Artificial Intelligence Engineer

We are seeking a hands-on AI Automation and Agent Implementation Consultant who will identify, design, and implement AI-enabled solutions that improve business processes and operational efficiency. The ideal candidate has practical experience using tools such as Cursor, GitHub Copilot, Microsoft Copilot, AI agents, and LLM-based platforms to transform manual workflows into production-ready, AI-supported solutions. This is an implementation-focused role for a builder who can rapidly prototype, implement, test, deploy, and operationalize AI solutions that deliver measurable business outcomes. It is not a strategy-only or advisory role.CANDIDATE PROFILEBachelor's degree in an engineering or computer science discipline and/or equivalent experience/certification.7+ years of experience in information technology, including experience in cloud technologies (AWS, AliCloud, Azure), OS scripting, and automation using Python, Bash, or similar languages.Demonstrated hands-on experience identifying, designing, implementing, testing, and deploying AI-enabled automation or agent-based solutions that improve business processes and operational efficiency.Practical experience using AI development and productivity tools such as Cursor, GitHub Copilot, Microsoft Copilot, AI agents, and LLM-based platforms to convert manual workflows into production-ready, AI-supported solutions.Proven ability to move AI solutions beyond experimentation into production, with measurable outcomes such as efficiency gains, cycle-time reduction, quality improvement, risk reduction, or cost savings.Experience rapidly prototyping and iterating AI solutions, integrating them with enterprise workflows, APIs, data sources, and automation platforms, and supporting production deployment and operational readiness.PreferredExcellent problem-solving skills, with the ability to work independently and lead outcomes across cross-functional teams.Excellent verbal and written communication skills for executives, business stakeholders, and IT teams.Experience with services such as Amazon Bedrock, SageMaker, Azure OpenAI, Azure AI Foundry, Google Vertex AI, OpenAI APIs, Anthropic, Databricks, Snowflake AI, or similar platforms.Experience with AI agent frameworks, retrieval-augmented generation, prompt and context engineering, model evaluation, observability, responsible AI controls, secure deployment patterns, and human-in-the-loop workflows.Experience defining and tracking implementation success measures, documenting reusable solution patterns, and transitioning AI-enabled capabilities to operations and support teams.Experience supporting generative AI or large language model workloads, including token-based pricing models and inference cost optimization.Experience in developing and implementing FinOps/Cloud Cost Optimization strategiesCORE WORK ACTIVITIESDesigns and implements AI-enabled FinOps capabilities for cloud cost visibility, forecasting, anomaly detection, optimization recommendations, and executive reporting.Identifies high-value manual workflows and translates business requirements into implementable AI automation and agent use cases with defined outcomes and success measures.Rapidly prototypes, implements, tests, deploys, and iterates production-ready AI-supported solutions using Cursor, GitHub Copilot, Microsoft Copilot, AI agents, LLM-based platforms, APIs, and automation tools.Builds practical AI agents and workflow automations that integrate with enterprise systems, data sources, and operational processes while meeting security, privacy, architecture, and responsible AI requirements.Establishes evaluation, monitoring, observability, fallback, human-oversight, and support mechanisms to improve the reliability and maintainability of deployed AI solutions.Measures and communicates business outcomes from implemented solutions, including productivity, cycle time, quality, cost, adoption, and operational efficiency improvements.Establishes FinOps governance for AI/ML and GenAI workloads, including cost allocation, usage tracking, forecasting, budgeting, and optimization.Develop and maintain cost transparency for AI and machine learning workloads, including compute, storage, networking, model training, inference, and third-party AI platform usage.Create and manage reporting, dashboards, and KPIs to track AI-related spend, utilization, efficiency, and business value across teams and use cases.· Partner with architecture, engineering, platform, and data science teams to identify opportunities to improve cost, performance, and utilization of AI infrastructure and services.Support the design and implementation of showback and chargeback models for AI-related services to improve accountability and decision-making.Analyzes cloud usage patterns, resource utilization, and spending to identify areas for improvement.Analyzes and implements cost optimization actions across cloud services.Implements and manages automated tools to identify cost trends and anomalies.Collaborates with finance and business teams to align cloud spending with budget constraints.Trains and mentors team members and peers, as appropriate.