What You’ll Do 

• Design and implement technical features leveraging best practices for technology stack being used 
• Collaborate with client-facing teams to understand solution context and contribute to technical requirement gathering and analysis 
• Work with technical architects on the team to validate design and implementation approach 
• Write production-ready code that is easily testable, understood by other developers, and accounts for edge cases and errors 
• Ensure the highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews 

• Write unit tests as well as higher-level tests to handle expected edge cases and errors gracefully, as well as happy paths 
• Uses bug tracking, code review, version control, and other tools to organize and deliver work 
• Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues, and dependencies 
• Consistently contribute in researching & evaluating the latest technologies through rapid learning, conducting proofs-of-concept and creating prototype solutions 
• Support the project architect in designing modules/component of the overall project/product architecture 
• Breaks down large features into estimable tasks lead estimation and can defend them with clients 
• Implement complex features with limited guidance from the engineering lead. For example service or application-wide change 
• Systematically debug code issues/bugs using stack traces, logs, monitoring tools, and other resources 
• Performs code/script reviews of senior engineers in the team 
• Mentor and groom technical talent within the team 

What You’ll Bring 

• At least 5+ relevant hands-on experience in deploying and productionizing ML models at scale  
• Experience in scaling GenAI or similar applications to accommodate a high number of users, large data size, and reduce response time.
• Strong knowledge in developing RAG-based pipelines using frameworks like LangChain & LlamaIndex
• Experience in creating GenAI applications such as answering engines, extraction components, and content authoring.
• Expertise in Designing, configuring, and using ML Engineering platforms like Sagemaker, MLFlow, Kubeflow, or other platforms  
• Big data - Hive, Spark, Hadoop, queuing system like Apache Kafka/Rabbit MQ/AWS Kinesis 
• Ability to quickly adapt to new technology and be innovative in creating solutions 
• Ability to independently run POCs on new technologies and document findings to share 
• Strong in at least one of the Programming languages - PySpark, Python or Java, Scala, etc. and Programming basics - Data Structures 
• Hands-on experience in building metadata-driven, reusable design patterns for data pipeline, orchestration, ingestion patterns (batch, real-time) 
• Experience in designing and implementation of solution on distributed computing and cloud services platform (but not limited to) - AWS, Azure, GCP 

• Hands-on experience building CI/CD pipelines and awareness of practices for application monitoring