aimlroles

AI Engineer | Onsite

Fa Ertb Saasfaprod1 Open · verified Oct 22, 2026
United States Mid-level AI Engineering

At a glance

Mid-level AI Engineering role at Fa Ertb Saasfaprod1. United States · no salary stated · we estimate $132,450–$205,151 across the United States.

Estimated pay

Fa Ertb Saasfaprod1 has not published a salary for this role. The figure below is our estimate, not theirs.

$132,450 – $205,151
What the middle half of mid-level AI engineering roles in the United States paid, among postings that did state a salary.
Worked out from 376 postings that stated pay, median $165,000. Last updated Oct 22, 2026.
RoleAI Engineering
SeniorityMid-level
LocationUnited States
PostedSep 18, 2026 · 4w ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Design and refine LLM prompts, then test how well they improve accuracy, reliability and business outcomes
  • Build AI agents with planning, memory, reasoning and multi-step task execution, connecting them to enterprise tools and data
  • Create agent architectures, retrieval methods and context handling, including RAG and knowledge grounding
  • Work with product, architecture and engineering teams to turn business needs into AI solutions
  • Monitor deployed agents, investigate issues and improve workflows while supporting security and responsible AI practices

What you bring

  • Hands-on work with LLMs and strong prompt design skills, including templates, system prompts and response tuning
  • Experience building AI applications with LLM APIs, RAG workflows, enterprise data and backend services
  • Programming experience in Python, Java, JavaScript or a comparable language, plus knowledge of embeddings and vector search
  • Ability to assess AI responses and address hallucinations, prompt injection, guardrails and responsible-use concerns
  • Knowledge of healthcare privacy practices for sensitive data, including HIPAA-aligned handling of PHI and PII

Who this fits

This mid-level, hands-on engineering role suits someone who builds and improves LLM applications and AI agents. Healthcare or regulated-system integration, MCP, cloud platforms and production deployment are preferred experience.

From the employer

Role Summary:

AI Engineer with strong hands-on experience in prompt engineering, LLM response optimization, and AI application development. The ideal candidate can design prompts, improve model output quality, help LLMs understand user input, and build reliable, context-aware AI solutions.

Key Responsibilities

  • Design, develop, and optimize prompts for Large Language Models (LLMs) to improve accuracy, reliability, and business outcomes.
  • Build and configure Agentic AI solutions that leverage planning, reasoning, memory, and multi-step task execution capabilities.
  • Develop and integrate MCP (Model Context Protocol) tools, enabling AIagents to securely discover and interact with enterprise systems, APIs, and data sources.
  • Design agent architectures, tool-calling frameworks, retrieval mechanisms, and context management strategies.
  • Collaborate with product managers, architects, and engineering teams to translate business requirements into AI-driven solutions.
  • Implement Retrieval-Augmented Generation (RAG), knowledge grounding, and context orchestration patterns.
  • Define evaluation frameworks and prompt testing methodologies to measure agent performance, quality, and reliability.
  • Ensure AI solutions adhere to security, compliance, governance, and responsible AI standards.
  • Support deployment, monitoring, troubleshooting, and continuous improvement of AI agents and MCP-enabled workflows.
  • Contribute to architecture reviews, technical design documentation, and engineering best practices for AI platforms.

Required Skill

  • Hands-on experience with LLMs such as GPT, Claude, Gemini, Llama, or similar models.
  • Strong prompt engineering skills, including prompt templates, system prompts, few-shot prompting, and response tuning.
  • Develop AI-powered applications using LLM APIs, RAG workflows, enterprise data sources, and backend services.
  • Experience with LLM APIs and application integration using Python, Java, JavaScript, or similar programming languages.
  • Knowledge of RAG, embeddings, vector databases, semantic search, and knowledge retrieval workflows.
  • Ability to evaluate and improve AI responses for accuracy, relevance, completeness, tone, and instruction following.
  • Understanding of NLP concepts, hallucination mitigation, prompt injection risks, guardrails, and responsible AI practices.
  • Knowledge of healthcare data privacy and secure sensitive-data handling, including HIPAA-aligned PHI/PII practices, data minimization, de-identification, masking, access controls, auditability, and responsible use of healthcare data in AI workflows.
  • Experience with MCP servers, cloud platforms, APIs, backend services, and production deployment is preferred.
  • Experience integrating AI agents with healthcare, health records, or regulated industry systems.