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senior machine learning engineer

Unifonic

<div>
 
 Job Description
 
 
 
 Proudly voted a Great Place to Work&reg;, we are a dynamic startup in the SaaS space that is revolutionizing the way businesses communicate. Our team is made up of 500 energetic and passionate Unifones who are dedicated to delivering the best possible experience to 5000+ customer-centric companies.
 
 
 We pride ourselves on our fun and collaborative work environment, where creativity and new ideas are constantly encouraged. As shareholders in the business, we&rsquo;re so much more than a group of passionate communicators. We are Unifones. Join our team and be a part of something big!
 
 
 
 Meet the team!
 
 
 
 Our Engineering team is responsible for designing, developing, and maintaining the systems and technologies that drive Unifonic&rsquo;s solutions. We work closely with other departments to ensure our products and services meet the needs of our customers. If you are passionate about technology and are excited about working on cutting-edge communication and engagement solutions, we want you on our team.
 
 
 As a Senior Machine Learning (AI) Engineer, you will be responsible for designing, developing, and deploying advanced machine learning solutions across various domains, including NLP, Text Classification, RAG, LLMs, Recommender engines, and Anomaly detection. This role involves end-to-end project ownership, from data preprocessing to the creation of service APIs, and offers opportunities to work on cutting-edge AI technologies.
 
 
 
 Help Us Shape The Future Of Communication By
 
 
 
 <ul>
 <li>
 Leading the end-to-end design, development, and deployment of robust and scalable machine learning solutions, with a strong emphasis on NLP and RAG architectures.
 </li>
 <li>
 Architecting and implementing RAG systems, combining large language models (LLMs) with robust retrieval mechanisms to improve the accuracy, factual grounding, and interpretability of generated content.
 </li>
 <li>
 Applying advanced NLP techniques for tasks such as text classification, entity recognition, sentiment analysis, summarization, question answering, and information extraction.
 </li>
 <li>
 Researching, evaluating, and integrating state-of-the-art NLP models and RAG frameworks (e.g., Transformers, BERT, GPT variants, Vector Databases, Semantic Search).
 </li>
 <li>
 Mentoring junior team members on the team, sharing knowledge, and advising the best machine learning and software engineering practices and approaches.
 </li>
 <li>
 Establishing and maintaining robust communication channels with other cross-functional teams to facilitate the integration of machine learning solutions into other Unifonic products.
 </li>
 <li>
 Developing and optimizing highly confident machine learning algorithms and models and creating/exposing the service APIs using frameworks such as Flask, FastAPIs, or other relevant frameworks.
 </li>
 <li>
 Staying up to date with the latest machine learning research papers, and AI trends (i.e. Generative AI).
 </li>
 <li>
 Collaborating with the data engineering team and other teams to collect and analyze extensive datasets, extracting insights and patterns, in real-time, near-real-time, or batch processing mode.
 </li>
 <li>
 Implementing proof of concepts and prototypes to demonstrate the potential of new AI use cases and innovations.
 </li>
 <li>
 Building scalable, maintainable machine learning services, which should handle thousands of requests per second, and help to perform the required load tests to meet the SLA.
 </li>
 <li>
 Reviewing the code of other team members and suggesting improvements to ensure the SOLID principles and clean architecture.
 </li>
 <li>
 Assisting in the project documentation and demos.
 
 
 </li>
 </ul>
 Job requirements
 
 
 
 What You'll Bring
 
 
 
 <ul>
 <li>
 Proven experience designing and implementing RAG systems, including familiarity with various retrieval strategies (e.g., BM25, dense retrieval, hybrid approaches) and knowledge graph integration.
 </li>
 <li>
 Hands-on experience with LLM orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar tools for building and managing autonomous agents.
 </li>
 <li>
 Deep expertise in various NLP techniques and models, including but not limited to:
 
 
 </li>
 </ul>
 Transformer architectures (e.g., BERT, GPT, T5, LLama, Mistral)
 
 
 Large Language Models (LLMs) and their fine-tuning/adaptation
 
 
 Vector embeddings and similarity search
 
 
 Text classification, named entity recognition (NER), sentiment analysis, summarization, and question answering.
 
