applied machine learning engineer

Applied Machine Learning Engineer – ML & AI Systems

Openkyber

<div><b>RESPONSIBILITIES:</b>
<p>OpenKyber has a client in Brooklyn, NY that is seeking a Senior Machine Learning Engineer to design and build scalable machine learning systems that power ad relevance, retrieval, and user engagement across digital platforms.</p>
<b>Responsibilities:</b>
<ul><li>Develop and productionize applied machine learning models to improve relevance and performance</li>
<li>Build and maintain scalable backend services and distributed data pipelines</li>
<li>Support the full ML lifecycle, including training, deployment, monitoring, and iteration</li>
<li>Partner with engineering, analytics, and product teams to drive measurable impact</li>
</ul><b>REQUIREMENTS:</b>
<ul><li>Strong software engineering skills with proficiency in Python (or similar language)</li>
<li>Experience with applied machine learning in production environments</li>
<li>Background in backend development and distributed systems</li>
<li>Hands-on experience managing end-to-end ML workflows</li>
</ul><b>Preferred Qualifications:</b>
<ul><li>Experience with vector databases, retrieval systems, or ANN techniques</li>
<li>Familiarity with ML frameworks such as TensorFlow or PyTorch</li>
</ul><p>The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.</p>
<p>We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability &amp; ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.</p>
<p>Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in OpenKyber's sole discretion unless and until paid and may be modified in its discretion consistent with the law.</p>
<p>This job is not eligible for bonuses, incentives or commissions. OpenKyber is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.</p>
<p>By clicking ?Apply Today? you agree to receive calls, AI-generated calls, text messages or emails from OpenKyber and its affiliates, and service providers. Note that if you choose to communicate with OpenKyber via text messaging the frequency may vary, and message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You will always have the right to cease communicating via text by using key words such as STOP.</p>
<p><b>For applications and inquiries, contact:</b> hirings@openkyber.com</p></div>

