Lead Applied AI Engineer

Innova Solutions•Tampa, FL

2d•Hybrid

About The Position

Innova Solutions is seeking a Lead Applied AI Engineer for a full-time contract (W2) position. This role involves building, delivering, and supporting AI-enabled solutions using advanced technologies like Large Language Models (LLMs), Generative AI, AI Agents, Retrieval-Augmented Generation (RAG), vector databases, and AI orchestration frameworks in enterprise environments. The position requires a strong understanding of Responsible AI principles, including governance, security, privacy, transparency, human oversight, and model evaluation. Candidates should demonstrate the ability to leverage AI tools to enhance productivity, accelerate delivery, improve decision-making, and automate tasks. A key aspect of the role is strong AI literacy, enabling the evaluation of AI-generated outputs, critical thinking, and identification of appropriate AI use cases. The role is based in Tampa, FL, and Nashville, TN, with a hybrid work model. The contract duration is 6+ months.

Requirements

Experience building, delivering, or supporting AI-enabled solutions using Large Language Models (LLMs), Generative AI, AI Agents, Retrieval-Augmented Generation (RAG), vector databases, AI orchestration frameworks, or related AI technologies in enterprise environments.
Understanding of Responsible AI principles, including AI governance, security, privacy, transparency, human oversight, and model evaluation best practices.
Demonstrated ability to effectively leverage AI tools and assistants to improve individual productivity, accelerate delivery, enhance decision-making, automate routine tasks, and improve quality across day-to-day work activities.
Strong AI literacy, including the ability to evaluate AI-generated outputs, apply critical thinking and human judgment, and identify appropriate use cases for AI-driven solutions.
Hands-on experience with Python and/or .NET technologies for developing, integrating, and operationalizing AI-enabled applications and services.
Continuous learner with a passion for staying current on emerging AI technologies, practices, and industry trends.
Experience with Python, .NET, cloud-native application development, APIs, microservices, and modern software engineering practices.
Experience with Azure OpenAI, Azure AI Foundry, Microsoft Copilot technologies, AI orchestration frameworks, vector databases, and enterprise AI platforms.
Experience developing, integrating, deploying, or operating AI-enabled applications in Azure cloud environments.
6+ years of full-stack software engineering experience with most of the following: Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, as well as unit and integration testing frameworks.
3+ years of experience building AI/ML and agentic applications, with hands-on GenAI experience across LLM integration (OpenAI, Anthropic, or open-source models), RAG pipelines, prompt engineering, vector databases, evaluations, and AI agent orchestration.
3+ years of experience with cloud-native engineering, using FaaS, PaaS, or micro-services on any of the cloud hyperscalers such as Azure, AWS, or GCP, including their AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI, plus application-level infrastructure-as-code and cost-aware engineering (FinOps accountability).
Prior experience in establishing engineering standards, including actively leading, mentoring, and guiding team members in the adoption and continuous improvement of these standards.
Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.

Responsibilities

Building, delivering, or supporting AI-enabled solutions using Large Language Models (LLMs), Generative AI, AI Agents, Retrieval-Augmented Generation (RAG), vector databases, AI orchestration frameworks, or related AI technologies in enterprise environments.
Leveraging AI tools and assistants to improve individual productivity, accelerate delivery, enhance decision-making, automate routine tasks, and improve quality across day-to-day work activities.
Evaluating AI-generated outputs, applying critical thinking and human judgment, and identifying appropriate use cases for AI-driven solutions.
Developing, integrating, and operationalizing AI-enabled applications and services.
Developing, integrating, deploying, or operating AI-enabled applications in Azure cloud environments.
Establishing engineering standards, including actively leading, mentoring, and guiding team members in the adoption and continuous improvement of these standards.
Handling diverse situations, complex projects, and changing priorities with passion, empathy, and care.