machine learning operations engineer

Sandisk

<div>
 <strong>
  Company Description
  <br/>
  <br/>
 </strong>
 Sandisk understands how people and businesses consume data and we relentlessly innovate to deliver solutions that enable today&rsquo;s needs and tomorrow&rsquo;s next big ideas. With a rich history of groundbreaking innovations in Flash and advanced memory technologies, our solutions have become the beating heart of the digital world we&rsquo;re living in and that we have the power to shape.
 <br/>
 <br/>
 Sandisk meets people and businesses at the intersection of their aspirations and the moment, enabling them to keep moving and pushing possibility forward. We do this through the balance of our powerhouse manufacturing capabilities and our industry-leading portfolio of products that are recognized globally for innovation, performance and quality.
 <br/>
 <br/>
 Sandisk has two facilities recognized by the World Economic Forum as part of the Global Lighthouse Network for advanced 4IR innovations. These facilities were also recognized as Sustainability Lighthouses for breakthroughs in efficient operations. With our global reach, we ensure the global supply chain has access to the Flash memory it needs to keep our world moving forward.
 <br/>
 <br/>
 <strong>
  Job Description
  <br/>
  <br/>
 </strong>
 We are seeking a
 <strong>
  ML Ops Engineer
 </strong>
 with expertise in
 <strong>
  Python, ML Frameworks, Model lifecycle, Cloud platforms &amp;Tableau/PBI
 </strong>
 to join our team.
 <br/>
 <br/>
 <strong>
  Duties And Responsibilities
  <br/>
  <br/>
 </strong>
 Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models.
 <br/>
 <br/>
 Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes.
 <br/>
 <br/>
 Automate the training, testing and deployment processes for machine learning models.
 <br/>
 <br/>
 Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability.
 <br/>
 <br/>
 Implement best practices for version control, model reproducibility and governance.
 <br/>
 <br/>
 Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness.
 <br/>
 <br/>
 Troubleshoot and resolve issues related to model deployment and performance.
 <br/>
 <br/>
 Ensure compliance with security and data privacy standards in all MLOps activities.
 <br/>
 <br/>
 Keep up-to-date with the latest MLOps tools, technologies and trends.
 <br/>
 <br/>
 Provide support and guidance to other team members on MLOps practices.
 <br/>
 <br/>
 <strong>
  Qualifications
  <br/>
  <br/>
 </strong>
 <strong>
  Required skills and experience
  <br/>
  <br/>
 </strong>
 3-5 years of experience in MLOps, DevOps or a related field.
 <br/>
 <br/>
 Bachelor&rsquo;s degree in Computer Science, Data Science or a related field.
 <br/>
 <br/>
 Strong understanding of machine learning principles and model lifecycle management.
 <br/>
 <br/>
 Proficiency in programming languages such as Python, with hands-on experience in machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
 <br/>
 <br/>
 Experience with cloud platforms like AWS, Azure, or Google Cloud and their respective machine learning services.
 <br/>
 <br/>
 Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
 <br/>
 <br/>
 Knowledge of CI/CD pipelines, automation tools and version control systems like Git.
 <br/>
 <br/>
 Ability to create dashboards and visualize the data quickly in tools like tableau, power bi, apache superset ,etc
 <br/>
 <br/>
 Strong problem-solving skills and ability to troubleshoot complex issues.
 <br/>
 <br/>
 Experience with monitoring tools and practices for model performance in production.
 <br/>
 <br/>
 Ability to work collaboratively in cross-functional teams.
 <br/>
 <br/>
 <strong>
  Nice To Have/preferred Skills And Experience
  <br/>
  <br/>
 </strong>
 Experience with infrastructure-as-code (IaC) tools such as Terraform or CloudFormation.
 <br/>
 <br/>
 Knowledge of big data technologies like Apache Spark or Hadoop.
 <br/>
 <br/>
 Familiarity with data engineering practices and tools.
 <br/>
 <br/>
 Experience with A/B testing and model validation in production environments.
 <br/>
 <br/>
 Relevant MLOps certifications (e.g., AWS Certified Machine Learning &ndash; Specialty, DataRobot MLOps Certification) are a plus.
 <br/>
 <br/>
 <strong>
  Additional Information
  <br/>
  <br/>
 </strong>
 Sandisk thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.
 <br/>
 <br/>
 Sandisk is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at jobs.accommodations@sandisk.com to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
</div>


Senior Engineer, Industrial Engineering (ML Ops Engineer with 3-5 Yrs Exp)