AI Application Architect

Carnegie Mellon University

<p></p><div><div><div><p>At the SEI AI Division, we conduct research in applied artificial intelligence and the engineering questions related to the practical design and implementation of AI technologies and systems. We currently lead a community-wide movement to mature the discipline of AI Engineering for Defense and National Security.
</p></div><div><p>As our government customers adopt AI and machine learning to provide leap-ahead mission capabilities, we
</p></div><div><ul><li><p>build real-world, mission-scale AI capabilities through solving practical engineering problems
</p></li></ul></div><div><ul><li><p>discover and define the processes, practices, and tools to support operationalizing AI for robust, secure, scalable, and human-centered mission capabilities
</p></li></ul></div><div><ul><li><p>prepare our customers to be ready for the unique challenges of adopting, deploying, using, and maintaining AI capabilities
</p></li></ul></div><div><ul><li><p>identify and investigate emerging AI and AI-adjacent technologies that are rapidly transforming the technology landscape<br>
<br>
</p></li></ul></div><div><p>Are you creative, curious, energetic, collaborative, technology-focused, and hard-working? Are you interested in making a difference by bringing innovation to government organizations and beyond? Apply to join our team.<br>
<br>
</p></div><div><p><b>Overview </b><b>: </b>As an Associate Machine Learning Engineer, you will specialize in engineering solutions that support research into the vulnerabilities of AI and ML algorithms and securing against those vulnerabilities.<br>
</p></div><p></p><div><p>The Secure AI Lab within the SEI&rsquo;s AI Division focuses on improving the security and robustness of AI systems. As part of the world-class research community at Carnegie Mellon University, the Secure AI Lab conducts and applies cutting-edge research to protect AI systems from adversaries who aim to manipulate the system to learn, do, or reveal something it isn&rsquo;t supposed to.<br>
</p></div><p></p><div><p>The Secure AI Lab consists of machine learning research scientists, machine learning engineers, and software developers who work together to solve problems in the following areas:
</p></div><div><ul><li><p><b>Counter AI Research </b><b>: </b>Study threat models targeting AI and ML algorithms , understand the behaviors of AI algorithms, identify weak points, and design novel ways to subvert AI and ML systems .
</p></li></ul></div><div><ul><li><p><b>AI </b><b>and </b><b>ML </b><b>Algorithm Defense </b><b>Research: </b>Creat e practical mitigations and defenses for observed attacks affecting AI and ML algorithms and evaluate the effectiveness of defensive techniques .
</p></li></ul></div><div><ul><li><p><b>Applied Adversarial Machine Learning: </b>Advance the state of the art in adversarial machine learning by developing and transitioning capabilities to government sponsors.<br>
</p></li></ul></div><p></p><div><p>As an engineer, you will solve problems for government sponsors by analyzing, designing, and building responsible AI systems.<br>
</p></div></div><p></p><div><div><p>Your day-to-day engineering tasks will include:
</p></div><div><ul><li><p><b>Identifying </b><b>and </b><b>i </b><b>nvestigating </b>emerging AI and AI-adjacent technologies.
</p></li></ul></div><div><ul><li><p><b>Defining and </b><b>r </b><b>efining </b>processes, practices, and tools for working with AI.
</p></li></ul></div><div><ul><li><p><b>Designing and </b><b>b </b><b>uilding </b>well-engineered prototypes of AI systems.
</p></li></ul></div><div><ul><li><p><b>Transitioning and </b><b>p </b><b>roviding </b>guidance on AI capabilities to government sponsors.<br>
</p></li></ul></div><p></p><div><p><b>Duties
</b></p></div><div><ul><li><p><b>Building Machine Learning Models and Systems: </b>You will work with machine learning frameworks such as TensorFlow, PyTorch , Torch, and Caffe and modern programming languages including Python, C/C++, and Java. You will build and work with dat a pipelines, ETL processes, and backend systems. You will work with, extend, and implement state-of-the-art machine learning methods.
</p></li></ul></div><div><ul><li><p><b>Technical Experimentation: </b>You will experiment with modern and emerging machine learning frameworks, methods, and algorithms in application domains that include computer vision, natural language processing, planning and scheduling, robot control, and engineering safe, trusted, and reliable machine learning systems.
</p></li></ul></div><div><ul><li><p><b>Test </b><b>ing </b><b>and evaluat </b><b>ion </b><b>. </b>You'll conduct rapid prototyping to demonstrate and evaluate technologies in relevant environments. You'll evaluate systems for performance and security. You'll test capabilities using novel testing and analysis techniques.
