What are the responsibilities and job description for the Applied AI Senior Engineer position at Jobs via Dice?
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Requirement Details
Backend/Systems Experience
3 years building production backend or distributed systems (pre-AI experience required)
Production AI Systems
Has shipped AI/LLM features serving real users at scale not just prototypes or demos
Agentic Systems
Has built AI agents, skills, tools, or MCP (Model Context Protocol) integrations
Python
Proficient for backend development
Secondary Language
Working knowledge of Go, TypeScript, or Rust
Cloud Infrastructure
Deep experience with AWS/Google Cloud Platform/Azure cost optimization, compute decisions, not just deployment
Container & Orchestration
Hands-on with Docker and Kubernetes can build, deploy, debug, and scale services themselves
LLM Integration
Understands token economics, context limits, rate limiting, structured outputs, API failure modes
LLM Evaluation
Understands how to evaluate LLM outputs and the inherent challenges (non-determinism, quality measurement, regression detection)
Hands-On Engineer
Not just an architect writes code, debugs production issues, deploys their own work
Preferred Skills:
Requirement Details
Backend/Systems Experience
3 years building production backend or distributed systems (pre-AI experience required)
Production AI Systems
Has shipped AI/LLM features serving real users at scale not just prototypes or demos
Agentic Systems
Has built AI agents, skills, tools, or MCP (Model Context Protocol) integrations
Python
Proficient for backend development
Secondary Language
Working knowledge of Go, TypeScript, or Rust
Cloud Infrastructure
Deep experience with AWS/Google Cloud Platform/Azure cost optimization, compute decisions, not just deployment
Container & Orchestration
Hands-on with Docker and Kubernetes can build, deploy, debug, and scale services themselves
LLM Integration
Understands token economics, context limits, rate limiting, structured outputs, API failure modes
LLM Evaluation
Understands how to evaluate LLM outputs and the inherent challenges (non-determinism, quality measurement, regression detection)
Hands-On Engineer
Not just an architect writes code, debugs production issues, deploys their own work
Preferred Skills:
- Built multi-step agentic workflows with tool use and function calling
- Experience with agent orchestration frameworks (LangGraph, CrewAI, Claude Agent SDK, Google ADK, OpenAI ADK)
- Built guardrails, fallbacks, or graceful degradation for AI systems
- Streaming inference and async agent orchestration
- Cost/latency optimization: caching, batching, prompt compression
- ML observability tools: Langfuse, Arize, Braintrust, W&B
- Retrieval systems (vector search, hybrid search) as a tool, not the focus