Zoolatech

<div><div><div><div><div><div><div><div><b>OVERVIEW</b></div>
</div><div><div>RESPONSIBILITIES</div>
</div><div><div>REQUIREMENTS</div>
</div></div></div></div><div><div><div><p>Are you passionate about improving the way Machine Learning systems are developed, deployed, and scaled in real-world production environments? We are collaborating with a leading European Online Fashion &amp; Beauty Retailer to find a highly capable and self-driven <b>Machine Learning Engineer (MLE/MLOps Focus)</b> to join a fast-moving and impactful team working on the Home page.</p><p></p>
This role is centered around building robust ML workflows, streamlining feature creation, and standardizing ML components to ensure scalability, consistency, and speed across the organization. You&rsquo;ll work at the intersection of engineering and data science, playing a key part in shaping how machine learning is delivered at scale.<p></p>
</div><div><ul><li><p>Design and build ML components, including systems for data access, feature management, model training, deployment, and inference at scale.</p></li><li><p>
Develop infrastructure and tooling that enable ML practitioners to experiment, version, deploy, and monitor models in a reliable and automated way.</p></li><li><p>
Architect scalable, modular, and reusable systems that serve as the backbone of ML development across multiple teams.</p></li><li><p>
Implement core abstractions and APIs &mdash; from feature creation to model rollout.</p></li><li><p>
Build and maintain observability and reliability tooling for ML systems &mdash; including telemetry pipelines, model health checks, and automated retraining triggers.</p></li><li><p>
Establish best practices, frameworks, and reference implementations that raise the bar for engineering rigor and speed in ML delivery.</p></li><li><p>
Work closely with infrastructure, data, and security teams to ensure that ML systems are secure, compliant, and production-grade by default.</p></li></ul>
</div><div><ul><li><p>5+ years of experience in Machine Learning Engineering or MLOps roles</p></li><li><p>
Solid Python development skills</p></li><li><p>
Strong hands-on experience with Airflow (MWAA), MLFlow, and/or SageMaker</p></li><li><p>
Familiarity with ML observability tools such as Grafana, custom metric logging, model drift detection, and alerting mechanisms</p></li><li><p>
Proficiency in building CI/CD pipelines for ML systems with automated testing and validation</p></li><li><p>
Experience with Infrastructure-as-Code tools (CloudFormation, YAML)</p></li><li><p>
Understanding of secure and compliant deployment of ML pipelines</p></li><li><p>
Excellent debugging and problem-solving skills</p></li><li><p>
Experience with OpenAI API usage in production, containerization, and Kubernetes orchestration is highly valued</p></li></ul>
</div></div></div><div><div><div><div><p>Location:
</p><div>Istanbul, Turkey
</div></div><div><p>Seniority:
</p><div>Senior
</div></div><div><p>Technologies:
</p><div>Python
</div></div></div></div><div><div><p>Benefits:
</p><ul><li><b>Paid Vacation
</b></li><li><b>Hybrid Work (home/office)
</b></li><li><b>Sick Days
</b></li><li><b>Sport/Insurance Compensation
</b></li><li><b>Holidays Day Off
</b></li><li><b>English Classes
</b></li><li><b>Training Compensation
</b></li><li><b>Transportation compensation</b></li></ul></div></div></div></div></div></div>

