Generative AI Engineer

TRUSYS, Inc.; Dallas (TX); On-site; USD 120,000 - 170,000; Full time

Job summary

TRUSYS, Inc. is seeking an AI Engineer with hands-on expertise in prompt engineering, LLM application development, and agentic AI to design context-aware solutions across enterprise systems.

The role requires strong software skills, practical understanding of LLM behavior, and secure handling of healthcare data (HIPAA-aligned). You will implement MCP integrations, RAG workflows, and robust evaluation tooling while collaborating with cross-functional teams.

Qualifications

• Hands-on experience building AI applications using LLMs.
• Strong prompt engineering skills including system instructions and few-shot prompting.
• Proficiency in Python, JavaScript/TypeScript, or Java with API/backend experience.
• Experience with RAG, embeddings, vector databases, and enterprise knowledge retrieval.
• Experience designing agent workflows, tool-calling integrations, and context management.
• Ability to evaluate and improve AI responses for accuracy and adherence to instructions.
• Understanding of hallucination mitigation and guardrails in responsible AI.
• Knowledge of HIPAA-aligned PHI/PII practices and secure healthcare-data handling.
• Strong debugging, problem-solving, and communication skills.

Responsibilities

• Prompt engineering and optimization to improve response quality, relevance, and instruction following.
• Agentic AI development with planning, tool use, memory, and multi-step execution.
• Develop MCP servers and integration with enterprise APIs and data sources.
• Design tool-calling workflows, retrieval mechanisms, and session memory management.
• Implement RAG workflows with document processing, embeddings and semantic search.
• Create evaluation datasets and automated tests for reliability and accuracy.
• Implement safeguards against prompt injection, data exposure, hallucinations, and unauthorized tool usage.
• Deploy, monitor, and optimize AI applications balancing quality, latency, and cost.
• Collaborate with product managers and engineers to translate requirements into AI solutions.
• Contribute to architecture reviews and engineering standards.

Skills

Prompt engineering

LLM application development

Programming: Python/JS/TS/Java

API integration

Debugging & problem solving

HIPAA data handling

Education

Bachelor's degree in CS/Engineering

Tools

MCP servers

Vector databases

Embeddings

Semantic search

Job description

Other Locations – New York / New Jersey, Austin,San Francisco

About the Role

We are looking for an AI Engineer with strong hands-on experience in prompt engineering, LLM application development, and agentic AI. You will design and build reliable, context-aware AI solutions that understand user intent, retrieve relevant information, and execute tasks securely across enterprise systems.

The ideal candidate combines strong software engineering skills with a practical understanding of LLM behavior, evaluation, and sensitive-data handling, particularly in healthcare environments.

Key Responsibilities

• Prompt engineering and optimization: Design, test, and refine system prompts, prompt templates, and few-shot examples to improve response accuracy, relevance, consistency, and instruction following.
• Agentic AI development: Build AI agents that support planning, tool use, memory, and multi-step task execution, with appropriate controls for failures and human intervention.
• MCP integration: Develop and integrate Model Context Protocol (MCP) servers and tools to enable secure interaction with enterprise APIs, applications, and data sources.
• Agent architecture: Design tool-calling workflows, retrieval mechanisms, session memory, and context management strategies.
• RAG and knowledge grounding: Implement retrieval-augmented generation workflows, including document processing, embeddings, semantic search, and grounded responses.
• Evaluation and testing: Create evaluation datasets and automated tests to measure response quality, retrieval accuracy, task completion, tool selection, and agent reliability.
• Security and responsible AI: Implement safeguards against prompt injection, sensitive-data exposure, hallucinations, and unauthorized tool execution.
• Production operations: Deploy, monitor, troubleshoot, and improve AI applications, balancing quality, latency, reliability, and cost.
• Cross-functional collaboration: Work with product managers, architects, and engineering teams to translate business requirements into practical AI solutions.
• Technical documentation: Contribute to architecture reviews, design documentation, and engineering standards.

Required Skills and Experience

• Hands-on experience building AI applications using LLMs such as GPT, Claude, Gemini, Llama, or comparable models.
• Strong prompt engineering skills, including system instructions, structured outputs, few-shot prompting, and iterative response optimization.
• Proficiency in Python, JavaScript/TypeScript, or Java, with experience integrating APIs and backend services.
• Practical experience with RAG, embeddings, vector databases, semantic search, and enterprise knowledge retrieval.
• Experience designing agent workflows, tool-calling integrations, and context management.
• Ability to evaluate and improve AI responses for accuracy, relevance, completeness, tone, and adherence to instructions.
• Understanding of hallucination mitigation, prompt injection risks, guardrails, and responsible AI practices.
• Knowledge of secure healthcare-data handling, including HIPAA-aligned PHI/PII practices, data minimization, de-identification, masking, access controls, and audit logging.
• Strong debugging, problem-solving, and communication skills.

Preferred Qualifications

• Experience developing MCP servers and integrating MCP-enabled tools.
• Experience integrating AI applications with healthcare platforms, electronic health records, or other regulated enterprise systems.
• Experience deploying and operating AI applications on cloud platforms.
• Familiarity with LLM evaluation, observability, and automated regression testing tools.
• Experience implementing human review, approval workflows, and recovery mechanisms for AI agents.