Senior Full Stack AI Engineer

AI & Cloud Platforms: Hands-on experience with AWS Bedrock; familiarity with LLM architectures and foundational models.
RAG & Prompt Engineering: Proficiency implementing retrieval-augmented generation (RAG) systems and crafting high-quality prompts for LLM interactions.
AI Orchestration / Agentic Workflows: Proven experience designing and implementing agentic workflows and multi-agent systems using modern orchestration frameworks (e.g., LangGraph, LlamaIndex), including tool calling, workflow routing, and structured task decomposition.
LLMOps / Evaluation & Observability: Strong experience across the LLMOps lifecycle, including automated prompt/model evaluation and benchmarking (e.g., promptfoo), and production observability, tracing, and debugging
AI Governance / Guardrails & Security: Demonstrated ability to implement AI guardrails and safety controls to mitigate prompt injection, prevent sensitive data leakage, enforce policy-based outputs, and ensure secure AI integrations in production environments.
Web Technologies: Strong skills in full-stack development using modern frameworks (e.g., JavaScript/TypeScript – React, Angular, Node.js), RESTful APIs, HTML5, and CSS3.
Backend Systems: Experience with Java, Spring Boot, Python, for building backend services.
Cloud & DevOps: Familiarity with serverless and containerized deployments (e.g., AWS Lambda, ECS/Fargate), CI/CD pipelines (GitHub Actions, Maven, Gradle, Bamboo), and infrastructure as code.
Data & APIs: Comfortable integrating AI models with data sources, microservices, and external APIs; experience with JSON, HTTP, and API authentication patterns.
Databases: Hands-on development experience using RDBMS/SQL databases (e.g., MySQL, PostgreSQL, Oracle) and NoSQL databases (e.g., MongoDB, DynamoDB), including schema design, query optimization, and integration with web applications and AI-driven services.
Testing & Quality: Experience writing automated tests (unit, integration), performance profiling, and production monitoring.
AI-Assisted Development Tools: Hands-on experience using AI development assistants (e.g., GitHub Copilot, Amazon Q Developer, AWS Kiro, Cursor, JetBrains AI, VS Code AI extensions) to accelerate development, improve code quality, and support productivity in day-to-day engineering workflows.
Agile / Product Delivery: Demonstrated experience delivering software in an Agile Scrum / product delivery environment, including story estimation, sprint execution, backlog refinement, collaboration with Product Owners, and continuous improvement practices.

Work arrangement
Yes