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About This Role
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.
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.
Position Description:
The AWS Certified AI Practitioner will be responsible for the design, implementation, optimization, and ongoing support of AWS AI and ML solutions supporting IT Call Center operations. This individual will work closely with program leadership, the AWS Solutions Architect, the AWS Connect Administrator, data analysts, and government stakeholders to identify opportunities to leverage AI and ML capabilities to enhance call center automation, operational intelligence, and service delivery outcomes. Principal responsibilities will include but are not limited to:
- 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.
Education and Experience:Required:
- 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).
Preferred:
- 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.
Required Skills and Competencies:
- 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.
Desired Skills and Competencies:
- 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.
Our Equal Employment Opportunity Policy:
The company is an equal opportunity employer. The company shall not discriminate against any employee or applicant because of race, color, religion, creed, ethnicity, sex, sexual orientation, gender or gender identity (except where gender is a bona fide occupational qualification), national origin or ancestry, age, disability, citizenship, military/veteran status, marital status, genetic information or any other characteristic protected by applicable federal, state, or local law. We are committed to equal employment opportunity in all decisions related to employment, promotion, wages, benefits, and all other privileges, terms, and conditions of employment.
The company is dedicated to seeking all qualified applicants. If you require an accommodation to navigate or to apply to a position on our website, please contact Heaven Wood via e\-mail at accommodations@koniag\-gs.com or by calling 703\-488\-9377 to request accommodations.
Koniag Government Services (KGS) is an Alaska Native Owned corporation supporting the values and traditions of our native communities through an agile employee and corporate culture that delivers Enterprise Solutions, Professional Services and Operational Management to Federal Government Agencies. As a wholly owned subsidiary of Koniag, we apply our proven commercial solutions to a deep knowledge of Defense and Civilian missions to provide forward leaning technical, professional, and operational solutions. KGS enables successful mission outcomes for our customers through solution\-oriented business partnerships and a commitment to exceptional service delivery. We ensure long\-term success with a continuous improvement approach while balancing the collective interests of our customers, employees, and native communities. For more information, please visit www.koniag\-gs.com.
Equal Opportunity Employer/Veterans/Disabled. Shareholder Preference in accordance with Public Law 88\-352
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Koniag Government Services, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Koniag Government Services AI Hiring
Koniag Government Services has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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