Job Description

AI Full stack Engineer: ( In Short ) AI + Backend Engineering will be 70% of the daily task - AI (LLM modeling, Data Preparation, AWS Model configuration, etc..) + Python for writing APIs Frontend Engineering will be 20% of the daily task for creating user interfaces for AI/backend output - Angular / React + HTML with CSS AWS side the candidate will be doing 10% of the daily task for managing or troubleshooting AWS infrastructure and should be able to contribute to setting up CI/CD pipelines. Knowledge of Terraform will be a plus, but if it's not there then Hurix to train the resource within a month's time. About JD: Minimum 5 years of relevant(full stack) experience in product or enterprise software development. Strong hands-on experience on the full software stack, including Frontend, Backend / API, Databases, and Infrastructure Proven technical abilities in software development, testing, monitoring, performance tuning, and deployment pipelines Strong technical communication and collaboration skills Strong knowledge of generative AI, with hands-on experience in applying LLM frameworks such as LangChain and others. Practical knowledge of Retrieval-Augmented Generation (RAG) techniques, including implementing and optimizing RAG models to enhance information retrieval and generation capabilities. Passion for technology and commitment to staying current on AI advancements Focus on application quality and testing practices, such as unit integration, e2e testing, code coverage analysis, and automated software inspection. Strong sense of customer empathy and a commitment to delighting customers Ability to work across functional and domain boundaries to deliver software on time and with quality Technology Stack: Back-end: Python, Go, Node.js Front-end: TypeScript, Angular/React (any) AI services such as Azure AI Search, Azure OpenAI service , Amazon Bedrock , or similar technologies. Amazon Web Services (AWS): ECS, RDS , Lambda , and many other services CI/CD and Infrastructure: Docker , Terraform , Github , CircleCI, Sonarqube, NewRelic and Datadog Collaboration: JIRA, Confluence , Zoom & Slack Databases: Relational databases such as MySQL and PostgreSQL ; NoSQL databases such as DynamoDB