Description

• Agentic AI / LLM orchestration with hands-on LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or Amazon Bedrock Agents
• Generative AI / LLM application development, with minimum 2 years developing LLM/GenAI applications
• RAG including embeddings, indexing strategies, vector retrieval, and retrieval-quality optimization
• Python 3.11+, including type annotations, automated testing, asynchronous programming, APIs/FastAPI
• LLM Evaluation, including golden datasets, metrics, non-deterministic output evaluation, and LLM-as-judge/evaluation frameworks
• Enterprise production AI deployment & post-production support including incident response, regression management, latency/cost management, and live production changes
• AI Observability & Cloud Services including tracing, structured logging, quality/cost monitoring, REST APIs/data pipelines/event-driven services
• Enterprise Security & Compliance, including security/compliance reviews, access control, data handling, and auditability

Required Qualifications:
• Minimum 5 years of professional software engineering experience.
• Minimum 2 years of experience developing LLM/Generative AI applications.
• Demonstrated experience deploying at least one enterprise-grade AI application into production and supporting it after launch.

Enterprise Production AI Experience Must Include
• AI solution serving real users or business processes, rather than only a proof of concept or internal pilot.
• Accountability for non-functional requirements such as:
o Availability
o Latency
o Cost management
o Access control
o Data handling
o Auditability
• Experience completing security and compliance reviews within a regulated or enterprise environment.
• Post-production ownership involving:
o Incident response
o Regression management
o Changes deployed against live traffic
 

Technical Requirements:
• Hands-on experience with at least one LLM agent/orchestration framework, such as:
o LangGraph
o LangChain
o LlamaIndex
o Semantic Kernel
o AutoGen
o Amazon Bedrock Agents
• Strong experience with LLM evaluation methodologies, including:
o Dataset/golden dataset curation
o Metric selection
o Evaluation of non-deterministic outputs
o Appropriate use of LLM-as-judge techniques
o Established evaluation frameworks or custom evaluation harnesses
• Strong proficiency in Python 3.11+, including:
o Type annotations
o Automated testing
o Asynchronous programming
• Experience building and operating cloud-based services such as:
o REST APIs
o Data pipelines
o Event-driven workers
• Experience working within codebases governed by automated quality gates and formal code reviews.

Technical Environment:
AI / GenAI
• LLMs
• LLM orchestration frameworks
• RAG
• Agentic AI
• LLM evaluation and observability tooling
Programming / Application Development
• Python 3.11+
• FastAPI
• Pydantic
Data / Database
• PostgreSQL with vector extensions
• Aurora PostgreSQL
Cloud / AI Platform
• AgentCore
Infrastructure / DevOps
• Kubernetes
• Helm
• Terraform
• CI/CD
Event-Driven Architecture
• Apache Kafka
Observability
• Datadog
• OpenTelemetry

Key Skills

Education

Bachelor’s degree