Senior MLOps Engineer

ScidentAi

<p>Responsibilities:</p><ul><li>Collaborate with data scientists, machine learning engineers, and software</li></ul><p>engineers to deploy ML models into production environments.</p><ul><li>Automate model deployment pipelines using CI/CD tools for continuous</li></ul><p>integration and delivery of machine learning models.</p><ul><li>Implement version control for ML models, datasets, and experiments (e.g., using</li></ul><p>DVC, MLflow, or similar tools).</p><ul><li>Develop and maintain infrastructure for model training, validation, and</li></ul><p>deployment using cloud platforms (AWS, Azure, GCP).</p><ul><li>Support scaling and monitoring of deployed ML models, ensuring they meet</li></ul><p>performance and reliability requirements in production.</p><ul><li>Set up and manage containerized environments for model deployment using</li></ul><p>tools such as Docker and orchestration platforms like Kubernetes.</p><ul><li>Develop and maintain monitoring tools to track model performance and data</li></ul><p>drift in production environments.</p><ul><li>Implement and automate workflows for data preprocessing, model training,</li></ul><p>testing, and versioning.</p><ul><li>Troubleshoot and resolve any issues related to model deployment,</li></ul><p>performance, or scalability.</p><ul><li>Collaborate with cross-functional teams to optimize model training pipelines</li></ul><p>and ensure seamless integration with production systems.</p><ul><li>Keep up to date with the latest tools, technologies, and best practices in MLOps</li></ul><p>and machine learning.</p><p>Requirements:</p><ul><li>Experience: 0-2 years of experience in MLOps, DevOps, data engineering, or</li></ul><p>related fields.</p><ul><li>Familiarity with machine learning concepts and model deployment processes.</li><li>Basic experience with cloud platforms such as AWS, Azure, or GCP.</li><li>Experience with CI/CD tools (e.g., Jenkins, GitLab CI, CircleCI) for automating</li></ul><p>model deployment pipelines.</p><ul><li>Exposure to version control tools for ML (e.g., DVC, MLflow, Git).</li><li>Proficiency in Python, including libraries like Pandas, NumPy, and Scikit-learn</li></ul><p>for ML-related tasks.</p><ul><li>Experience with containerization tools such as Docker and container</li></ul><p>orchestration platforms like Kubernetes.</p><ul><li>Familiarity with model monitoring tools and understanding of concepts like</li></ul><p>model drift and performance tracking.</p><ul><li>Knowledge of SQL and data pipeline technologies to handle and manipulate</li></ul><p>large datasets.</p><ul><li>Strong problem-solving and troubleshooting skills with a focus on scalable and</li></ul><p>efficient solutions.</p><ul><li>Good communication skills and the ability to collaborate effectively with cross-</li></ul><p>functional teams.</p><ul><li>Exposure to ML model deployment frameworks such as TensorFlow</li></ul><p>Serving, Seldon, or TorchServe.</p><ul><li>Familiarity with machine learning pipelines and orchestration tools</li></ul><p>like Kubeflow or Apache Airflow.</p><ul><li>Experience with data storage systems like Amazon S3, Google Cloud Storage, or</li></ul><p>databases like PostgreSQL, MongoDB, etc.</p><ul><li>Knowledge of model versioning and management platforms like MLflow, Weights</li></ul><p>&amp; Biases, or ModelDB.</p><ul><li>Understanding of data security and privacy concerns in deploying machine</li></ul><p>learning models.</p><ul><li>Experience with performance monitoring tools like Prometheus, Grafana,</li></ul><p>or Datadog.</p><p>Job Types: Full-time, Fresher</p><p>Pay: &#8377;400,000.00 - &#8377;550,000.00 per year</p><p>Benefits:</p><ul><li>Flexible schedule</li><li>Health insurance</li><li>Paid time off</li></ul><p>Work Location: In person</p>

