AI Engineer

Responsibilities:

Designs, develops, deploys, and maintains AI-powered applications using Python/FastAPI backends, React/TypeScript frontends, and Google Cloud Vertex AI, while adhering to development best practices and quality standards. Takes full end-to-end ownership of assigned GenAI systems, including architectural decisions, feature delivery, operational readiness, production support, and cost optimization.

Leads by example in adopting AI-augmented development workflows, demonstrating effective collaboration with AI coding assistants (Claude Code, GitHub Copilot) to accelerate delivery and improve code quality.

Works closely with solution engineers, architects, and product teams to develop technical design specifications and validate that proposed solutions align with architectural standards, non-functional requirements, and business needs.

Implements LLM orchestration patterns including prompt engineering, retrieval-augmented generation (RAG), multi-agent workflows, and structured output generation.

Builds and maintains observability pipelines for AI systems platforms, including trace analysis, token usage tracking, cost monitoring, and quality metrics.

Provides technical guidance and mentorship across the team, upskilling members on agentic development practices, prompt engineering techniques, and AI tool integration.

Performs cross-platform analysis and trade-off assessments across backend (Python/FastAPI), frontend (React/TypeScript), and AI services (GCP Vertex AI, LangFuse) to ensure consistency and identify optimization opportunities.

Responsible for troubleshooting and issue analysis, as well as coding, testing, and implementing software enhancements across the full stack. Ensures timely delivery of AI initiatives, proactively communicating risks, blockers, and schedule impacts, and driving resolution to maintain delivery commitments.

Demonstrates exceptional prioritization and multitasking skills to manage concurrent deliverables across services and platforms, handle operational requests, and resolve production issues effectively.

Uses observability and monitoring tools (e.g., LangFuse, Grafana, Dynatrace, Splunk) to actively review dashboards, investigate anomalies, monitor AI model performance, and track cost metrics.

Available to support/unblock planned deployments during off hours as needed. We are excited about you if you have the following experience: AI Coding Assistants (e.g., Claude Code, GitHub Copilot, Cursor, Windsurf) Generative AI Application Development/Generative AI Design Patterns GenAI cloud platforms (Google Cloud Vertex AI, Gemini, Anthropic Claude, OpenAI) LLM Observability and Tracing (LangFuse, LangSmith, or similar)

Prompt Engineering techniques (e.g., chain-of-thought, few-shot learning, structured outputs) Python, FastAPI, async programming, Pydantic, SQLAlchemy ORM React, TypeScript, Zustand state management, Material-UI (MUI) Data visualization libraries (Recharts, D3.js) MySQL/PostgreSQL databases, query optimization, Alembic migrations Git, CI/CD, GitHub Actions, ArgoCD Docker and docker-compose Google Cloud Platform (Vertex AI, Cloud Run, BigQuery, Cloud Storage, IAM) Authentication and Authorization (OAuth 2.0, Okta, JWT, RBAC) Kubernetes and container orchestration

Additional Qualifications:

Bachelor's or master's degree in Computer Science, AI/ML, Information Technology, or equivalent experience

5+ years of software engineering experience with at least 2+ years focused on AI/ML or GenAI applications

Proven track record of delivering production AI systems with end-to-end ownership Expert-level proficiency with AI coding assistants and agentic development workflows Strong full-stack development skills across Python and TypeScript/React ecosystems

Good communication skills (verbal and written) with ability to articulate complex AI concepts to diverse audiences

Strong delegation skills with the ability to receive, prioritize, and execute on delegated tasks autonomously Proven ability to context-switch between different codebases, tech stacks, and AI platforms efficiently Leadership mindset with experience mentoring engineers and driving technical decisions Being able to seamlessly integrate new AI technologies and agentic workflows into current work streams Intermediate to advanced knowledge of microservice design patterns and trade-offs Understanding of responsible AI practices including bias, safety, and ethical considerations Experienced on Agile methodology and tools (Jira, Confluence) Work experience in the Retail industry is a plus