AI Engineer

Astir IT Solutions, Inc.; Dallas (TX); Sur place; USD 150 000 - 190 000; Plein temps

Résumé du poste

Astir IT Solutions, Inc. is seeking an AI Engineer with hands-on experience in prompt engineering, LLM response optimization, and AI application development. You will design prompts, improve model outputs, and build context-aware AI solutions for enterprise systems.

The role requires implementing MCP tools, RAG workflows, and responsible AI practices, with emphasis on security, governance, and reliable deployment across healthcare or regulated environments.

Qualifications

• 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.
• 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.

Responsabilités

• 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.

Connaissances

LLM knowledge

Prompt engineering

AI app development

RAG workflows

Knowledge retrieval

NLP concepts

HIPAA data privacy

Description du poste

Location

( Dallas, TX / Chicago, IL/ New York) W2 Only

Exp

8+ Years

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.
• 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.