</p></li></ul></div><div><ul><li><p><b>Collaborat </b><b>ion </b><b>. </b>You'll actively participate on teams of developers, researchers, designers, and technical leads. You'll collaborate with researchers and our government customers to understand challenges, needs, and possible solutions .
</p></li></ul></div><div><ul><li><p><b>Mentoring. </b>You'll contribute to improving the overall technical capabilities of the Division by mentoring and teaching others, participating in design (software and otherwise) sessions, and sharing insights and wisdom across the SEI.<br>
</p></li></ul></div><p></p><div><p><b>Knowledge and </b><b>Experience
</b></p></div><div><ul><li><p>Comprehensive knowledge of machine learning ; previous experience in adversarial machine learning desirable but not required
</p></li></ul></div><div><ul><li><p>A track record of using well-established engineering practices to solve difficult problems
</p></li></ul></div><div><ul><li><p>An understanding of how to convert research results in to functioning prototypes or capabilities
</p></li></ul></div><div><ul><li><p>Experience l ead ing technical projects in novel areas with limited previous work to build upon
</p></li></ul></div><div><ul><li><p>Strong written and verbal communication skills ; able to convey complex technical ideas in a layperson&rsquo;s terms
</p></li></ul></div></div><div><div><ul><li><p>Ample experience with publishing written or technical artifacts showcasing your work
</p></li></ul></div><div><ul><li><p>Strong collaboration skills for working with colleagues and sponsors
</p></li></ul></div><div><ul><li><p>Willing ness to guide and mentor junior team members<br>
</p></li></ul></div><p></p><div><p><b>Requirements
</b></p></div><div><ul><li><p>A bachelor&rsquo;s degree in computer science, statistics, machine learning, electrical engineering, or related discipline with three (3) years of experience; OR MS in the same fields with one (1) year of experience; OR PhD in a relevant discipline.<br>
<br>
</p></li><li><p>Willingness to work onsite 5 days per week at SEI offices in Pittsburgh, PA or Arlington, VA.
</p></li></ul></div><div><ul><li><p>You will be subject to a background investigation and must be able to obtain and maintain an active Department of War security clearance.<br>
<br>
</p></li><li><p>Willing to travel up to 25% of the time to locations outside of your home location. Travel sites include SEI offices in Pittsburgh and Washington, D.C., sponsor sites, and conferences.
</p></li></ul></div></div><p></p><p><b>Joining the CMU team opens the door to an array of exceptional benefits.
</b></p><p></p><p><b>Benefits eligible </b><b>employees enjoy a wide array of benefits including </b><b>comprehensive medical, prescription, dental, and vision insurance </b><b>as well as a generous </b><b>retirement savings program </b><b>with employer contributions. Unlock your potential with </b><b>tuition benefits </b><b>, take well-deserved breaks with ample </b><b>paid time off </b><b>and observed </b><b>holidays </b><b>, and rest easy with life and accidental death and disability insurance.
</b></p><p></p><p><b>Additional perks include a free Pittsburgh Regional Transit bus pass, access to our </b><b>Family Concierge Team </b><b>to help navigate childcare needs, </b><b>fitness center access </b><b>, </b><b>and much more!
</b></p><p></p><p><b>For a comprehensive overview of the benefits available, explore our </b><b>Benefits page </b><b>.
</b></p><p></p><p><b>At Carnegie Mellon, we value the whole package when extending offers of employment. Beyond credentials, we evaluate the role and responsibilities, your valuable work experience, and the knowledge gained through education and training. We appreciate your unique skills and the perspective you bring. Your journey with us is about more than just a job; it&rsquo;s about finding the perfect fit for your professional growth and personal aspirations.
</b></p><p></p><p><b>Are you interested in an exciting opportunity with an exceptional organization?! Apply today!
</b></p><p></p><p><b>Location
</b></p>Arlington, VA, Pittsburgh, PA
<p><b>Job Function
</b></p>Software/Applications Development/Engineering
<p><b>Position Type
</b></p>Staff &ndash; Regular
<p><b>Full Time/Part time
</b></p>Full time
<p><b>Pay Basis
</b></p>Salary
<p><b>More Information:
</b></p><div><div><div><div><ul><li><p>Please visit <b>&ldquo; </b><b>Why Carnegie Mellon </b><b>&rdquo; </b>to learn more about becoming part of an institution inspiring innovations that change the world.</p></li><li><p><br>
</p></li><li><p><b>Carnegie Mellon University is an Equal Opportunity </b><b>Employer/Disability/Veteran </b>.
</p></li><li><p><b>Statement of Assurance</b></p></li></ul></div></div></div></div></div><p></p>

