machine learning engineer

Techwaresoft

<p><b>Senior Machine Learning Engineer</b></p><p>Remote - Contract (Extendable)</p><p><b>Role Description</b></p><p>This is a full-time remote role at Techwaresoft. As a Senior Machine Learning Engineer, you will design, build, and scale machine learning models that drive intelligent, data-driven decisions across the customer lifecycle. You will work at the intersection of data, product, and engineering to translate customer behavior and transaction data into predictive insights. Your work will directly influence customer engagement, retention, and commercial performance. This role offers the opportunity to deliver measurable business impact through applied machine learning.</p><p><b>Qualifications</b></p><ul><li>Bachelor&rsquo;s degree in Computer Science, Engineering, or a related field</li><li>Master&rsquo;s degree in Data Science or another quantitative discipline is preferred</li><li>Proven experience working with large-scale structured and unstructured datasets</li><li>Background in telecom, digital, or consumer-tech environments</li><li>Hands-on experience with model development and deployment pipelines</li><li>Exposure to BI or data warehousing environments</li><li>Strong analytical thinking and problem-solving skills</li><li>3&ndash;6 years of experience as a Data Scientist or Machine Learning Engineer</li></ul><p><b>Responsibilities</b></p><ul><li>Design, train, and optimize machine learning and predictive models</li></ul><p><b>Must Have</b></p><ul><li>Strong knowledge of ML algorithms such as k-NN, Naive Bayes, SVM, Random Forests, and XGBoost</li><li>Advanced proficiency in Python, R, and SQL for data preparation and modeling</li><li>Experience with feature engineering, data preprocessing, and data quality handling</li><li>Solid understanding of model evaluation techniques including AUC, precision, recall, and overfitting</li><li>Ability to build end-to-end ML workflows from data extraction to model scoring</li></ul><p><b>Nice to have</b></p><ul><li>Experience with Databricks platform</li><li>Familiarity with cloud-native environments for ML deployment</li><li>Knowledge of big data frameworks such as Hadoop</li><li>Experience with data visualization tools like Plotly, D3.js, or Tableau</li><li>Strong understanding of ML performance metrics and validation methodologies</li><li>Identify, collect, preprocess, and transform relevant data sources</li><li>Perform exploratory data analysis to uncover trends and behavioral patterns</li><li>Evaluate and combine models using ensemble or hybrid approaches</li><li>Collaborate with engineering, BI, and product teams to align solutions with business needs.</li></ul><p>Job Types: Full-time, Contract</p><p>Work Location: Remote</p>