DevOps Engineer

FDM Group

<div><div><div><div><div><h2 class="jobSectionHeader"><b>About The Role</b></h2></div><b>
</b></div></div><div><div><div><div><p>This position requires the successful candidate to work on a W2 directly with FDM. We cannot accept C2C, 1099 or employment sponsorship (e.g. H1-B) for this position.</p>
<p>
FDM is a global business and technology consultancy delivering client and industry driven solutions through our 5 core specialist Practices; Software Engineering, Data &amp; Analytics, IT Operations, Change &amp; Transformation, and Risk, Regulation &amp; Compliance.</p>
<p>
FDM is seeking a <b>ML Ops Engineer </b>located in <b>Cary, NC </b>to support a project in the <b>Financial Services </b>sector. Involvement in this project is anticipated to last initially <b>12 month </b>but may be extended.</p>
<p>
This role will be <b>hybrid</b> with requirements to be in office <b>3</b> days per week.</p>
<div>
<p>We are seeking a skilled MLOps Engineer with deep expertise in Google Cloud Platform (GCP) to help us build a world-class machine learning and AI capabilities within the bank. You will be instrumental in designing, implementing, and maintaining scalable infrastructure and automated pipelines that support the full machine learning lifecycle&mdash;from experimentation to deployment and monitoring.</p>
<p></p>
<p>This role involves close collaboration with data scientists, data engineers, product managers, and platform teams to operationalize models, streamline workflows, and uphold the highest standards of security, privacy, and compliance. You&rsquo;ll help define and evolve our ML Ops practices, ensuring our AI solutions are reliable, reproducible, and impactful.</p><br>
<div></div>
<p>
Key Responsibilities</p>
<ul>
<li>Build and maintain CI/CD pipelines for ML workflows using GCP-native tools such as Cloud Build, Artifact Registry, and Cloud Deploy.</li>
<li>Containerize and orchestrate ML workloads using Docker, Kubernetes, and GKE (Google Kubernetes Engine).</li>
<li>Collaborate with cross-functional teams to transition models from development to production, integrating them into customer-facing applications.</li>
<li>Implement robust model monitoring, logging, and alerting using tools like Vertex AI Model Monitoring, Cloud Logging, and Cloud Monitoring.</li>
<li>Define and enforce best practices for model versioning, testing, and reproducibility using tools like MLflow and Vertex AI Pipelines.</li>
<li>Ensure infrastructure adheres to security and compliance standards, working closely with Cybersecurity and Data Governance teams.</li>
<li>Continuously evaluate and integrate emerging GCP technologies to enhance platform capabilities and delivery speed.</li>
</ul></div></div></div></div></div><div><div><div><h2 class="jobSectionHeader"><b>About You</b></h2></div><b>
</b></div></div><div><div><div><div><p><b>Required Skills &amp; Experience</b></p>
<ul>
<li>Bachelor&rsquo;s degree in Computer Science, Software Engineering, or related field.</li>
<li>5+ years of experience in MLOps or DevOps roles, with a strong focus on cloud-native ML infrastructure.</li>
<li>Proven experience deploying ML models in production (batch and real-time), ideally in regulated or privacy-sensitive environments.</li>
<li>Proficiency in Python, with solid software engineering fundamentals and experience using Terraform or Deployment Manager for infrastructure-as-code.</li>
<li>Hands-on experience with:
<ul><li>GCP ML tools: Vertex AI, AI Platform, BigQuery ML</li>
<li>CI/CD: Cloud Build, GitHub Actions, Jenkins</li>
Containerization &amp; Orchestration: Docker, Kubernetes, GKE<br>
<li>
</li></ul></li></ul><p></p><p><b>Preferred Qualifications</b></p>
<ul>
<li>Experience deploying and managing generative AI models (LLMs) in production, including prompt engineering, evaluation pipelines, and safety guardrails.</li>
<li>Familiarity with observability tools such as MLflow, LangFuse, or Braintrust.</li>
<li>Exposure to data governance and privacy frameworks in cloud environments.</li>
</ul></div></div></div></div><div><div><div><h2 class="jobSectionHeader"><b>About Us</b></h2></div><b>
</b></div></div><div><div><div><div><p><b>About FDM</b></p>
<p>
FDM is an award-winning global business and technology consultancy powering the people behind tech and innovation for over 30 years. We collaborate with world-leading companies to identify the expertise they need, exactly when they need it. We have helped successfully launch nearly 25,000 careers globally to date and are a trusted partner to over 300 companies worldwide.</p>
<p>
FDM has 2500+ employees worldwide, with over 80 nationalities working together as a team. From our origins in Brighton, UK, FDM now has 19 centers located across North America, Europe and Asia-Pacific and is now on the FTSE4Good Index.</p>
<p></p>
<p><b>Dedicated to Diversity, Equity and Inclusion</b></p>
<p>
FDM Group&rsquo;s mission is to make tech and business careers accessible for everyone. Our diverse team of 80+ nationalities thrive on differences, fuels innovation through varied experiences, and celebrates shared successes. As an Equal Opportunity Employer that is listed on the FTSE4Good Index, FDM ensures every qualified applicant, regardless of race, color, religion, sex, sexual orientation, national origin, age, disability or any other status protected by federal or provincial law, gets the chance they deserve.</p><br>
<p></p>
<p>
FDM Group, Inc. is registered to operate and hire employees in select states within the US. We will consider employment applications exclusively from candidates who are either residing in one of the following states or willing to relocate to them: Arizona, California, Colorado, Delaware, Florida, Georgia, Illinois, Indiana, Massachusetts, Maryland, Maine, Minnesota, North Carolina, New Jersey, New York, Pennsylvania, Tennessee, Texas, Utah, and Virginia.</p></div></div></div></div></div><p></p><div><div><div><br>
</div><div></div><ul><div><div>Share Vacancy on Facebook</div></div>
<div></div><div><div>Share Vacancy on X</div></div>
<div></div><div><div>Share Vacancy on LinkedIn</div></div>
<div></div><div><div>Share Vacancy on WhatsApp</div></div>
<div></div><div><div>Send Vacancy to a friend</div></div></ul></div></div></div>