 
 <ul>
 <li>
 Hands-on 3-5 years of relevant work experience as a Machine Learning Engineer.
 </li>
 <li>
 Hands-on 3+ years of experience with Python.
 </li>
 <li>
 Excellent analytical abilities, with the capacity to collect, organize, and analyze large datasets to glean valuable insights.
 </li>
 <li>
 End-to-end experience in training, evaluating, testing, and deploying machine learning products in production.
 </li>
 <li>
 Ability to write world-class code in Python (SOLID principles), considering the best software engineering fundamentals, i.e. data structures, algorithms, and data modeling
 </li>
 <li>
 Solid experience in ML frameworks such as NumPy, Pandas, Scikit-Learn, PyTorch, Keras, BERT, Tensorflow, and similar.
 </li>
 <li>
 Familiarity with MLOps best practices, e.g. Model deployment and reproducible research.
 </li>
 <li>
 Mastering data science needed skills like SQL, hypothesis testing, Data cleansing, data augmentation, data pre-processing techniques, and dimensionality reduction.
 </li>
 <li>
 Basic knowledge of Kubernetes and Docker is nice to have.
 </li>
 <li>
 Excellent understanding of Machine learning techniques like Naive Bayes classifiers, SVM, Decision Tree, KNN, K-means, Random Forest, modeling and optimization, evaluation metrics, classification, and clustering.
 </li>
 <li>
 Experience with the Hugging Face libraries (i.e. transformers).
 </li>
 <li>
 Experience fine-tuning pre-trained models and using vector search to enhance LLMs results.
 </li>
 <li>
 Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques.
 </li>
 <li>
 Familiar with code versioning tools such as GIT, CI/CD concepts, and toolchains.
 </li>
 <li>
 Familiar with Agile methodologies i.e. scrum and kanban.
 </li>
 <li>
 Ability to develop high-level architecture and low-level design, End-to-end for a specific project.
 </li>
 <li>
 Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is a plus.
 </li>
 <li>
 Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques is nice to have.
 </li>
 <li>
 Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is nice to have.
 </li>
 <li>
 General knowledge of Data warehouse tools e.g. Vertica is a plus.
 </li>
 <li>
 A Bachelor&rsquo;s degree in a relevant field. (e.g. Computer Science, Computer Engineering, Software, etc).
 </li>
 <li>
 Excellent communication and collaboration skills.
 </li>
 <li>
 Good level of spoken and written Arabic and English.
 
 
 </li>
 </ul>
 
 As a Unifone you will receive a range of benefits:
 
 
 
 <ul>
 <li>
 Competitive salary and bonus.
 </li>
 <li>
 Unifonic share scheme (we are all owners!).
 </li>
 <li>
 30 holiday days after the first anniversary.
 </li>
 <li>
 Your Birthday off!
 </li>
 <li>
 Spend up to 25 days per year working from anywhere in the world!
 </li>
 <li>
 Paid leave for new parents.
 </li>
 <li>
 LinkedIn learning license.
 </li>
 </ul>
</div>

Senior Machine Learning Engineer

Siemens

<div>
 
 About Brightly
 
 
 
 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.
 
 
 
 The Opportunity
 
 
 
 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.
 
 
 
 What You&rsquo;ll Do
 
 
 
 <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>
 (Consistent with ML engineer responsibilities to analyze data before model development.)
 
 
 <ul>
 <li>
 Conduct deep research on asset-related, operational, and domain-specific datasets to understand root causes, trends, and predictive signals.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Bring
 
 
 
 
 Required experience
 
 
 
 <ul>
 <li>
 5&ndash;7 years total software/ML engineering experience, with 3+ 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.
 
 
 </li>
 </ul>
 
 Nice to have
 
 
 
 <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>
 How you&rsquo;ll work
 
 
 <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>
 
 Qualifications
 
 
 
 <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>
 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.
 
 
 
 The Brightly culture
 
 
 
 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>

Arrow Electronics

<div>
 
 Position
 
 
 
 Data Scientist / Senior Machine Learning Engineer
 
 
 
 Job Description
 
 
 
 
 Position Overview:
 
 
 
 As Data Scientist / Senior Machine Learning Engineer at Arrow Electronics, you will assume a pivotal role in driving our data science and machine learning initiatives to reach unprecedented heights. Collaborating closely with cross-functional multinational teams, your responsibilities will encompass the end-to-end machine learning lifecycle, including stakeholder communications, data analysis, designing, development, deployment, and optimization of cutting-edge models aimed at addressing intricate challenges and generating actionable insights. Your expertise in data analysis, model development, and deployment will be crucial in shaping the future of our company.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Machine Learning Model Development: Design, develop, and implement advanced machine learning models and algorithms to solve real-world problems in various domains.
 </li>
 <li>
 Data Analysis: Explore and analyze large datasets to extract meaningful insights and identify trends, anomalies, and patterns.
 </li>
 <li>
 Model Evaluation: Evaluate the performance of machine learning models using appropriate metrics and fine-tune models for optimal results.
 </li>
 <li>
 Deployment: Lead the deployment of machine learning models into production environments and ensure scalability and efficiency.
 </li>
 <li>
 Cross-functional Collaboration: Collaborate closely with business stakeholders, software engineers, data engineers, and domain experts to integrate machine learning solutions into our products and services.
 </li>
 <li>
 Mentorship: Provide guidance and mentorship to junior data scientists and machine learning engineers, fostering a culture of continuous learning and growth.
 </li>
 <li>
 Research and Innovation: Stay up-to-date with the latest advancements in the field of data science and machine learning and apply innovative techniques to solve complex challenges.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Proven experience (2 to 5 years) in developing and deploying machine learning models in a production environment.
 </li>
 <li>
 Proficiency in programming languages, especially Python, and experience with data-related libraries such as pandas, matplotlib, NumPy, etc.
 </li>
 <li>
 Strong knowledge of machine learning frameworks and libraries.
 </li>
 <li>
 Expertise in data preprocessing, feature engineering, and model selection.
 </li>
 <li>
 Familiarity with large language models and AI agent design.
 </li>
 <li>
 Experience with text-based NLP, information extraction, entity recognition, and text-related data science tasks is highly desirable.
 </li>
 <li>
 Strong communication skills and the ability to convey technical concepts to non-technical stakeholders.
 </li>
 <li>
 Experience with vectorized databases and unstructured search engines is a plus.
 </li>
 <li>
 A Master&rsquo;s degree in computer science, Statistics, Data Science, or a related field is a plus.
 </li>
 <li>
 Publications or contributions to open-source projects are a plus.
 