Senior Machine Learning Engineer

Siemens

<div><div><b>About Brightly</b></div><div><br>
</div><div>
Brightly is a global leader in intelligent, cloud‑based asset lifecycle management, empowering 12,000+ clients with predictive insights to optimize maintenance, energy, and capital planning&mdash;backed by Siemens. Our products help schools, hospitals, cities, and manufacturers keep critical infrastructure running efficiently and sustainably.</div><div><br>
</div><div><b>
The Opportunity</b></div><div><br>
</div><div>
We&rsquo;re looking for a Senior Machine Learning Engineer to lead LLM‑powered application development&mdash;from prototype to production&mdash;on AWS. You&rsquo;ll design robust ML/LLM services that power search, recommendations, copilots, and workflow automation in Brightly&rsquo;s platform, partnering closely with product, data, and engineering teams. Responsibilities and skill expectations reflect current industry practice for senior ML/LLM engineers, including end‑to‑end model lifecycle ownership, production‑grade code, and MLOps.</div><div><br>
</div><div><b>
What you&rsquo;ll do</b></div><div><br>
</div><ul><li>Build LLM applications: Design and implement RAG pipelines, prompt orchestration, tools/agents, safety/guardrails, and evaluation harnesses; instrument for latency, cost, and quality. (Guided by current LLM engineer role practices.)
</li><li>Own the ML lifecycle: Data curation, feature engineering, training/fine‑tuning (LoRA/QLoRA), A/B testing, deployment, monitoring, and continuous improvement of models and prompts.
</li><li>Productionize on AWS: Ship scalable services on EKS/ECS/Lambda; leverage SageMaker, Bedrock, EMR, MSK, Step Functions; apply observability (CloudWatch/OpenTelemetry) and cost controls. (Duties aligned to modern AWS ML roles.)
</li><li>Scale training &amp; inference: Use distributed training (FSDP/DeepSpeed), quantization, caching, vector databases, and GPU/Inferentia for performance and efficiency.
</li><li>MLOps &amp; governance: Establish CI/CD for models (MLflow/Kedro/SageMaker Pipelines), model/version registries, data and prompt lineage, evaluation gates, and responsible‑AI controls. (Aligned with contemporary MLOps templates.)
</li><li>Partner across Brightly: Translate asset‑management use cases into ML/LLM solutions; collaborate with product managers and UX to ship customer‑visible features that measurably improve reliability, safety, and sustainability.
</li><li>Mentor &amp; lead: Provide technical leadership, review designs/PRs, and raise the bar on ML engineering excellence across the team. (Common senior ML expectations.)
</li><li>Perform Exploratory Data Analysis (EDA) on structured, semi‑structured, and unstructured datasets to identify patterns, correlations, feature importance, and data quality issues.
</li></ul><div>(Consistent with ML engineer responsibilities to analyze data before model development.)
</div><ul><li>Conduct deep research on asset-related, operational, and domain-specific datasets to understand root causes, trends, and predictive signals.</li></ul><div><br>
</div><div><b>
What you&rsquo;ll bring</b></div><div><br>
</div><div><b>
Required experience
</b></div><ul><li>8&ndash;10 years total software/ML engineering experience, with 5+ years building and operating ML systems in production.
</li><li>1+ years hands‑on LLM application development (e.g., RAG, fine‑tuning, prompt engineering, evaluators/guardrails, agentic workflows) using packages such as Langchain and Langgraph.
</li><li>AWS proficiency (3+ years): Strong with core services (EKS/ECS, Lambda, S3, DynamoDB/RDS, Step Functions, IAM) and ML stack (SageMaker, Bedrock or HF on AWS). (Representative AWS ML role skills.)
</li><li>Modeling &amp; frameworks: Python, PyTorch, Hugging Face ecosystem; vector stores (e.g., OpenSearch, PGVector, Pinecone), embeddings, retrieval, and evaluation metrics for NLP/LLMs. (In line with senior LLM roles.)
</li><li>MLOps: CI/CD for ML, model registries, experiment tracking, telemetry/monitoring, automated retraining; Docker/Kubernetes, GitHub Actions/GitLab CI. (Current MLOps expectations.)
</li><li>Data engineering fluency: ETL/ELT, streaming/batch (Spark/Flink), data quality and governance controls for ML.<br>
</li></ul><div></div><br>
<div><b>Nice to have
</b></div><ul><li>Experience with distributed training (FSDP, DeepSpeed), RLHF, or Inferentia/Trainium optimization.
</li><li>Exposure to sustainability/asset/intelligent operations domains.
</li><li>Familiarity with security &amp; compliance for ML systems in enterprise environments. (Frequently included in senior ML roles.)</li></ul><div><br>
</div><div><b>
How you&rsquo;ll work
</b></div><ul><li>Pragmatic and product‑oriented: You bias to measurable outcomes and iterate quickly with stakeholders. (Modern senior ML role framing.)
</li><li>Engineering excellence: You write production‑quality Python, design reliable APIs/services, and uphold testing/observability standards. (Common duties in senior templates.)
</li><li>Collaborative leadership: You mentor peers and influence architecture across teams. (Industry‑standard senior expectations.)</li></ul><div><br>
</div><div><b>
Qualifications
</b></div><ul><li>Bachelor&rsquo;s in CS/EE/Math or related field (Master&rsquo;s preferred) or equivalent practical experience. (Typical for senior ML roles.)
</li></ul><div>Join a mission‑driven team building technology that keeps communities running&mdash;safer, greener, and more resilient&mdash;at global scale. You&rsquo;ll pair startup‑speed product work with the reach and rigor of Siemens.</div><div><br>
</div><div><b>
The Brightly culture</b></div><div><br>
</div><div><br>
</div><div>We&rsquo;re guided by a vision of community that serves the ambitions and wellbeing of all people, and our professional communities are no exception. We model that ideal every day by being supportive, collaborative partners to one another, conscientiously making space for our colleagues to grow and thrive. Our passionate team is driven to create a future where smarter infrastructure protects the environments that shape and connect us all. That brighter future starts with us.</div></div><div></div>

