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Mid-level AI role in metro with broad required skills yields moderate applicant competition.
Core AI engineering skills transfer across industries, but enterprise integration experience raises domain specificity.
Explicit years, domain-specific GenAI/cloud skills, and multiple mandatory tech stacks increase filtering.
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Develop production-grade code for AI-enabled backend microservices and API integrations, ensuring quality through peer reviews and technical debt resolution.
Integrate and advance AI-native features such as GenAI patterns, agentic workflows, and intelligent automation within enterprise solutions.
Engage in technical consulting with clients to design AI/cloud modernization solutions and support pre-sales technical assessments and proof-of-concepts.
5–8 years of hands-on software engineering experience, including 3+ years as a Senior AI engineer.
Proficiency in Python and/or .NET (C#, ASP.NET Core), Java, or Node.js; practical experience with GenAI APIs (OpenAI, Azure OpenAI) and AI frameworks such as LangChain.
Experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and CI/CD pipelines.
Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related technical field.
Experienced with enterprise-grade AI and cloud-native solution engineering including code development, debugging, and performance tuning in client-facing IT service environments.
Able to independently evaluate and integrate emerging AI tools and frameworks, contributing to reusable AI engineering assets and internal communities.
Skilled at mentoring junior engineers and collaborating across global teams, supporting technical upskilling and architecture discussions.