Associate Machine Learning Engineer - Secure AI Lab

All Points Logistics

<div><div><p><b>Data Engineer
 </b></p><div></div><div><div>Huntsville , Alabama
</div><p>Direct Hire
</p><p>Apr 9, 2026
</p></div></div><div><b><i>*US CITIZENSHIP REQUIRED*</i></b><br>
<b><i>*ACTIVE DoD SECRET CLEARANCE REQUIRED*</i></b><br>
<b><i>*THIS IS A SIX MONTH POSITION WITH POTENTIAL TO EXTEND*</i></b><br>
<br>
<b>DESCRIPTION OF POSITION/DUTIES:</b><br>
<br>
All Points is seeking a <b>Data Scientist / Data Engineer &ndash; Supply Chain Analytics</b> to support the <b>Army Materiel Command</b> at <b>Redstone Arsenal in Huntsville, AL</b>. This is a <b>six</b><br>
<b>month, full time position with potential to extend</b> and requires <b>100% on site support</b>. There is no remote work option. <b><i>THIS IS A SIX MONTH POSITION WITH POTENTIAL TO EXTEND.</i></b><br>
<br>
The selected candidate will support <b>Army Supply Chain Command Orchestration and Monitoring operations</b> as a hybrid data scientist and data engineer. This role operates at the intersection of <b>data engineering and applied machine learning</b>, owning both the <b>data pipelines</b> that move supply chain data and the <b>models and analytics</b> that extract operational insight.<br>
Working under the technical direction of a Senior AI Solutions Engineer, the candidate will build, optimize, and maintain production grade analytics that enable <b>supply chain mapping, disruption detection, and risk visibility</b> across priority weapons systems and materiel programs. This is a <b>mission driven, production cadence role</b>, not a research only position.<br>
<br>
<b><i>Duties may include but are not limited to:</i></b>
<ul><li>Build and maintain supply chain mapping constructs that trace multi-tier supplier relationships, component dependencies, foreign ownership indicators, and program linkages</li>
<li>Design, optimize, and maintain data pipelines ingesting supply chain data from government and commercial sources into Snowflake and downstream analytics environments</li>
<li>Develop and enhance disruption alerting models to detect supply chain stress signals, including single-source dependencies, supplier financial distress, geopolitical exposure, and lead-time anomalies</li>
<li>Apply machine learning and AI techniques to supply chain datasets, including entity resolution, anomaly detection, risk scoring, and predictive modeling</li>
<li>Write, optimize, and maintain complex SQL queries and Python-based transformation logic against Snowflake data warehouses</li>
<li>Produce analytic outputs that support Red Team vulnerability assessments, Blue Team mitigation planning, and executive decision-support dashboards</li>
<li>Participate in data quality validation, confidence scoring, and model monitoring workflows</li>
<li>Maintain pipeline documentation, data dictionaries, model artifacts, and data provenance per program governance standards</li>
<li>Support government-directed onboarding of new data sources, including schema mapping and integration into existing pipelines</li>
</ul><b>EDUCATION:
</b><ul><li>Bachelor&rsquo;s degree from an accredited college in a related discipline</li>
</ul><b>YEARS OF EXPERIENCE:
</b><ul><li>Eight (8) to ten (10) years of relevant experience preferred<br>
Minimum of four (4) years of combined data science and data engineering experience required</li>
</ul><b>SKILLS/CERTIFICATIONS:</b>
<ul><li>Demonstrated supply chain domain knowledge, including supplier networks, procurement data, logistics, or defense materiel programs</li>
<li>Proficiency in Python for data engineering, analytics, and machine learning workflows</li>
<li>Strong SQL skills, including complex queries, window functions, and performance optimization</li>
<li>Hands-on Snowflake experience in a production analytics environment</li>
<li>Experience building and maintaining ETL/ELT pipelines using orchestration frameworks (Airflow, dbt, Spark, or equivalent)</li>
<li>Experience applying ML or AI techniques to real-world structured datasets in production or near-production environments</li>
<li>Strong documentation practices supporting data lineage, auditability, and governance</li>
<li>Experience with defense supply chain datasets (CAGE codes, NSNs, DLA/GIDEP) preferred</li>
<li>Experience with entity resolution, graph-based supply chain mapping, or network analysis preferred</li>
<li>Familiarity with AWS or other cloud data platforms in GovCloud or regulated environments preferred</li>
<li>Familiarity with CUI handling and NIST 800-171 data governance requirements preferred</li>
</ul><b>SECURITY CLEARANCE:
</b><ul><li>US Citizenship required</li>
<li><b>Active DoD Secret security clearance required</b></li>
</ul>Positions with All Points are Full-Time, Direct, and include a comprehensive benefits package. For more information, please visit our Career Portal:<br>
<b>https://www.allpointsllc.com/careers/</b><br>
<br>
<b>ALL POINTS IS AN EQUAL OPPORTUNITY EMPLOYER</b></div></div>