Principal Machine Learning Engineer

Microsoft

<div><b>Overview</b><div><p><br>
Microsoft Purview is redefining how data protection works in an AI-native world. As Copilot adoption accelerates and collaboration patterns evolve, protection can no longer live as complex policy configuration buried in portals. It must be intelligent, adaptive, and operational by default. We are looking for a Senior Machine Learning Engineer to build AI-native experiences at the core of our data security platform. This role focuses on applying Generative AI and applied machine learning to solve real customer problems in security, risk reasoning, and automation. You will design, build, and productionize AI systems that power risk scoring, reasoning layers, explainability, AI graders, and intelligent recommendations across multi-tenant SaaS environments. This is a hands-on role requiring strong depth in LLMs, applied ML systems, evaluation frameworks, and large-scale distributed architectures.</p></div><div><p>
You will work closely with Product, Applied Scientists, and Engineering teams to translate ambiguous product problems into robust, reliable, and trustworthy AI capabilities that operate at enterprise scale.</p></div><div><p>
This is not a research-only role &mdash; it is a build-and-ship role focused on measurable customer impact.</p></div><p></p><p><i><br>
Microsoft&rsquo;s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.</i></p><b><br>
Responsibilities</b><br>
<ul><li>
Architect and productionize AI systems that power risk scoring, classifier-level reasoning, explainability narratives, and intelligent recommendations across multi-tenant SaaS environments.</li><li>
Design and build GenAI pipelines (RAG + agentic orchestration) including indexing, embedding strategies, retrieval optimization, prompt frameworks, tool integration, and memory/control flows tailored for security use cases.</li><li>
Own model evaluation and guardrails, defining offline/online metrics (precision, recall, risk calibration, hallucination control), building evaluation harnesses, and implementing responsible AI safeguards.</li><li>
Optimize model trade-offs (latency, cost, accuracy, safety) and deploy scalable inference systems with monitoring, drift detection, and feedback loops from customer signals.</li><li>
Partner with Product and Engineering to translate ambiguous security requirements into reliable AI-native workflows that are simple, trustworthy, and adoption-ready for SMB/SMC and Enterprise customers.</li></ul><p></p><b><br>
Qualifications</b><br>
<ul><li>
8+ years of experience building and deploying machine learning systems in production environments.</li><li>
Strong hands-on experience with LLMs, RAG architectures, and applied Generative AI systems, including prompt design and grounding techniques.</li>
Proven expertise in designing end-to-end ML pipelines (data ingestion feature engineering training evaluation<li> serving).</li><li>
Experience defining and operationalizing offline and online evaluation frameworks (precision/recall, calibration, drift detection, A/B testing) and implementing guardrails for reliability and safety.</li><li>
Strong programming skills in Python, with experience in modern ML frameworks (e.g., PyTorch/TensorFlow) and cloud-based ML infrastructure; experience building multi-tenant SaaS AI systems with attention to scalability, latency, and cost optimization.</li><li>
Experience fine-tuning or adapting foundation models (e.g., LoRA/PEFT) and building agentic orchestration systems is preferred; knowledge of data security, compliance, or risk-scoring domains will be an additional advantage.</li></ul><br>
<br>
<p>This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.</p><p></p><p><br>
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process<b>.</b></p></div>

