We are seeking a highly skilled and innovative Data Engineer to join our dynamic team. This role is ideal for professionals passionate about building scalable data pipelines and enabling machine learning and analytics at scale. Youll play a key role in designing and implementing systems that support real-time and batch data processing for advanced AI-driven use cases, including Retrieval-Augmented Generation (RAG). Key Responsibilities: Develop, maintain, and scale data pipelines using Airflow (preferably Metaflow ) and other orchestration tools. Design microservices-based data solutions to support AI/ML initiatives. Work with vector databases to optimize data retrieval for RAG and generative AI models. Collaborate with data scientists, ML engineers, and software engineers to integrate data engineering solutions into broader systems. Ensure data quality, reliability, and governance across all data pipelines and storage layers. Required Skills: Proficiency in Python for data processing, scripting, and service development. Experience with Airflow (Metaflow preferred) or other workflow orchestration tools. Strong knowledge of Microservices architecture and REST APIs. Familiarity with RAG (Retrieval-Augmented Generation) frameworks and concepts. Experience with Vector databases (e.g., Pinecone, FAISS, Weaviate, Qdrant). Solid understanding of data modeling, ETL/ELT processes, and cloud data platforms.

Develop cross-platform apps (iOS & Android) using React Native Integrate APIs & backend services Build modern, intuitive UI/UX Write clean, testable, documented code Debug and fix cross-platform issues CI/CD exposure is a plus Required Candidate profile Min 3-6 yrs in React Native (iOS & Android) Strong with Redux/MobX Exp publishing to App Store & Play Store Knowledge of native modules & push notifications Familiar with mobile security & REST APIs

Min 3–4 yrs with Ruby on Rails in a production environment. Develop and maintain backend services using ROR. Collaborate with frontend team to design RESTful APIs. Write clean/tested code, optimize performance. Required Candidate profile Solid understanding of Relational DB(PostgreSQL preferred) Exp building & consuming RESTful APIs Good knowledge of test-driven development, MiniTest Exp with Docker, Kubernetes, AWS

- Be an integral part of an autonomous,multidisciplinary squad that tackles challenges in a particular domain of machine health platform - Be a focal point of backend related challenges - Design,Coding,Testing, Implementation & Production Monitoring Required Candidate profile - Experience in Go/Java/Python/C#(any) - Experience in Reactjs or Angular-Must - Exp. in Javascript - Willing to migrate to Golang - Experience with cloud infrastructure (GCP, AWS, Azure etc)

Role & responsibilities Develop and maintain automated test suites using Appium and Python for mobile testing. Design E2E, smoke, integration, and regression tests for the app, backend APIs, and data flows. Collaborate with developers and DevOps to integrate automation into CI/CD pipelines. Validate event flows, API responses, and data integrity across systems. Investigate failures and ensure quick resolution by collaborating with relevant teams. Monitor automated tests and report coverage and stability metrics. Preferred candidate profile 3+ years of experience in QA automation, with focus on mobile and backend systems. Hands-on experience with Appium and Python. Experience testing RESTful APIs and backend logic. Familiarity with cloud services (AWS/GCP/Azure). Experience with CI/CD tools like GitHub Actions, Jenkins, or GitLab CI. Understanding of relational databases and data validation using SQL. Nice to Have : Experience with testing platforms like AWS Device Farm, BrowserStack, or Sauce Labs. Exposure to observability and error tracking tools like CloudWatch, Sentry, or Datadog. Experience with test case management platforms like Xray or TestRail. Knowledge of analytics event validation (Firebase, Segment, etc.).

Responsibilities :- Build and manage data infrastructure on AWS , including S3, Glue, Lambda, Open Search, Athena, and CloudWatch using IaaC tool like Terraform Design and implement scalable ETL pipelines with integrated validation and monitoring. Set up data quality frameworks using tools like Great Expectations , integrated with PostgreSQL or AWS Glue jobs. Implement automated validation checks at key points in the data flow: post-ingest, post-transform, and pre-load. Build centralized logging and alerting pipelines (e.g., using CloudWatch Logs, Fluent bit ,SNS, File bit ,Logstash , or third-party tools). Define CI/CD processes for deploying and testing data pipelines (e.g., using Jenkins, GitHub Actions) Collaborate with developers and data engineers to enforce schema versioning, rollback strategies, and data contract enforcement. Preferred candidate profile 5+ years of experience in DataOps, DevOps, or data infrastructure roles. Proven experience with infrastructure-as-code (e.g., Terraform, CloudFormation). Proven experience with real-time data streaming platforms (e.g., Kinesis, Kafka). Proven experience building production-grade data pipelines and monitoring systems in AWS . Hands-on experience with tools like AWS Glue , S3 , Lambda , Athena , and CloudWatch . Strong knowledge of Python and scripting for automation and orchestration. Familiarity with data validation frameworks such as Great Expectations, Deequ, or dbt tests. Experience with SQL-based data systems (e.g., PostgreSQL). Understanding of security, IAM, and compliance best practices in cloud data environments.

Build and manage data infrastructure on AWS , including S3, Glue, Lambda, Open Search, Athena, and CloudWatch using IaaC tool like Terraform Design and implement scalable ETL pipelines with integrated validation and monitoring. Set up data quality frameworks using tools like Great Expectations , integrated with PostgreSQL or AWS Glue jobs. Implement automated validation checks at key points in the data flow: post-ingest, post-transform, and pre-load. Build centralized logging and alerting pipelines (e.g., using CloudWatch Logs, Fluent bit ,SNS, File bit ,Logstash , or third-party tools). Define CI/CD processes for deploying and testing data pipelines (e.g., using Jenkins, GitHub Actions) Collaborate with developers and data engineers to enforce schema versioning, rollback strategies, and data contract enforcement. Preferred candidate profile 5+ years of experience in DataOps, DevOps, or data infrastructure roles. Proven experience with infrastructure-as-code (e.g., Terraform, CloudFormation). Proven experience with real-time data streaming platforms (e.g., Kinesis, Kafka). Proven experience building production-grade data pipelines and monitoring systems in AWS . Hands-on experience with tools like AWS Glue , S3 , Lambda , Athena , and CloudWatch . Strong knowledge of Python and scripting for automation and orchestration. Familiarity with data validation frameworks such as Great Expectations, Deequ, or dbt tests. Experience with SQL-based data systems (e.g., PostgreSQL). Understanding of security, IAM, and compliance best practices in cloud data environments.