ML Ops Engineer (experienced) - Cary, NC

Unify CX

<div>Job Title: ML Ops Engineer<br>
Location: Remote<br>
Department: Engineering<br>
Job Type: Full-time<br>
<br>
<b>Who We Are:</b><br>
unifyCX is an emerging Global Business Process Outsourcing company with a strong presence in the U.S., Colombia, Dominican Republic, India, Jamaica, Honduras, and the Philippines. We provide personalized contact centers, business processing, and technology outsourcing solutions to clients worldwide. In nearly two decades, unifyCX has grown from a small team to a global organization with staff members all over the world dedicated to supporting our international clientele.<br>
<br>
UnifyCX is a transformative AI platform that empowers and enables teams to deliver efficient, exceptional customer experiences. We engineer superhuman customer experiences through a powerful blend of strategy, omnichannel support, analytics, and AI-driven tools like GoTalent.AI, Voice of Customer, and automatic QA. Our outcome-based model prioritizes measurable results for more than 200 client programs serviced today. With a focus on automation, talent enablement, strategic partnerships, and strict data ethics, UnifyCX delivers scalable, personalized, and compliant solutions that create real business impact.<br>
<br>
At unifyCX, we leverage advanced AI technologies to elevate the customer experience (CX) and drive operational efficiency for our clients. Our commitment to innovation positions us as a trusted partner, enabling businesses across industries to meet the evolving demands of a global market with agility and precision. unifyCX is a certified minority-owned business and an EOE employer who welcomes diversity.<br>
<br>
<b>First Key Projects:</b><br>
Develop the infrastructure needed for ML workloads, including cloud services, containerization, and orchestration tools.<br>
<br>
<b>Key Responsibilities:</b><br>
<ul><li>Model Deployment &amp; Monitoring: Design, implement, and manage the deployment pipelines for machine learning models. Monitor model performance and ensure the stability and scalability of production systems.</li><li>Infrastructure Management: Develop and maintain the infrastructure needed for ML workloads, including cloud services, containerization, and orchestration tools.</li><li>Automation: Automate the ML lifecycle processes, including data ingestion, model training, testing, and deployment.</li><li>Collaboration: Work closely with data scientists and engineers to understand model requirements and ensure seamless integration into production systems.</li><li>Version Control: Implement and manage version control systems for ML models, datasets, and code.</li><li>Performance Tuning: Optimize ML models and systems for performance, cost, and efficiency. Troubleshoot and resolve issues that arise in production environments.</li><li>Security &amp; Compliance: Ensure that all ML systems and processes comply with security policies and regulatory requirements.</li><li>Documentation &amp; Reporting: Document processes, best practices, and workflows. Provide regular reports on system performance, issues, and improvements.</li></ul>
<br>
<b>Required Skillsets:</b><br>
<ul><li>Bachelor&rsquo;s degree in Computer Science, Data Engineering, or related field.</li><li>3+ years of experience in ML Ops, DevOps, or Data Engineering.</li><li>Strong proficiency with Python and Linux-based systems.</li><li>Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).</li><li>Proficiency with containerization (Docker) and orchestration (Kubernetes).</li><li>Experience with cloud platforms (AWS Sagemaker, GCP Vertex AI, or Azure ML).</li><li>Familiarity with CI/CD pipelines and infrastructure-as-code tools (Terraform, CloudFormation).</li></ul>
<br>
<b>Preferred Skillsets:</b><br>
<ul><li>Experience with feature stores (Feast, Tecton, etc.).</li><li>Knowledge of data pipelines and orchestration tools (Airflow, Prefect, Dagster).</li><li>Background in MLOps frameworks (MLflow, Kubeflow, BentoML, DVC).</li><li>Experience implementing model monitoring and drift detection.</li><li>Strong understanding of software engineering best practices and agile workflows.</li></ul></div>