<div><p>At ABB, we help industries outrun - leaner and cleaner. Here, progress is an expectation - for you, your team, and the world. As a global market leader, we&rsquo;ll give you what you need to make it happen. It won&rsquo;t always be easy, growing takes grit. But at ABB, you&rsquo;ll never run alone. Run what runs the world.</p><p></p><p>
This Position reports to:</p>
Senior Machine Learning Engineer<p><b><br>
What we believe in</b></p><p>
Our mission in ABB IS (Information Systems) is to use information technology to provide valuable, reliable, and competitive IS services for ABB. If you offer deep technical experience, a methodical approach to solving problems, and the willingness to help us remain competitive, we want you on our team.</p><p></p><p><b>
Your role and responsibilities</b></p><p>
Own the stability, scalability, and performance of production-grade ML platforms by designing and enhancing backend services, orchestration layers, and system integrations that power critical ML workflows. Ensure resilient system architectures in distributed settings to preserve high availability, tolerate faults, and enable smooth operation. Own complex problem resolution and perform in-depth root-cause analysis across application, data, and infrastructure layers to reduce operational risk and downtime. Advocate for reliable deployment and DevOps workflows using Docker, Kubernetes (AKS), and CI/CD, ensuring configuration and release controls are applied. Act as a senior technical anchor for cross-functional teams and external vendors, translating complex ML and business requirements into resilient, scalable backend solutions.</p><p></p><p>
The work model for the role is: Hybrid</p><p></p><p><b>
You will be mainly accountable for:</b></p><ul><li>
Oversee the design, improvement, and ownership of backend services that support ML workflows (model registry, deployments, APIs), ensuring well-architected systems across service boundaries, data flow, scalability, fault tolerance, performance, and maintainability, while advising internal teams and vendors on architecture.</li><li>
Diagnose and fix complex production incidents involving Spark jobs, Airflow pipelines, Azure ML runs, and AKS services through in-depth root-cause analysis across application, infrastructure, data, and configuration layers, while enhancing reliability, observability, and deployment stability.</li><li>
Design and maintain Dockerized services and Kubernetes (AKS) deployments, contribute to CI/CD pipelines, and establish best practices for configuration management, secrets handling, environment isolation, scaling strategies, upgrades, and rollback mechanisms.</li><li>
Serve as the primary technical contact, working with data scientists, ML engineers, business analysts, platform teams, and external vendors to convert business and ML workflow needs into scalable backend architectures, while coaching less experienced engineers on system design and complex debugging techniques.</li></ul><p></p><p><b>
Qualifications for the role:</b></p><ul><li>
3&ndash;5 years of experience as a Backend Engineer, Platform Engineer, Senior ML Engineer, or in a similar role.</li><li>
Demonstrated backend development skills in Python (or Java/Go) with direct experience creating APIs and services.</li><li>
Robust understanding of system design, covering distributed systems, microservices, scalability, fault tolerance, and reliability.</li><li>
Direct experience with AML and Airflow focused on platform or operational responsibilities.</li><li>
Working knowledge of Azure ML pipelines, endpoints, and deployment patterns.</li><li>
Solid practical experience with Docker and Kubernetes (AKS).</li><li>
Ability to identify and resolve complex production issues across application, data, and infrastructure layers.</li></ul><p><br>
</p><p><b>More about us</b></p><p>
At ABB IS (Information Systems), our mission is to apply information technology to deliver secure, reliable, and competitive digital solutions that create measurable value for ABB. Our commitment is to advance innovation, operational efficiency, and responsive business processes via reliable technical capabilities and strategic execution. If you possess deep technical capability, clear problem-solving skills, and the commitment to support our advancement in a rapidly developing landscape, we welcome you aboard<br>
</p><p><br>
We value people from different backgrounds. Could this be your story? Apply today or visit www.abb.com to read more about us and learn about the impact of our solutions across the globe.</p><p></p><p><b>
Fraud Warning:</b> Any genuine offer from ABB will always be preceded by a formal application and interview process.<br>
We never ask for money from job applicants.</p><p></p><p>
For current open positions you can visit our career website https://global.abb/group/en/careers and apply.</p><p></p><p>
Please refer to detailed recruitment fraud caution notice using the link https://global.abb/group/en/careers/how-to-apply/fraud-warning.</p></div>

