (Delta/Iceberg) and implement bronze-silver-gold data modeling standards.
- Design and manage ETL/ELT workflows using tools such as Airflow, DBT, and Airbyte.
- Lead MLOps initiatives, including model deployment, monitoring, and lifecycle management.
- Develop and optimize distributed data processing and training workflows using Spark, EMR, Glue, and similar big data technologies.
- Oversee database design and optimization across OLTP and OLAP systems, including relational and NoSQL databases.
- Drive cloud architecture decisions and implementations on AWS or Azure.
- Build BI and analytics solutions using tools like Power BI, Tableau, or QuickSight.
- Mentor and guide junior engineers, promoting best practices in AI/ML engineering and software development.
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