Custom Software Engineer - Data Engineering & Machine Learning

Custom Software Engineer

Project Role : Custom Software Engineer
Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Must have skills : Data Engineering
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

Mandatory: To work from client Bengaluru office

The position requires a dynamic individual with a solid foundation in computer science and hands-on experience in programming, particularly in Python and SQL. Familiarity with cloud platforms such as AWS, Azure, and Google Cloud Platform (GCP) is essential. Additionally, experience with distributed computing frameworks like Databricks, big data technologies like MongoDB, and Cloud ETL/data warehousing skills using Snowflake are crucial for this role.
Proficiency in machine learning (ML) frameworks, statistical analysis, and data quality methods is vital as the role involves managing data pipelines for AI models. Strong problem-solving, communication, and continuous learning abilities are also key soft skills we are looking for in a candidate.
Technical Skills
Programming Languages: Mastery of Python is essential, often alongside SQL, Java, C++, or Scala.
Cloud Platforms: Experience with cloud-based AI/ML platforms like Azure, Amazon Web Services (AWS), and Google Cloud (GCP) is a must.
Big Data Technologies: Expertise in tools like Databricks, Apache Spark , and Apache Kafka for processing and streaming large datasets is vital.
ETL and Data Warehousing: Proficiency in building data pipelines and understanding data warehousing/lake concepts using Snowflake is critical for feeding AI systems.
Databases: Familiarity with both relational (SQL) and non-relational (NoSQL) databases for data storage is important.
ML Frameworks: Knowledge of frameworks such as ML Flow, TensorFlow, PyTorch, and Streamlit is necessary for deploying AI solutions.
Data Modeling: The ability to design data models for AI applications is crucial.

Knowledge & Understanding
Machine Learning Concepts: A strong understanding of algorithms, feature engineering, and data quality methods.
Responsible AI: Awareness of responsible AI principles and data privacy regulations.
Generative AI & LLMs: Experience with generative AI and Large Language Models (LLMs) is increasingly important