Artificial Intelligence (AI) Engineer

Koniag Government Services, LLC•Huntsville, AL

27d

About The Position

Koniag Government Services is seeking an experienced Artificial Intelligence (AI) Engineer with an active Secret security clearance to support KMS and our government customer in Huntsville, AL. We offer competitive compensation and an extraordinary benefits package including health, dental and vision insurance, 401K with company matching, flexible spending accounts, paid holidays, three weeks paid time off, and more. This role will involve providing expert guidance on AI initiatives, leading research efforts, and collaborating with cross-functional teams to implement AI solutions in cloud environments (especially Google Cloud or Microsoft Azure). The ideal candidate ensures not only strong model performance, but also that systems are resilient, secure, scalable, and compliant with best practices and relevant regulations. They will oversee the full lifecycle from model development to production deployment and maintenance, with a heavy emphasis on operational and security posture.

Requirements

Strong proficiency in Python and AI/ML frameworks (e.g. PyTorch, TensorFlow, cloud-native AI libraries).
Hands-on experience with cloud platforms (Google Cloud, Azure) for AI/ML deployment.
Experience with containerization (Docker), orchestration (Kubernetes) for model deployment.
Experience building and managing CI/CD pipelines including automated testing and secure software delivery.
Familiarity with DevSecOps practices: vulnerability scanning, dependency management, secure coding, secrets management, encryption.
Experience with Infrastructure as Code (e.g. Terraform, CloudFormation, Azure Resource Manager).
Proficiency with APIs / microservices (FastAPI, REST, GraphQL).
Knowledge of vector databases or retrieval systems for embeddings, etc.
Active or Interim SECRET security clearance
Active CompTIA Security+ certification.
B.S. in Computer Science, Engineering, Mathematics, or a related discipline.
Equivalent experience may be accepted in lieu of formal education.
Microsoft Certified: Azure AI Engineer Associate, Google Cloud Professional Machine Learning Engineer, OR similar AI certification.
Compliant with DoW Cyber Workforce Framework (DCWF) requirements – defines foundational, residential, and continuous professional development for roles within the DoW.
Must have excellent written and oral communications skills.
Must be comfortable working in a fast-paced, matrixed team environment, with a client-centric culture, and an environment of high performers.
Must be able to multi-task and shift priorities as needed.
Must have a deep understanding of cloud migrations, software development lifecycle and cybersecurity requirements for the federal government.
Must be able to work effectively with numerous stakeholder types and bring a group to consensus.

Nice To Haves

Prior experience in regulated or high-sensitivity environments (e.g., government, defense, healthcare).
Knowledge of RAG (Retrieval-Augmented Generation) pipelines or multi-agent architecture.
Integrate RPA solutions with cloud AI services, data sources, and mission applications
Experience with agent frameworks and interoperability standards, including MCP, A2A, and LangServe.
Proficiency in designing cloud-native applications with portability across environments (Azure, Google Cloud, on-premises).
Familiarity with observability/monitoring tools and practices (logging, metrics, alerting).
Experience with cloud-native security services in Azure/Google (IAM, policies, encryption, network segmentation).
Experience in model governance, data privacy, bias mitigation, fairness.
Strong software engineering practices: code reviews, automated testing, version control workflows.
Masters in AI, Machine Learning, or a closely related discipline is highly preferred.
3-5 years’ experience with AI implementations
Experienced with Cloud and System Engineering
Experience in Technical Writing for Policy and Planning Documentation
Experience with project roadmaps and planning and tracking multiple, disparate teams
Experience authoring and presenting Executive Level documentation and briefs.

Responsibilities

Design, train, fine-tune, and deploy AI/ML models (LLMs, embeddings, classification models, RAG pipelines).
Build secure, scalable APIs and services to integrate AI capabilities into business systems.
Design and implement agentic AI workflows that coordinate multiple models or services to achieve mission objectives
Integrate retrieval-augmented generation (RAG) and agentic reasoning into production environments
Implement and maintain automated CI/CD pipelines for model development, testing, deployment, and rollback.
Design and implement containerized applications and services that can be deployed across multiple cloud environments.
Build portable AI pipelines with containerization to ensure future-proof deployments and migration flexibility.
Integrate security and compliance controls into all phases of development (DevSecOps), code scans, dependency management, vulnerability assessment, etc.
Use Infrastructure as Code (IaC) tools to provision, configure, and maintain cloud resources.
Deploy AI/ML systems to production in cloud environments (Google Cloud or Microsoft Azure), ensuring scalability, performance, and cost optimization.
Design data pipelines for ingestion, preprocessing, training, inference, monitoring.
Monitor production systems: model drift, performance degradation, operational/logging/metrics, incident response.
Understanding of autonomous workflow design, reasoning strategies, and error handling
Ensure compliance with data security, privacy, and regulatory requirements.
Collaborate across teams (product, operations, security, data) to translate business requirements and risk posture into technical implementation.
Perform other duties as assigned