AWS Certified AI Practitioner (Remote)

Koniag Government Services, LLC•Nashville, TN

21d•Remote

About The Position

Koniag Services Inc., a Koniag Government Services company, is seeking an innovative and technically skilled AWS Certified AI Practitioner to support the design, implementation, and optimization of artificial intelligence (AI) and machine learning (ML) solutions built on the Amazon Web Services (AWS) platform for our IT Call Center serving government clients. The ideal candidate is a forward-thinking and analytically minded professional with demonstrated experience leveraging AWS AI and ML services to develop intelligent automation, predictive analytics, and natural language processing solutions that enhance the efficiency, performance, and customer experience of IT Call Center operations. They bring strong technical acumen, a passion for emerging AI technologies, and the ability to work independently and collaboratively in a fully remote environment to deliver AI-powered solutions that drive measurable operational improvements. Ability to obtain a government security clearance may be required to support Koniag Services Inc. and our government customers. This position is Remote.

Requirements

Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, Mathematics, or a related field from an accredited college or university. Relevant experience may be considered in lieu of a degree.
3+ years of hands-on experience designing, implementing, and managing AWS AI and ML solutions in a structured IT service delivery or data-driven operational environment.
Demonstrated experience developing and deploying solutions leveraging AWS AI services including Amazon Lex, Amazon Comprehend, Amazon Transcribe, Amazon Polly, and/or Amazon SageMaker.
Experience integrating AWS AI and ML services with cloud-based operational platforms and enterprise systems.
AWS Certified AI Practitioner certification (required at time of hire).
Strong communication skills in English – both written and oral – with the ability to present complex AI and ML solution designs, technical findings, and strategic recommendations clearly and effectively to both technical and non-technical audiences including program leadership and government stakeholders in a remote work environment.
Demonstrated hands-on proficiency in designing and implementing AWS AI and ML solutions leveraging a broad portfolio of AWS AI services including Amazon Lex, Amazon Polly, Amazon Comprehend, Amazon Transcribe, Amazon Rekognition, Amazon SageMaker, and Amazon Bedrock.
Strong working knowledge of natural language processing (NLP), conversational AI, sentiment analysis, speech-to-text, and text-to-speech concepts and their practical application within AWS AI service implementations.
Proficiency in Amazon SageMaker for the development, training, deployment, and monitoring of custom machine learning models, including experience with SageMaker Studio, SageMaker Pipelines, and SageMaker Model Monitor.
Working knowledge of AWS Connect AI-powered contact center capabilities including Amazon Lex IVR and chatbot integration, Amazon Transcribe Call Analytics, Amazon Connect Wisdom, and Amazon Connect Customer Profiles.
Proficiency in serverless and automation services such as AWS Lambda, Amazon EventBridge, and AWS Step Functions for the development of AI-powered workflow automation solutions.
Strong understanding of machine learning concepts including supervised and unsupervised learning, model training and evaluation, feature engineering, and model deployment and monitoring best practices.
Proficiency in scripting or programming languages such as Python for ML model development, AWS Lambda function development, data processing, and AI solution automation.
Working knowledge of AWS security and compliance principles as they apply to AI and ML solution design, including data privacy, IAM policy configuration, and FedRAMP compliance considerations.
Proficiency in Microsoft Office Suite (Word, Excel, PowerPoint, Outlook) and remote collaboration tools such as Microsoft Teams and SharePoint for documentation, communication, and coordination purposes.
Ability to obtain and maintain a government security clearance as required.

