Back
Tier-1 employer, metro locations, generic 'Software Engineer' title and mid-level appeal increase applicant density.
Highly specialized GenAI and SageMaker agent engineering reduces cross-industry transferability.
Role mandates specific GenAI stack and hands-on AWS, LangChain, monitoring, and DevOps skills.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Engineer and maintain high performance, secure, customer-centric software solutions within a feature team.
Own full software development life cycle including design, coding, testing, deployment, maintenance, and decommissioning.
Collaborate with engineers, architects, and business analysts to optimize software engineering capabilities and rapidly deliver critical software that adds business value.
Experience with AWS AI services including SageMaker Unified Studio (SMUS), Bedrock, and AI solution deployment.
Proficiency in Python and/or Java, with experience in AI frameworks such as Spring AI, LangChain, and LangGraph.
Knowledge of GenAI concepts including Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and model tool integration and optimization.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building, testing, monitoring, and optimizing AI agents using telemetry and observability tools like Splunk and Grafana.
Familiar with DevOps practices, CI/CD pipelines, and Agile methodologies for efficient AI solution development and deployment.
Capable of working in cross-functional teams responsible for end-to-end software lifecycle and engaging with a broad network of stakeholders across technical and business functions.