About the role
What Youll Do
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Design and implement technical features leveraging best practices for technology stack being used
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Collaborate with client-facing teams to understand solution context and contribute to technical requirement gathering and analysis
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Work with technical architects on the team to validate design and implementation approach
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Write production-ready code that is easily testable, understood by other developers, and accounts for edge cases and errors
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Ensure the highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews
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Write unit tests as well as higher-level tests to handle expected edge cases and errors gracefully, as well as happy paths
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Uses bug tracking, code review, version control, and other tools to organize and deliver work
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Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues, and dependencies
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Consistently contribute in researching evaluating the latest technologies through rapid learning, conducting proofs-of-concept and creating prototype solutions
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Support the project architect in designing modules/component of the overall project/product architecture
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Breaks down large features into estimable tasks lead estimation and can defend them with clients
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Implement complex features with limited guidance from the engineering lead. For example service or application-wide change
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Systematically debug code issues/bugs using stack traces, logs, monitoring tools, and other resources
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Performs code/script reviews of senior engineers in the team
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Mentor and groom technical talent within the team
What Youll Bring
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At least 5+ relevant hands-on experiencein deploying andproductionizing MLmodelsat scale
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Experience in scaling GenAI or similar applications to accommodate a high number of users, large data size, and reduce response time.
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Strong knowledge in developing RAG-based pipelines using frameworks like LangChain LlamaIndex
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Experience in creating GenAI applications such as answering engines, extraction components, and content authoring.
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Expertise inDesigning, configuring, and using ML Engineering platformslikeSagemaker,MLFlow,Kubeflow, or other platforms
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Big data - Hive, Spark, Hadoop, queuing system like Apache Kafka/Rabbit MQ/AWS Kinesis
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Ability to quickly adapt to new technology and be innovative in creating solutions
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Ability to independently run POCs on new technologies and document findings to share
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Strong in at least one of the Programming languages - PySpark, Python or Java, Scala, etc. and Programming basics - Data Structures
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Hands-on experience in building metadata-driven, reusable design patterns for data pipeline, orchestration, ingestion patterns (batch, real-time)
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Experience in designing and implementation of solution on distributed computing and cloud services platform (but not limited to) - AWS, Azure, GCP
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Hands-on experience building CI/CD pipelines and awareness of practices for application monitoring
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Fluency in English
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Client-first mentality
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Intense work ethic
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Collaborative spirit and problem-solving approach
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