Machine Learning Engineer

Zendesk

<div><h2 class="jobSectionHeader"><b>Job Description
</b></h2><p></p><p>Role: Machine Learning Engineer
</p><p>TBH#:
</p><p>Location: US<br>
</p><p></p><br>
<p>Zendesk&rsquo;s people have one goal in mind: to make Customer Experience better.<br>
</p><p></p><br>
<p>The Enterprise Machine Learning team drives organizational value through scalable ML solutions and data-driven insights, fundamentally changing how business decisions are made. We collaborate closely with stakeholders, applying the latest advances in machine learning, deep learning, and large language models (LLMs) to create highly impactful outcomes. Our commitment is to advance the state of AI, statistical modeling, and robust system design to enhance and expand our core business capabilities.
</p><h3 class="jobSectionHeader"><b>Role Overview
</b></h3><p>As a Machine Learning Engineer, you will serve as a technical and strategic member within the team, driving the development and deployment of advanced data science and machine learning solutions&mdash;particularly those harnessing LLMs and deep learning. You will architect and scale ML systems, foster effective cross-functional collaborations, and ensure that business value is embedded in every technical decision. Your business acumen allows you to translate complex analytical approaches into actionable insights and stakeholder-friendly narratives, strengthening partnership and adoption across the enterprise.
</p><h3 class="jobSectionHeader"><b>Key Responsibilities
</b></h3><ul><li><p>Drive the design, development, and deployment of advanced ML and AI solutions, with an emphasis on large language models (LLMs), deep learning architectures, and sophisticated statistical modeling.
</p></li><li><p>Build scalable, robust data science systems&mdash;from data ingestion, data curation, data modeling to algorithm development, model deployment and monitoring&mdash;meeting enterprise-grade performance, reliability, and compliance standards.
</p></li><li><p>Act as a subject matter expert, collaborating with data scientists, ML engineers, analysts, and business stakeholders to understand needs, define requirements, and deliver practical solutions with measurable business impact.
</p></li><li><p>Effectively articulate complex technical concepts to non-technical partners, bridging gaps between technical teams and business operations for maximum results.
</p></li><li><p>Drive adoption of best practices in MLOps, including CI/CD pipelines, containerization, orchestration, observability, and reproducibility.
</p></li><li><p>Oversee and enhance the integrity, security, and compliance of all data science workflows and contracts.
</p></li><li><p>Stay abreast of the latest industry advancements in ML, LLMs, deep learning, cloud data engineering, and MLOps solutions (AWS, Kubernetes, Snowflake, etc.).
</p></li><li><p>Fostering technical excellence and ensuring alignment with business objectives.
</p></li></ul><h3 class="jobSectionHeader"><b>What We&rsquo;re Looking For
</b></h3><ul><li><p>Education &amp; Experience:
</p><ul><li><p>3+ years&rsquo; experience in Data Science, Machine Learning, or a related field
</p></li><li><p>BA/BS in Computer Science, Data Science, or related discipline (advanced degree is highly preferred)
</p></li></ul></li><li><p>Technical Expertise:
</p><ul><li><p>Deep expertise in statistical modeling, machine learning, and deep learning (including practical experience with LLMs and transformers)
</p></li><li><p>Strong programming skills (Python preferred; Java, Scala, or similar also valued)
</p></li><li><p>Proven ability to build and optimize scalable data science solutions&mdash;end-to-end&mdash;from data pipelines (dbt, Astronomer, Snowflake, AWS) to deployment and monitoring (Docker, Kubernetes, CI/CD, MLOps best practices)
</p></li><li><p>Experience handling and analyzing large datasets, with a preference for experience in cloud data warehouses (Snowflake)
</p></li></ul></li><li><p>Business Acumen:
</p><ul><li><p>Demonstrated success in translating business needs into analytical solutions, driving quantifiable impact