2033 Cleared Data Scientist/Data Engineer – Supply Chain Analytics (Secret Clearance Required)

Gathern

<div><p><b>Job Purpose:</b></p><p>
The Business Intelligence Manager owns the company&rsquo;s Business Intelligence function and is accountable for turning data into trusted insights that drive strategic and operational decisions. This role leads the activation of BI across the organization, focusing on correlation, causation, forecasting, and storytelling.The role also leads applied AI and machine learning initiatives, managing ML engineers to build, deploy, and operate production-grade, business-driven models, with clear ownership of applied MLOps practices to ensure reliability, scalability, and trust.</p><p></p><p><b>
Key Responsibilities</b></p><ul><li>
 Lead the BI team in planning, developing, and delivering high-impact dashboards, reports, and analytics solutions</li><li>
 Business Intelligence Leadership</li></ul><ul><li>
Own the end-to-end Business Intelligence strategy and execution</li><li>
Establish BI as a core decision-support capability across the company</li><li>
Define and govern KPIs, metrics, and analytical standards</li><li>
Ensure insights consistently explain what happened, why it happened, and what to do next</li><li>
Lead executive and operational reporting and insight narratives</li></ul>
Analytics, Forecasting &amp; Insights<ul><li>
Oversee trend analysis, root-cause analysis, and correlation studies</li><li>
Drive forecasting, projections, and scenario analysis for planning and expectation-setting</li><li>
Ensure analytical outputs are explainable, assumption-driven, and decision-ready</li></ul><p>
Applied AI and Machine Learning Leadership</p><ul><li>
Lead and mentor ML engineers to design, build, and deploy applied machine learning models</li><li>
Own the applied ML and MLOps roadmap, aligned with BI and business priorities</li><li>
Guide the development of models such as:</li><li><ul><li>
Recommendation and pattern discovery models</li><li>
Anomaly detection</li></ul></li><li>
Define and enforce MLOps standards, including:</li><li>
Model versioning and lifecycle management</li><li>
Deployment and rollback strategies</li><li>
Monitoring model performance, drift, and data quality</li><li>
Retraining and validation processes</li><li>
Ensure ML models are:</li><li>
Business-driven and use-case oriented</li><li>
Explainable and interpretable</li><li>
Observable and maintainable in production</li><li>
Cross-Functional Leadership</li><li>
Act as the intelligence partner for Product, Finance, Operations, and Engineering</li><li>
Work closely with Data Engineering, Backend Engineering, and Platform teams to operationalize insights and ML models</li></ul>
Technical &amp; Analytical Requirements<ul><li>
Strong understanding of Business Intelligence and analytics practices</li><li>
Hands-on experience with BI tools (Power BI preferred)</li><li>
Strong analytical foundation, including:</li><li><ul><li>
Correlation vs causation</li><li>
Forecasting and scenario modeling</li><li>
Root-cause analysis</li></ul></li><li>
Solid understanding of data warehousing concepts (BigQuery preferred)</li><li>
Working knowledge of applied machine learning, including:</li><li><ul><li>
Supervised and unsupervised learning approaches</li><li>
Feature engineering and model evaluation</li><li>
Model explainability techniques</li></ul></li><li>
Working knowledge of MLOps concepts, including:</li><li><ul><li>
Model deployment and lifecycle management</li><li>
Monitoring for performance degradation and data drift</li><li>
Retraining strategies and validation workflows</li><li>
Collaboration with engineering teams on CI/CD and production readiness</li></ul></li><li>
Ability to guide ML engineers on both modeling and operationalization, without acting as a research data scientist</li></ul><br>
<b>
Requirements</b><br>
<ul><li>
Bachelor&rsquo;s degree in Computer Science, Information Systems, or a related field..</li><li>
5+ years of experience in business intelligence, data analytics, or data management.</li><li>
Applied ML models are deployed, monitored, and reliably operated in production</li><li>
MLOps practices ensure models remain accurate, observable, and trusted over time</li><li>
ML outputs are explainable and confidently used by business stakeholders</li><li>
BI remains the starting point for decisions, with ML and MLOps extending insight into action</li></ul></div>

