Applied AI Senior Engineer

My name is Himanshu and I serve as the Recruitment Manager at Nexiva INC. I am reaching out to share an excellent career opportunity for the role of Applied AI Senior Engineer with our esteemed client. If you are interested then please share your updated resume at

Job Description

Title: Applied AI Senior Engineer
Location: Austin TX/Sunnyvale CA (Onsite) Relocation Works
Duration: 12 Months


Job Description

Must-Have Requirements

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/GCP/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 / Differentiators

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

Screening Questions for Candidates

1. Describe a production AI agent or skill system you built. What broke and how did you fix it

2. Have you built MCP servers/integrations or custom tool-use systems for LLMs

3. How do you evaluate whether an LLM-based feature is working well What makes this hard

4. Walk me through how youd deploy and scale an AI service on Kubernetes.

Not a Fit If

Primarily a model trainer/fine-tuner (were not training models)

AI experience is mainly academic research or tutorial-based

No production systems experience (only notebooks/demos)

Looking for entry-level role with heavy mentorship

Background is primarily data science/analytics rather than engineering

Architects who dont write or deploy code themselves