Senior Software Developer Female Graduate Jobs in Bangalore

Key Responsibilities Model Development: Lead the design and development of quantitative data engineeringmodels, including algorithms, data pipelines, and data processing systems, to supportbusiness requirements. Data Processing: Develop and maintain data processing pipelines to ingest, clean,transform, and aggregate large volumes of data from various sources, ensuring dataquality and reliability. Algorithm Development: Design and implement algorithms for data analysis, machinelearning, and statistical modeling, using techniques such as regression analysis,clustering, and predictive modeling. Performance Optimization: Identify and implement optimizations to improve theperformance and efficiency of data processing and modeling algorithms, consideringfactors like scalability and resource utilization. Data Visualization: Create visualizations of data and model outputs to communicateinsights and findings to stakeholders. Data Quality Assurance: Implement data quality checks and validation processes toensure the accuracy, completeness, and consistency of data used in models andanalyses. Model Evaluation: Evaluate the performance of data engineering models using metricsand validation techniques, and iterate on models to improve their accuracy andeffectiveness. Collaboration: Collaborate with data scientists, analysts, and business stakeholders tounderstand requirements, develop models, and deliver insights that drive businessdecisions. Documentation: Document the design, implementation, and evaluation of dataengineering models, including assumptions, methodologies, and results, to ensurereproducibility and transparency. Continuous Learning: Stay updated with the latest trends, tools, and technologies inquantitative data engineering and data science, and continuously improve your skillsand knowledge.Desired Skills and Experience Data Engineering: Strong background in data engineering principles, including dataingestion, data processing, data transformation, and data storage, using tools andframeworks such as Apache Spark, Apache Flink, or AWS Glue. Quantitative Analysis: Proficiency in quantitative analysis techniques, includingstatistical modeling, machine learning, and data mining, with experience inimplementing algorithms for regression analysis, clustering, classification, and predictivemodeling. Programming Languages: Proficiency in programming languages commonly used fordata engineering and quantitative analysis, such as Python, R, Java, or Scala, as well asexperience with SQL for data querying and manipulation. Big Data Technologies: Familiarity with big data technologies and platforms, such asHadoop, Apache Kafka, Apache Hive, or AWS EMR, for processing and analyzing largevolumes of data. Data Visualization: Experience in data visualization techniques and tools, such asMatplotlib, Seaborn, or Tableau, for creating visualizations of data and model outputs tocommunicate insights effectively. Machine Learning Frameworks: Familiarity with machine learning frameworks andlibraries, such as PyTorch for implementing and deploying machine learning models. Cloud Computing: Experience with cloud computing platforms, such as AWS, Azure, orGoogle Cloud Platform, and proficiency in using cloud services for data engineering andmodel deployment. Software Development: Strong software development skills, including proficiency insoftware design patterns, version control systems (e.g., Git), and software testingframeworks, to develop robust and maintainable code. Problem-solving Skills: Excellent problem-solving skills, with the ability to analyzecomplex data engineering and quantitative analysis problems, identify solutions, andimplement them effectively. Communication and Collaboration: Strong communication and collaboration skills, withthe ability to work effectively with cross-functional teams, including data scientists,analysts, and business stakeholders, to understand requirements and deliver solutions. Domain Knowledge: Domain knowledge in areas such as finance, healthcare, ormarketing, depending on the industry, to understand the context and requirements ofdata engineering models in specific domains.Continuous Learning: A commitment to continuous learning and staying updated withthe latest trends, tools, and technologies in data engineering, quantitative analysis, andmachine learning.

Role: Senior Software Engineer / Lead Software EngineerDate: 2026/April/06Experience: 4-6 yearsOpen Positions: 2Joining Timeline: Immediate (within 10-15 days)Job Description:Arnest is looking for experienced full-stack developers with strong AI/ML implementation expertise.The ideal candidate will have a proven track record of designing, developing, and deploying scalableapplications integrated with advanced analytics and machine learning models.You will play a key role in building intelligent AI solutions, working across the stack-from datapipelines and model development to frontend dashboards and system deployment.