</p></li><li><p>Strong stakeholder engagement skills, with a track record of building trusted business partnerships and driving adoption of data science initiatives
</p></li></ul></li><li><p>Communication &amp; Collaboration:
</p><ul><li><p>Exceptional ability to simplify and communicate complex data science concepts to technical and non-technical audiences alike
</p></li><li><p>Experience working cross-functionally with engineers, analysts, and product leaders
</p></li><li><p>Steadfast commitment to continuous learning, collaboration, and fostering an inclusive, innovative team environment
</p></li></ul></li></ul><h3 class="jobSectionHeader"><b>Why You&rsquo;ll Thrive Here
</b></h3><ul><li><p>Opportunity to develop and scale of LLM and deep learning solutions with real-world business impact
</p></li><li><p>An environment that values innovation, ownership, and professional growth
</p></li><li><p>The chance to work on high-visibility, high-impact projects at scale alongside a passionate multidisciplinary team<br>
</p></li></ul><p></p><br>
<br>
<p>Zendesk builds software for better customer relationships. It empowers organizations to improve customer engagement and better understand their customers. Zendesk products are easy to use and implement. They give organizations the flexibility to move quickly, focus on innovation, and scale with their growth. Based in San Francisco, Zendesk has operations in the United States, Europe, Asia, Australia, and South America. Learn more at www.zendesk.com .<br>
</p><p></p><br>
<p>Individuals seeking employment at Zendesk are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, or sexual orientation.
</p><p></p>The US annualized base salary range for this position is $206,000.00-$308,000.00. This position may also be eligible for bonus, benefits, or related incentives. While this range reflects the minimum and maximum value for new hire salaries for the position across all US locations, the offer for the successful candidate for this position will be based on job related capabilities, applicable experience, and other factors such as work location. Please note that the compensation details listed in US role postings reflect the base salary only (or OTE for commissions based roles), and do not include bonus, benefits, or related incentives.
<p></p><p>Hybrid: In this role, our hybrid experience is designed at the team level to give you a rich onsite experience packed with connection, collaboration, learning, and celebration - while also giving you flexibility to work remotely for part of the week. This role must attend our local office for part of the week. The specific in-office schedule is to be determined by the hiring manager.
</p><p></p><p><b>The intelligent heart of customer experience
</b></p><p>Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love.
</p><p></p><p>Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week.
</p><p></p><p>As part of our commitment to fairness and transparency, we inform all applicants that artificial intelligence (AI) or automated decision systems may be used to screen or evaluate applications for this position, in accordance with Company guidelines and applicable law.
</p><p></p><p>Zendesk is an equal opportunity employer, and we&rsquo;re proud of our ongoing efforts to foster global diversity, equity, &amp; inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law. We are an AA/EEO/Veterans/Disabled employer. If you are based in the United States and would like more information about your EEO rights under the law, please click here .
</p><p></p><p>Zendesk endeavors to make reasonable accommodations for applicants with disabilities and disabled veterans pursuant to applicable federal and state law. If you are an individual with a disability and require a reasonable accommodation to submit this application, complete any pre-employment testing, or otherwise participate in the employee selection process, please send an e-mail to peopleandplaces@zendesk.com with your specific accommodation request.</p></div>