ML Ops Engineer

W2S Solutions

<p>Role: <b>Junior ML Ops Engineer</b></p><p>Work Mode: <b>Onsite/Full time</b></p><p>Location: <b>Chennai, Sholinganallur</b></p><p>Year of Experience: <b>3 - 5 years </b></p><p>Notice Period: <b>Immediate or 15 days</b></p><p><b>Good To Have Skills: </b>Good communication</p><p><b>Must Have Skills: </b>Azure, MLOPS, Open AI/Generative AI, Continuous integration (CI) and continuous delivery (CD), Python</p><p><b>External Description:</b></p><ul><li>We are seeking a passionate and skilled Junior MLOps Engineer with 2-3 years of professional experience to join our team. In this role, you will collaborate closely with Data Scientists to migrate AI applications from Landing Zone V1 to Landing Zone V2 within the Azure Saint-Gobain private network.</li><li>The ideal candidate will possess a strong foundation in Python development, experience deploying applications on Azure, and familiarity with modern DevOps tools and practices. Your contributions will involve adapting Python code, conducting non-regression testing, and ensuring the seamless migration of AI applications.</li><li>3+ years of experience as an MLOps Engineer, Strong foundation in Python development for AI/ML applications.</li><li>Hands-on experience with Azure cloud services and deploying applications in secure environments.</li><li>Familiarity with DevOps tools and practices</li></ul><p>Interested candidates can reach us at <b>@7338773388 or careers@w2ssolutions.com, &amp; hr@way2smile.com</b></p><p>Job Types: Full-time, Permanent</p><p>Pay: &#8377;500,000.00 - &#8377;800,000.00 per year</p><p>Work Location: In person</p>

Junior ML Ops Engineer

Valleysoft

<p></p><div><p>Valleysoft | Center of Excellence is a regional IT services provider based in Riyadh, serving clients globally since 2006. The company collaborates with global partners like IBM to address diverse business and technical challenges, from enterprise application development to process management. Valleysoft's vendor-neutral and process-oriented approach, coupled with operational maturity, ensures high-quality and cost-effective services for clients.</p><p></p><p>
Valleysoft | Center of Excellence is on the lookout for a skilled Data &amp; ML Engineer to join our dynamic team. In this role, you will be responsible for developing and implementing data solutions and machine learning models that enable our clients to derive actionable insights from their data.</p><p>
The ideal candidate will have a strong background in data engineering and machine learning, with experience in creating robust data pipelines and deploying machine learning models in production. You should be comfortable working collaboratively in a fast-paced environment and possess a passion for leveraging data to solve complex problems. If you are ready to take your career to the next level, we want to hear from you!</p><p></p><h3 class="jobSectionHeader"><b>
Responsibilities</b></h3><ul>
<li>Design, develop, and maintain scalable data processing pipelines and workflows for data ingestion and transformation.</li>
<li>Collaborate with cross-functional teams to gather data requirements and implement ETL processes.</li>
<li>Develop, train, and optimize machine learning models based on business needs and available data.</li>
<li>Ensure data quality, consistency, and accessibility throughout data life cycles.</li>
<li>Monitor and troubleshoot data pipelines and machine learning models to optimize performance.</li>
<li>Stay abreast of industry trends, tools, and technologies relevant to data engineering and machine learning.</li>
<li>Document processes and provide training on data engineering and machine learning practices.</li>
<li>Integrate machine learning solutions into existing applications and systems.</li>
</ul><p><b>
Requirements</b></p><ul>
<li>Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.</li>
<li>Proven experience as a Data Engineer or Machine Learning Engineer.</li>
<li>Strong programming skills in Python, Java, or similar languages.</li>
<li>Solid understanding of machine learning algorithms, data pipelines, and data processing frameworks.</li>
<li>Relevant experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and big data technologies (e.g., Hadoop, Spark).</li>
<li>5 years of relevant experience in data engineering and machine learning.</li>
<li>Familiarity with data visualization tools (e.g., Tableau, Power BI) is an advantage.</li>
<li>Excellent analytical and problem-solving skills.</li>
<li>Strong communication and collaboration skills, with the ability to work in a team-oriented environment.</li>
<li>Experience with containerization tools (e.g., Docker) and orchestration systems (e.g., Kubernetes) is a plus.</li>
<li>Relevant certifications in data engineering or machine learning (e.g., AWS Certified Data Analytics, Google Professional Data Engineer) are preferred.</li>
<li>Understanding of data governance and privacy regulations is desirable.</li>
<li>Experience with version control systems (e.g., Git) is a plus.</li>
<li>Understanding of National Information Exchange Model (NIEM) is preferred.</li>
<li>Experience with Dublin Core Metadata is preferred.</li>
</ul><p><b>Skills:</b></p><ul>
<li>Strong understanding of data engineering principles and machine learning workflows.</li>
<li>Ability to design and implement efficient data pipelines and machine learning solutions.</li>
<li>Excellent analytical and problem-solving capabilities.</li></ul></div>

Data & ML Engineer1

Search 10000+ Machine learning operations engineer jobs near you this October. New roles added regularly across leading industries. Apply today.