Key Responsibilities Model Development: Lead the design and development of quantitative data engineeringmodels, including algorithms, data pipelines, and data processing systems, to supportbusiness requirements. Data Processing: Develop and maintain data processing pipelines to ingest, clean,transform, and aggregate large volumes of data from various sources, ensuring dataquality and reliability. Algorithm Development: Design and implement algorithms for data analysis, machinelearning, and statistical modeling, using techniques such as regression analysis,clustering, and predictive modeling. Performance Optimization: Identify and implement optimizations to improve theperformance and efficiency of data processing and modeling algorithms, consideringfactors like scalability and resource utilization. Data Visualization: Create visualizations of data and model outputs to communicateinsights and findings to stakeholders. Data Quality Assurance: Implement data quality checks and validation processes toensure the accuracy, completeness, and consistency of data used in models andanalyses. Model Evaluation: Evaluate the performance of data engineering models using metricsand validation techniques, and iterate on models to improve their accuracy andeffectiveness. Collaboration: Collaborate with data scientists, analysts, and business stakeholders tounderstand requirements, develop models, and deliver insights that drive businessdecisions. Documentation: Document the design, implementation, and evaluation of dataengineering models, including assumptions, methodologies, and results, to ensurereproducibility and transparency. Continuous Learning: Stay updated with the latest trends, tools, and technologies inquantitative data engineering and data science, and continuously improve your skillsand knowledge.Desired Skills and Experience Data Engineering: Strong background in data engineering principles, including dataingestion, data processing, data transformation, and data storage, using tools andframeworks such as Apache Spark, Apache Flink, or AWS Glue. Quantitative Analysis: Proficiency in quantitative analysis techniques, includingstatistical modeling, machine learning, and data mining, with experience inimplementing algorithms for regression analysis, clustering, classification, and predictivemodeling. Programming Languages: Proficiency in programming languages commonly used fordata engineering and quantitative analysis, such as Python, R, Java, or Scala, as well asexperience with SQL for data querying and manipulation. Big Data Technologies: Familiarity with big data technologies and platforms, such asHadoop, Apache Kafka, Apache Hive, or AWS EMR, for processing and analyzing largevolumes of data. Data Visualization: Experience in data visualization techniques and tools, such asMatplotlib, Seaborn, or Tableau, for creating visualizations of data and model outputs tocommunicate insights effectively. Machine Learning Frameworks: Familiarity with machine learning frameworks andlibraries, such as PyTorch for implementing and deploying machine learning models. Cloud Computing: Experience with cloud computing platforms, such as AWS, Azure, orGoogle Cloud Platform, and proficiency in using cloud services for data engineering andmodel deployment. Software Development: Strong software development skills, including proficiency insoftware design patterns, version control systems (e.g., Git), and software testingframeworks, to develop robust and maintainable code. Problem-solving Skills: Excellent problem-solving skills, with the ability to analyzecomplex data engineering and quantitative analysis problems, identify solutions, andimplement them effectively. Communication and Collaboration: Strong communication and collaboration skills, withthe ability to work effectively with cross-functional teams, including data scientists,analysts, and business stakeholders, to understand requirements and deliver solutions. Domain Knowledge: Domain knowledge in areas such as finance, healthcare, ormarketing, depending on the industry, to understand the context and requirements ofdata engineering models in specific domains. Continuous Learning: A commitment to continuous learning and staying updated withthe latest trends, tools, and technologies in data engineering, quantitative analysis, andmachine learning.

Key Responsibilities Analyst Workflows: Design and development of company integrated content publishing platform using a proprietary 3rd party editorial and publishing platform for integrated digital publishing. Designing and Developing APIs: Design and development of robust, scalable, and secure APIs on AWS, considering factors like performance, reliability, and cost-efficiency. AWS Service Integration: Integrate APIs with various AWS services such as AWS Lambda, Amazon API Gateway, Amazon SQS, Amazon SNS, AWS Glue, and others, to build comprehensive and efficient solutions. Performance Optimization: Identify and implement optimizations to improve performance, scalability, and efficiency, leveraging AWS services and tools. Security and Compliance: Ensure APIs are developed following best security practices, including authentication, authorization, encryption, and compliance with relevant standards and regulations. Monitoring and Logging: Implement monitoring and logging solutions for APIs using AWS CloudWatch, AWS X-Ray, or similar tools, to ensure availability, performance, and reliability. Continuous Integration and Deployment (CI/CD): Establish and maintain CI/CD pipelines for API development, automating testing, deployment, and monitoring processes on AWS. Documentation and Training: Create and maintain comprehensive documentation for internal and external users, and provide training and support to developers and stakeholders. Team Collaboration: Collaborate effectively with cross-functional teams, including product managers, designers, and other developers, to deliver high-quality solutions that meet business requirements. Problem Solving: troubleshooting efforts, identifying root causes and implementing solutions to ensure system stability and performance. Stay Updated: Stay updated with the latest trends, tools, and technologies related to development on AWS, and continuously improve your skills and knowledge. Desired Skills and Experience Development: 5+ years of extensive experience in designing, developing, and deploying using modern technologies, with a focus on scalability, performance, and security. AWS Services: proficiency in using AWS services such as AWS Lambda, Amazon API Gateway, Amazon SQS, Amazon SNS, Amazon SES, Amazon RDS, Amazon DynamoDB, and others, to build and deploy API solutions. Programming Languages: Proficiency in programming languages commonly used for development, such as Python, Node.js, or others, as well as experience with serverless frameworks like AWS. Architecture Design: Ability to design scalable and resilient API architectures using microservices, serverless, or other modern architectural patterns, considering factors like performance, reliability, and cost-efficiency. Security: Strong understanding of security principles and best practices, including authentication, authorization, encryption, and compliance with standards like OAuth, OpenID Connect, and AWS IAM. DevOps Practices: Familiarity with DevOps practices and tools, including CI/CD pipelines, infrastructure as code (IaC), and automated testing, to ensure efficient and reliable deployment on AWS. Problem-solving Skills: Excellent problem-solving skills, with the ability to troubleshoot complex issues, identify root causes, and implement effective solutions to ensure the stability and performance. Communication Skills: Strong communication skills, with the ability to effectively communicate technical concepts to both technical and non-technical stakeholders, and collaborate with crossfunctional teams. Agile Methodologies: Experience working in Agile development environments, following practices like Scrum or Kanban, and ability to adapt to changing requirements and priorities. Continuous Learning: A commitment to continuous learning and staying updated with the latest trends, tools, and technologies related to development and AWS services. Bachelor's Degree: A bachelor's degree in Computer Science, Software Engineering, or a related field is often preferred, although equivalent experience and certifications can also be valuable.