BI Manager

<div><p>OpenKyber is a Proven Leader in providing full IT Services, Software Development and Solutions for 15 years. OpenKyber is a Small &amp; Woman Owned Minority Business with GSA Advantage Certification. We have offices in VA; MD &amp; Offshore development centers in India. We have successfully executed 100+ projects for clients ranging from small business and non-profits to Fortune 50 companies and federal, state and local agencies.</p>
<b>Job Role:</b> Senior DevOps Engineer (Kubernetes)<br>
<b>Location:</b> Camden, NJ - Hybrid (3 days/week onsite) only locals<br>
<b>Duration:</b> Long-Term<br>
<b>About the Role</b>
<p>The Data Scientist plays a pivotal role in planning, executing, and delivering machine learning-based projects that drive business impact. This role involves analyzing large datasets, developing AI /ML /optimization models, and translating findings into actionable insights. The Data Scientist partners with business and operational leaders, supports senior leadership with analytics, and fosters a culture of data-driven decision-making across the organization.</p>
<b>Key Responsibilities</b>
<ul><li>Collect, clean, and analyze datasets from diverse internal and external sources, applying advanced data wrangling techniques to handle structured, semi-structured, and unstructured data while ensuring completeness, consistency, and accuracy.</li>
<li>Acquire access to various databases and source systems (SQL, NoSQL, graph databases) and create data pipelines for efficient and repeatable data science projects.</li>
<li>Apply statistical analysis and visualization techniques (hierarchical clustering, principal components analysis (PCA)) to explore and prepare data.</li>
<li>Design, develop, and validate machine learning, statistical, and optimization models for classification, regression, clustering, recommendation, and prediction tasks.</li>
<li>Select appropriate algorithms and models for AI /ML, and rigorously test them for accuracy, robustness, and fairness.</li>
<li>Perform feature selection and engineering, create predictive variables, and experiment with transformations to enhance performance and interpretability.</li>
<li>Integrate domain knowledge into ML solutions (e.g., care delivery, financial risk, customer journey, quality prediction, sales, marketing).</li>
<li>Conduct controlled experiments (A/B and multivariate testing), to evaluate hypotheses, measure workflow changes, and quantify the impact of AI solutions on operations.</li>
<li>Collaborate with MLOps, data engineers, and IT to evaluate deployment options, and establish best practices around ML production infrastructure.</li>
<li>Continuously monitor execution and health of production ML models, recalibrating as needed and updating them to reflect new data or changing business conditions.</li>
<li>Work with cross-functional teams, collaborating with stakeholders to refine objectives, and ensure alignment between technical outputs and strategic goals.</li>
<li>Create dashboards, and interactive visualizations that communicate results to a wide range of audiences, turning technical findings into actionable recommendations.</li>
<li>Communicate complex projects, models, and results to diverse audiences, including executives and frontline staff, using storytelling and presentation techniques.</li>
<li>Stay current with industry research and emerging technologies in AI, machine learning, and optimization, proactively experimenting with new methods and recommending adoption of tools that strengthen analytics capabilities.</li>
<li>Mentor junior data scientists and analysts, provide guidance on technical approaches and model interpretation, and promote collaboration across teams.</li>
</ul><b>Qualifications</b>
<p>Education Master's, or PhD in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, Operations Research, or a related quantitative field. Specialization in ML, AI, cognitive science, or data science is highly preferred.</p>
<p>Experience and Skills 3-5 years of hands-on experience planning and executing end-to-end data science projects with demonstrated impact on clinical or operational outcomes in business environments Advanced programming proficiency in Python or R with strong expertise in machine learning frameworks (scikit-learn, TensorFlow, PyTorch) and statistical analysis tools Expertise in machine learning and statistical techniques including supervised/unsupervised learning, deep learning, NLP, computer vision, regression models, ensemble methods, and experimental design (A/B testing) Strong data engineering capabilities including SQL/NoSQL database programming, distributed computing tools (Hadoop, Spark, Kafka), data pipeline development, and experience with cloud platforms (AWS, Azure, Google Cloud Platform) Production ML and MLOps experience including model deployment, monitoring, containerization (Docker, Kubernetes), version control, and applying DevOps principles to data science workflows Data visualization and communication excellence with ability to create compelling dashboards (Tableau, Power BI), translate complex technical findings into actionable insights, and present to diverse audiences from executives to frontline staff Cross-functional collaboration skills with proven ability to work in agile environments, partner with stakeholders to align technical solutions with business objectives, and mentor junior team members Healthcare domain knowledge preferred, particularly experience with Epic EHR systems, clinical workflows, and healthcare data standards, along with relevant certifications (Clarity /Caboodle, Google Cloud ML Engineer, AWS ML Specialist)</p>
<p><b>For applications and inquiries, contact:</b> hirings@openkyber.com</p></div>

