Corporate AI Engineer (Agent Whisperer)

Bluestaq US External•Colorado Springs, CO

About The Position

Bluestaq is seeking a Corporate AI Engineer to serve as the dedicated, central resource for all company-wide AI initiatives. This is a hands-on individual contributor role focused on the full lifecycle of AI tools: designing, building, securely deploying, maintaining, monitoring, and scaling production-grade AI solutions that become reliable corporate assets. You will be the primary point of contact for AI across teams, providing technical expertise, implementing solutions, ensuring ongoing reliability and compliance, and supporting adoption. The emphasis is on deep engineering execution, production operations, and practical enablement to deliver real business value through AI capabilities like automation, analytics, decision support, and generative tools. Why This Role Matters AI drives strategic advantage at Bluestaq. A single, skilled engineer dedicated to consistently building, deploying, and sustaining AI tools prevents silos, security risks, technical debt, and underutilization. This role establishes a dependable corporate foundation for AI, ensuring tools are robust, compliant, cost-effective, and continuously improved, while empowering teams to leverage them effectively.

Requirements

3+ years hands-on experience in AI/ML engineering, MLOps, or production AI deployment/maintenance.
Proficiency in Python and AI ecosystems (e.g., frameworks for LLMs, RAG, agents, prompt optimization).
Experience with cloud platforms (AWS, Azure, or GCP) for AI workloads, model serving, and infrastructure as code.
Knowledge of containerization/orchestration (Docker, Kubernetes) and CI/CD for AI pipelines.
Strong grasp of AI security, monitoring, governance, and ethics in enterprise contexts.
Demonstrated success supporting diverse stakeholders as a technical point of contact.
Excellent problem-solving, documentation, and communication for technical/non-technical audiences.
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience).

Nice To Haves

Production experience with large-scale generative models, RAG, agentic workflows, or similar.
Exposure to regulated/security-focused environments (defense, government, etc.).
Relevant certifications (cloud ML, etc.).

Responsibilities

Design, build, and deploy internal AI tools, models, and applications using leading foundation models, generative AI platforms, and current frameworks.
Implement robust MLOps pipelines: data handling, model integration/fine-tuning, versioning, CI/CD, containerization, and secure production rollout.
Integrate AI solutions with enterprise systems, APIs, and workflows to enable automation or enhanced capabilities.
Rapidly prototype and refine based on performance metrics and stakeholder input.
Manage ongoing operations for deployed AI tools: monitor performance, detect drift, retrain models, optimize scaling/costs, and resolve incidents promptly.
Set up and maintain observability stacks (logging, metrics, alerting) with industry tools and cloud services.
Uphold security, privacy, ethical standards, and compliance (bias checks, data protection, regulatory alignment).
Conduct routine updates, performance tuning, and audits to keep AI tools effective and up-to-date.
Serve as the go-to expert and point of contact for AI questions, use cases, and requests organization-wide.
Guide teams on feasibility, best practices, tools, and implementation strategies.
Create and maintain reusable standards, templates, reference architectures, and clear documentation for AI work.
Deliver practical support: demos, workshops, guides, and direct assistance to boost company-wide AI capabilities.
Monitor AI tool usage, impact, and ROI; share insights and recommendations regularly.
Assess and securely integrate external AI services or platforms when beneficial.
Support AI governance: risk evaluation, ethical reviews, and policy compliance.
Keep pace with generative AI, agentic systems, and enterprise patterns; suggest actionable adoption approaches.
Collaborate closely with engineering, data, security, product, and corporate teams to align solutions with priorities.