Production AI Automation Engineer — Hands-on Builder

DKMRBH Inc

Bethesda (MD)

On-site

USD 110,000 - 165,000

Full time

14 days+

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Benefits offered by this job

Remote work

Quarterly travel for PI Planning

Job summary

DKMRBH Inc is 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 builder/implementation role works with AI development tools, LLM platforms, AI agents, APIs, and automation frameworks to turn manual workflows into production-ready capabilities, with quarterly travel for PI planning.

Qualifications

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 environments.
Hands-on experience designing, implementing, testing, and deploying AI automation or AI-enabled solutions.
Experience using Cursor, GitHub Copilot, and Microsoft Copilot or similar AI-assisted tools.
Strong ability to convert manual workflows into AI-enabled automated processes.
Experience taking AI solutions from rapid prototype to production deployment.
Experience integrating AI with enterprise APIs, applications, databases, and data sources.
Programming/scripting experience with Python, Bash, or similar languages.
Experience with AWS, Azure, or AliCloud or other major cloud platforms.
Understanding of AI/LLM implementation, production readiness, testing, monitoring, and operational support.

Responsibilities

Identify high-value manual processes and translate requirements into AI automation or agent-based solutions.
Prototype, develop, test, deploy, and iterate AI-enabled solutions using AI tools and automation platforms.
Build AI agents and workflows that integrate with enterprise systems and data sources.
Move AI solutions into production with proper security, architecture, governance, and responsible AI controls.
Implement evaluation, monitoring, observability, fallback, and human-in-the-loop for deployed AI.
Measure business impact via productivity, cycle-time, quality, adoption, and cost savings.
Collaborate with architecture, engineering, platform, data, finance, and business teams to identify opportunities.
Document reusable AI patterns, approaches, and procedures.
Provide knowledge transfer and mentorship on AI tools and automation practices.
Support FinOps and cloud cost optimization for AI workloads.