Artificial Intelligence Engineer
GTN Technical Staffing · ·
About the Role
Hybrid Schedule-Monday, Wednesday, Friday Must be Greencard or US Citizen Excellent client with great benefits! About the Opportunity Our client is seeking an AI Engineer to join its growing technology organization and help develop practical, business-focused artificial intelligence solutions. This is an excellent opportunity for a mid-level AI professional who has hands-on experience developing AI, machine learning, and/or Generative AI applications and wants to take the next step in their career. The successful candidate will work alongside experienced technology and business professionals to design, develop, test, and deploy AI solutions that improve business processes, enhance decision-making, and create new capabilities across the organization. This is a hands-on development role for someone who enjoys building technology—not simply researching or evaluating it. Key Responsibilities Design, develop, test, and deploy AI and Generative AI applications . Work with business and technology teams to identify opportunities where AI can deliver measurable value. Develop solutions utilizing Large Language Models (LLMs), RAG, APIs, embeddings, and AI agents . Integrate AI capabilities with existing enterprise applications, databases, and data sources. Develop and maintain data pipelines required to support AI applications. Work with cloud-based AI platforms and services. Evaluate different AI models and technologies based on performance, cost, security, and business requirements. Develop and refine prompts and workflows to improve AI application performance. Assist with deploying AI solutions into production environments. Monitor AI applications and help troubleshoot performance, accuracy, and reliability issues. Participate in testing, model evaluation, and continuous improvement of AI solutions. Follow established security, data privacy, and AI governance practices. Research emerging AI technologies and make recommendations for potential applications. Collaborate with software engineers, data professionals, IT leadership, and business stakeholders. Document solutions, processes, and technical designs. Contribute to the development of the organization's broader AI capabilities and best practices. Qualifications Required Bachelor's degree in computer science, Engineering, Data Science, or related technical field, or equivalent experience. 2–4 years of professional experience in software engineering, machine learning, data engineering, AI development, or a related field. Hands-on experience developing AI/ML or Generative AI applications . Strong programming skills in Python . Understanding of software development, APIs, databases, and application architecture. Experience working with LLMs or Generative AI technologies . Experience working with cloud technologies. Ability to take a technical concept from prototype through implementation . Strong analytical and problem-solving skills. Excellent communication and collaboration skills. Preferred Experience RAG and retrieval-based AI applications Vector databases AI agents and agentic workflows Azure OpenAI, AWS Bedrock, or Google Vertex AI LangChain, LlamaIndex, or similar frameworks PyTorch, TensorFlow, or other ML frameworks Docker and CI/CD MLOps / LLMOps SQL and data engineering Enterprise system integrations AI security, governance, and responsible AI practices What We're Looking For The ideal candidate is a builder . You don't need to be a research scientist or have a Ph.D. in AI. We're looking for someone who understands modern AI technologies and has demonstrated the ability to actually build and deploy solutions . The strongest candidates will typically have: Strong software engineering foundation + hands-on AI/GenAI experience + business curiosity. We are particularly interested in candidates who have built projects or applications using LLMs and can explain: What problem they were solving Why they selected a particular model or architecture How they handled the data How they evaluated the results How they moved the solution toward production What they would do differently the second time Show more Show less
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