Nice To Haves

Prior experience designing and implementing AWS AI and ML solutions within a federal government contracting or AWS GovCloud environment.
Experience supporting AI and ML solution development for an IT call center, service desk, or IT managed services program, including experience with AWS Connect AI-powered contact center capabilities.
AWS Certified Machine Learning – Specialty certification or demonstrated progress toward obtaining the certification.
Experience working in a fully remote AI and ML development role within a government contracting environment.
Active government security clearance (Secret or higher).
AWS Certified Machine Learning – Specialty certification.
AWS Certified Solutions Architect – Associate or Professional certification.
AWS Certified AI Practitioner certification.
Experience designing and implementing AWS AI and ML solutions within an AWS GovCloud environment in support of federal government FedRAMP authorization and data privacy requirements.
Familiarity with federal IT security frameworks and compliance requirements such as NIST SP 800-53, FedRAMP, FISMA, and emerging federal AI governance frameworks as they relate to AI and ML solution design and deployment.
Experience with Amazon Bedrock and large language model (LLM) integration for generative AI application development within a government IT service delivery context.
Familiarity with responsible AI principles, AI ethics frameworks, and bias detection and mitigation strategies as they apply to AI and ML solution development and deployment in a federal government environment.
Experience with data engineering and ML pipeline development tools including AWS Glue, Amazon Kinesis, Amazon Redshift, and related AWS data processing and storage services.
Proficiency in machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn for custom model development and integration with AWS SageMaker.
Experience with MLOps principles and practices including model versioning, continuous integration and delivery for ML pipelines, and automated model retraining and monitoring.
Familiarity with Amazon QuickSight or equivalent cloud-based business intelligence platforms for AI and ML solution performance reporting and operational analytics.
ITIL Foundation certification or higher demonstrating knowledge of IT service management principles relevant to AI-powered IT call center operations.
Experience contributing to business development efforts including proposal writing, AI and ML capability development narratives, and past performance documentation.
Knowledge of conversational AI design principles and best practices for developing effective and accessible IVR and chatbot experiences within a government IT call center context.

Responsibilities

Identify, evaluate, and implement AWS AI and ML service solutions that enhance IT Call Center operations, including intelligent automation, natural language processing, predictive analytics, sentiment analysis, and conversational AI capabilities.
Design, develop, and maintain AWS AI and ML solutions leveraging core AWS AI services including Amazon Lex, Amazon Polly, Amazon Comprehend, Amazon Rekognition, Amazon Transcribe, Amazon SageMaker, Amazon Bedrock, and related AWS AI and ML platform services.
Collaborate with the AWS Connect Administrator to design, implement, and optimize AI-powered contact center capabilities within the AWS Connect platform, including Amazon Lex-powered IVR and chatbot solutions, Amazon Transcribe Call Analytics for real-time and post-call analysis, and Amazon Connect Wisdom for AI-driven agent assistance.
Partner with data analysts and program leadership to develop and implement predictive analytics and machine learning models that leverage call center operational data to forecast call volumes, predict SLA risks, identify performance trends, and support data-driven operational decision making.
Design and implement natural language processing (NLP) and sentiment analysis solutions using Amazon Comprehend and related AWS AI services to analyze customer interactions, identify satisfaction trends, and surface actionable insights for call center quality assurance and continuous improvement programs.
Develop and maintain AI-powered automation solutions that streamline call center workflows, reduce manual processing requirements, and enhance agent productivity, leveraging AWS Lambda, Amazon EventBridge, AWS Step Functions, and related AWS serverless and automation services.
Support the development and implementation of Amazon SageMaker-based machine learning pipelines for training, testing, deploying, and monitoring custom ML models that address specific IT Call Center operational challenges and performance improvement opportunities.
Collaborate with the AWS Solutions Architect to ensure all AI and ML solution designs are architected in alignment with program-wide AWS cloud architecture standards, security requirements, and FedRAMP compliance obligations.
Conduct regular assessments of deployed AI and ML solutions, monitoring model performance, accuracy, and operational impact, and implementing updates, retraining, and optimization activities to maintain solution effectiveness over time.
Develop and maintain comprehensive technical documentation for all AI and ML solutions including solution design documents, architecture diagrams, model documentation, data flow diagrams, and operational runbooks.
Stay current on AWS AI and ML platform updates, new service releases, emerging AI technologies, and industry best practices, proactively identifying opportunities to leverage new AWS AI capabilities to enhance IT Call Center performance and customer experience.
Provide technical guidance and subject matter expertise to program leadership, operations staff, and data analysts on AWS AI and ML service capabilities, solution design approaches, and responsible AI principles and practices.
Ensure all AWS AI and ML solution design and implementation activities comply with applicable federal security requirements, AWS GovCloud policies, FedRAMP authorization requirements, data privacy regulations, and responsible AI governance standards.
Support the development and delivery of training materials and knowledge sharing sessions for program staff on deployed AI and ML solutions, ensuring all relevant team members understand solution capabilities, limitations, and appropriate use cases.
Contribute to business development activities as needed, including supporting proposal efforts with AI and ML capability narratives, technical solution concepts, and relevant past performance documentation.