Mowasalat Qatar

<div>
 <p>
  <strong>
   Job Summary
  </strong>
 </p>
 <p>
  The Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models and data‑driven solutions to support Mowasalat&rsquo;s business optimization and digital transformation initiatives. The role focuses on translating complex data into actionable insights, collaborating with cross‑functional teams, and ensuring scalable, reliable, and high‑performing machine learning solutions that drive operational and strategic value.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   <u>
    Main Responsibilities
   </u>
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Machine Learning Model Development &amp; Deployment
  </strong>
 </p>
 <ul>
  <li>
   Design, develop, test, and deploy machine learning models using supervised, unsupervised, and reinforcement learning techniques.
  </li>
  <li>
   Perform exploratory data analysis, feature engineering, and model tuning to improve accuracy, reliability, and scalability.
  </li>
  <li>
   Continuously evaluate and refine models to ensure optimal performance and alignment with business objectives.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Data Pipeline Management
  </strong>
 </p>
 <ul>
  <li>
   Collaborate with data engineers to design, implement, and maintain data pipelines for model training and deployment.
  </li>
  <li>
   Ensure data quality, integrity, security, and availability across all machine learning workflows.
  </li>
  <li>
   Troubleshoot data pipeline issues and optimize data flow for efficient model operations.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Model Evaluation &amp; Performance Monitoring
  </strong>
 </p>
 <ul>
  <li>
   Conduct comprehensive model validation, testing, and performance evaluation.
  </li>
  <li>
   Monitor deployed models for performance degradation or data drift and implement corrective actions as required.
  </li>
  <li>
   Measure and report the impact of machine learning solutions on business outcomes.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Stakeholder Collaboration &amp; Business Alignment
  </strong>
 </p>
 <ul>
  <li>
   Work closely with business stakeholders to understand requirements and translate them into data‑driven solutions.
  </li>
  <li>
   Communicate complex technical concepts and analytical findings to non‑technical audiences in a clear and effective manner.
  </li>
  <li>
   Collaborate with cross‑functional teams to integrate machine learning solutions into business operations and ensure adoption.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Innovation &amp; Continuous Improvement
  </strong>
 </p>
 <ul>
  <li>
   Stay up to date with emerging machine learning technologies, tools, and industry best practices.
  </li>
  <li>
   Research and evaluate new techniques, frameworks, and methodologies to drive innovation and enhance solution effectiveness.
  </li>
  <li>
   Support continuous improvement initiatives within data science and AI capabilities.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   <u>
    Qualifications, Certificates &amp; Skills
   </u>
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Education
  </strong>
 </p>
 <ul>
  <li>
   Bachelor&rsquo;s degree in Computer Science, Mathematics, Statistics, or a related field.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Experience
  </strong>
 </p>
 <ul>
  <li>
   Minimum
   <strong>
    6&ndash;8 years
   </strong>
   of experience in machine learning, data science, or a related field.
  </li>
  <li>
   Preferably with experience in the
   <strong>
    transportation sector
   </strong>
   .
  </li>
  <li>
   <strong>
    2&ndash;3 years of GCC experience
   </strong>
   in a similar or equivalent role within a reputed organization.
  </li>
  <li>
   Proven experience in developing and deploying machine learning models in production environments.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Certifications &amp; Training
   <em>
    (Preferred)
   </em>
  </strong>
 </p>
 <ul>
  <li>
   Certified Machine Learning Engineer (CMLE)
  </li>
  <li>
   TensorFlow Developer Certificate
  </li>
  <li>
   Certified Data Scientist (CDS) or equivalent
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Skills
  </strong>
 </p>
 <ul>
  <li>
   Machine Learning Algorithms &amp; Model Development
  </li>
  <li>
   Programming Languages:
   <strong>
    Python, R, or Julia
   </strong>
  </li>
  <li>
   ML Frameworks:
   <strong>
    TensorFlow, PyTorch, Scikit‑learn
   </strong>
  </li>
  <li>
   Data Analysis &amp; Visualization:
   <strong>
    Pandas, NumPy, Matplotlib, Seaborn
   </strong>
  </li>
  <li>
   Data Pipeline &amp; Model Deployment
  </li>
  <li>
   Cloud Platforms:
   <strong>
    AWS, Azure, or Google Cloud
   </strong>
  </li>
  <li>
   Analytical Thinking &amp; Problem‑Solving
  </li>
  <li>
   Communication &amp; Stakeholder Collaboration
  </li>
 </ul>
 <p>
 </p>
</div>