Senior DevOps Engineer (Kubernetes)

United Arab Emirates University, Department of Family Medicine

<div>
 <strong>
  Job Description
  <br/>
  <br/>
 </strong>
 The Research Assistant (RA) will work directly under the supervision of Dr. Osama Sohaib and contribute to the development and implementation of the CultureXAI framework for precision public health in the UAE. The RA will play a central role in key technical components of the project, including machine learning model development, explainable AI (XAI) implementation, open data analysis, and fairness and bias assessment across diverse population groups. The RA will be responsible for data preprocessing and integration from multiple health and demographic sources, designing and evaluating predictive models for non-communicable disease (NCD) risk, and applying XAI techniques (e.g., SHAP, LIME, counterfactual analysis) to generate interpretable and culturally-aware insights. The role also includes supporting the development of a prototype decision-support dashboard for policymakers and healthcare stakeholders. In addition, the RA will contribute to academic dissemination by assisting in the preparation of high-quality research publications (targeting Q1 journals), technical reports, and conference submissions, as well as supporting broader project dissemination activities. The position requires strong analytical, programming, and research capabilities, along with the ability to work effectively in an interdisciplinary research environment.
 <br/>
 <br/>
 Minimum Qualification
 <br/>
 <br/>
 <ul>
  <li>
   PhD in Business Analytics, Statistics, Data Science, Machine Learning, Computer Science, or a closely related quantitative discipline. - Strong foundation in machine learning, data analysis, and statistics - Proficiency in Python (e.g., Pandas, Scikit-learn) - Experience in developing dashboards or web-based applications (e.g., Flask, Streamlit, React)
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualification
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   PhD in Machine Learning, AI, Data Science, or a related discipline - Prior experience in healthcare analytics, public health data, or applied AI research - Familiarity with explainable AI techniques (e.g., SHAP, LIME, counterfactual methods) - Experience with advanced ML models (e.g., XGBoost, Neural Networks)
   <br/>
   <br/>
  </li>
 </ul>
 Expected Skills
 <br/>
 <br/>
 Experience: - 1&ndash;3 years (RA level) or 3+ years / PhD-level (Research Associate) - Experience working with real-world datasets and applied machine learning projects Technical Skill Set: - Machine learning: supervised/unsupervised learning, model evaluation, hyperparameter tuning - Explainable AI: model interpretability, feature importance, fairness and bias analysis - Programming: Python (required); familiarity with TensorFlow/PyTorch is a plus - Data handling: data cleaning, preprocessing, and multi-source data integration - Visualization: Matplotlib, Seaborn, Plotly, or dashboard tools (e.g., Streamlit, Flask) Research &amp; Soft Skills: - Academic writing and contribution to publications - Literature review and analytical thinking - Ability to work independently and collaboratively in interdisciplinary teams
 <br/>
 <br/>
 Close Date Kindly apply
 <strong>
  before
 </strong>
 the closing date.
 <br/>
 <br/>
 30/06/2026
 <br/>
 <br/>
 Apply
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Research Assistant — Machine Learning & Explainable AI (XAI)

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