Senior AI Engineer

We are seeking a hands-on AI Automation & Agent Implementation Consultant to design, build, deploy, and operationalize AI-driven solutions that automate business processes and improve operational efficiency. This is a builder/implementation role, not a strategy-only or advisory position. The successful candidate will work directly with AI development tools, LLM platforms, AI agents, APIs, automation frameworks, and enterprise systems to turn manual workflows into production-ready AI capabilities. The ideal candidate has practical experience with tools such as Cursor, GitHub Copilot, Microsoft Copilot, AI agents, LLMs, and generative AI platforms, with a proven ability to move solutions from prototype through testing, deployment, monitoring, and production support. FinOps and cloud cost optimization experience is a plus but is not required. Key Responsibilities Identify high-value manual processes and translate business requirements into practical AI automation and agent-based solutions. Rapidly prototype, develop, test, deploy, and iterate AI-enabled solutions using Cursor, GitHub Copilot, Microsoft Copilot, LLMs, AI agents, APIs, and automation platforms. Build AI agents and intelligent workflows that integrate with enterprise applications, APIs, data sources, and operational processes. Move AI solutions beyond proof-of-concept into production environments with appropriate security, privacy, architecture, governance, and responsible AI controls. Implement evaluation, monitoring, observability, fallback, human-in-the-loop, and operational support mechanisms for deployed AI solutions. Measure business impact through metrics such as productivity improvement, cycle-time reduction, quality, adoption, cost savings, and operational efficiency. Collaborate with architecture, engineering, platform, data, finance, and business teams to identify and implement AI automation opportunities. Document reusable AI solution patterns, implementation approaches, and operational procedures. Support knowledge transfer and mentor team members on AI development tools, agent implementation, and automation practices. FinOps / Cloud Cost Optimization Preferred Support AI/ML and GenAI FinOps and cloud cost optimization initiatives. Analyze cloud usage, resource utilization, and spending to identify optimization opportunities. Develop cost visibility, forecasting, anomaly detection, dashboards, and reporting for AI/ML workloads. Track costs associated with compute, storage, networking, model training, model inference, and third-party AI platforms. Support cost allocation, showback/chargeback, budgeting, forecasting, and governance for AI workloads. Identify and implement cloud cost optimization opportunities across AWS, Azure, and other cloud environments. Work with Finance and business stakeholders to align cloud spending with budgets and business priorities. Required Qualifications, AWS SageMaker, Azure OpenAI Azure AI Foundry Google Vertex AI OpenAI APIs Anthropic Databricks Snowflake AI AI Agent Frameworks Generative AI / LLM Applications Retrieval-Augmented Generation (RAG) Prompt Engineering / Context Engineering Model Evaluation AI Observability Responsible AI Human-in-the-Loop Workflows Secure AI Deployment API Integration Workflow Automation Cloud Cost Optimization / FinOps Additional Preferred Experience Generative AI and LLM workload implementation Token-based pricing and LLM inference cost optimization AI/ML infrastructure cost management Cloud cost anomaly detection and forecasting FinOps governance and cost allocation Showback / Chargeback AI/ML utilization and performance optimization Work Arrangement Remote W2 This position requires quarterly travel for PI Planning and related team activities. Candidates should be comfortable traveling approximately once per quarter when required. What We’re Looking For We are looking for someone who builds, not someone who only recommends what should be built. The strongest candidates will be able to demonstrate examples where they personally used AI tools, LLMs, agents, APIs, and automation technologies to solve a real business problem, deployed the solution into a production environment, and measured the resulting business impact.

Bachelor’s degree in Computer Science, Engineering, Information Technology, or equivalent experience/certification. 7+ years of IT experience, including cloud technologies, scripting, automation, and enterprise technology environments. Hands-on experience designing, implementing, testing, and deploying AI automation, AI agents, Generative AI, or LLM-based solutions. Practical experience using Cursor, GitHub Copilot, Microsoft Copilot, or comparable AI-assisted development/productivity tools. Strong experience converting manual or inefficient workflows into AI-enabled automated processes. Demonstrated ability to take AI solutions from rapid prototype to production deployment. Experience integrating AI solutions with enterprise APIs, applications, databases, data sources, and automation platforms. Programming/scripting experience with Python, Bash, or similar languages. Experience working with one or more major cloud platforms such as AWS, Azure, or AliCloud. Strong understanding of AI/LLM implementation, production readiness, testing, monitoring, and operational support. Ability to work independently and collaborate effectively with engineering, architecture, data, business, and executive stakeholders. Preferred Technical Skills Experience with one or more of the following is highly desirable, Description Do you have experience leading people and an interest in shaping U.S. National Leadership Command Capability (NLCC) communications technology solutions? Are you pas…

Vantor

• McLean, VA
• $137,000-200,200 per year Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what’s happening now and shape what’s coming next. Vantor is a place for …