Machine Learning Engineer - Qatarization

<div><p>Siemens Digital Industries (DI) is a global leader in automation and digitalization. In close collaboration with partners and customers, we drive digital transformation across process and discrete industries. Our Digital Enterprise portfolio delivers an end-to-end ecosystem of products, solutions, and services that help organizations of all sizes integrate and digitalize their entire value chain.</p><p>
Our industry-focused portfolio enables customers to achieve greater efficiency, flexibility, and innovation. We continuously evolve our technology stack by integrating groundbreaking future technologies. Siemens Digital Industries Software is headquartered in Nuremberg, Germany, and employs approximately 75,000 people worldwide.</p><p>
We offer a role that combines responsibility, autonomy, and the opportunity to contribute proactively. Our culture promotes teamwork, ownership, and continuous individual development.</p><p>
Learn more: https://siemensneo.com/disw/</p><p>
We're looking for a <b>Principal Machine Learning Engineer </b>to lead LLM‑powered application development&mdash;from prototype to production&mdash;on AWS. You&rsquo;ll design robust ML/LLM services that power search, recommendations, copilots, and workflow automation in Brightly&rsquo;s platform, partnering closely with product, data, and engineering teams. Responsibilities and skill expectations reflect current industry practice for senior ML/LLM engineers, including end‑to‑end model lifecycle ownership, production‑grade code, and MLOps.
</p><h2 class="jobSectionHeader"><b>Responsibilities:</b></h2><ul><li>
Build LLM applications: Design and deliver RAG pipelines, prompt workflows, agent tools, safety guardrails, and evaluation frameworks &mdash; optimizing for performance, cost, and quality.</li><li>
Own the ML lifecycle end-to-end: From data prep and feature engineering to fine-tuning (LoRA/QLoRA), testing, deployment, monitoring, and continuous improvement.</li><li>
Productionize on AWS: Launch scalable ML services using AWS infrastructure and ML tooling, with strong observability and cost efficiency.</li><li>
Scale training &amp; inference: Apply distributed training, quantization, caching, and vector databases to improve performance and efficiency.</li><li>
Drive MLOps &amp; governance: Build CI/CD pipelines for ML, manage model/version registries, track experiments, and enforce responsible AI practices.</li><li>
Partner cross-functionally: Work with product and engineering teams to turn real asset-management problems into customer-facing ML solutions.</li><li>
Mentor &amp; lead: Provide technical guidance, review designs, and elevate ML engineering standards across the team.</li><li>
Analyze data deeply: Perform EDA on structured and unstructured data to uncover insights, patterns, and quality issues.</li><li>
Research domain signals: Investigate operational and asset datasets to identify trends, root causes, and predictive opportunities.</li></ul><h2 class="jobSectionHeader"><b>
Qualifications:</b></h2><ul><li>
B.Sc. in Computer Engineering or Electronics Engineering.
</li><li>10+ years in software/ML engineering, including 3+ years running production ML systems end-to-end.</li><li>
Hands-on experience building LLM applications (RAG, fine-tuning, prompt engineering, evaluators/guardrails, agent workflows) using tools like LangChain/LangGraph.</li><li>
Strong AWS background (3+ years), including core infrastructure and ML services such as SageMaker and Bedrock.</li><li>
Solid modeling stack: Python, PyTorch, Hugging Face, embeddings/vector databases, and LLM evaluation techniques.</li><li>
Proven MLOps experience: CI/CD for ML, experiment tracking, model registries, monitoring, and automated retraining pipelines.</li><li>
Good data engineering foundations: ETL/ELT pipelines, streaming/batch processing (Spark/Flink), and data governance practices.</li><li>
Experience with distributed training, RLHF, or optimization on specialized AI hardware is a plus!</li><li>
Preferrably has background in asset, sustainability, or intelligent operations domains.</li><li>
Familiarity with enterprise ML security and compliance practices is nice to have.</li></ul><p>
We&rsquo;re Siemens. A collection of over 377,000 minds building the future, one day at a time in over 200 countries. We're dedicated to equality, and we welcome applications that reflect the diversity of the communities we work in. All employment decisions at Siemens are based on qualifications, merit and business need. Bring your curiosity and creativity and help us shape tomorrow!</p><p>
Siemens is an equal opportunities employer and do not discriminate unlawfully on any grounds. We are committed to providing access and equal opportunity.</p></div>

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