 
 </li>
 </ul>
 If you are a passionate and experienced Data Scientist / Senior Machine Learning Engineer who thrives on solving challenging problems and wants to make a significant impact in the field, we encourage you to apply and join our dynamic team at Arrow Electronics Together, we will drive innovation and shape the future of data science and machine learning.
 
 
 
 Location:
 
 
 
 EG-Cairo, Egypt (Al Emdad &amp; Al Tamween)
 
 
 
 Time Type
 
 
 
 Full time
 
 
 
 Job Category
 
 
 
 Information Technology
</div>

Data Scientist / Senior Machine Learning Engineer

<div>
 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.
 
 
 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.
 
 
 We offer a role that combines responsibility, autonomy, and the opportunity to contribute proactively. Our culture promotes teamwork, ownership, and continuous individual development.
 
 
 Learn more: https://siemensneo.com/disw/
 
 
 We're looking for a
 
 Principal 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.
 
 
 
 Responsibilities:
 
 
 
 <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>
 Qualifications:
 
 
 <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>
 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!
 
 
 Siemens is an equal opportunities employer and do not discriminate unlawfully on any grounds. We are committed to providing access and equal opportunity.
</div>

Principal Machine Learning Engineer

700apps

<div>
 We are looking for a highly skilled
 
 Senior AI Engineer / AI Architect
 
 to lead the design, development, and deployment of scalable AI solutions.
 
 
 In this role, you will combine hands-on model development with high-level system architecture to help shape our AI strategy and deliver production-ready intelligent systems.
 
 
 You will work closely with engineering, product, and leadership teams to transform business needs into powerful AI-driven solutions.
 
 
 
 Key Responsibilities
 
 
 
 AI Architecture &amp; System Design
 
 
 <ul>
 <li>
 Design end-to-end AI/ML system architecture from data ingestion to deployment and monitoring.
 </li>
 <li>
 Define scalable and reliable ML pipelines.
 </li>
 <li>
 Select the appropriate tools, frameworks, and infrastructure.
 </li>
 <li>
 Ensure performance, security, scalability, and maintainability of AI systems.
 </li>
 <li>
 Design APIs and AI services for integration with products and platforms.
 
 
 </li>
 </ul>
 Model Development
 
 
 <ul>
 <li>
 Develop, train, and fine-tune machine learning and deep learning models.
 </li>
 <li>
 Work on advanced AI solutions such as LLMs, NLP systems, or computer vision models.
 </li>
 <li>
 Optimize models for accuracy, speed, and cost efficiency.
 </li>
 <li>
 Conduct experiments and evaluate model performance using best practices.
 
 
 </li>
 </ul>
 MLOps &amp; Deployment
 
 
 <ul>
 <li>
 Build and maintain CI/CD pipelines for machine learning workflows.
 </li>
 <li>
 Deploy models to production using containers and cloud services.
 </li>
 <li>
 Implement monitoring, logging, and automated retraining processes.
 </li>
 <li>
 Manage model lifecycle, versioning, and performance tracking.
 
 
 </li>
 </ul>
 Technical Leadership
 
 
 <ul>
 <li>
 Provide technical leadership to AI engineers and data scientists.
 </li>
 <li>
 Review code and guide best practices in AI development.
 </li>
 <li>
 Collaborate with cross-functional teams to align AI solutions with business goals.
 </li>
 <li>
 Contribute to technical strategy and AI roadmap planning.
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 7+ years of experience in AI / Machine Learning engineering.
 </li>
 <li>
 Strong programming skills in Python.
 </li>
 <li>
 Hands-on experience with PyTorch or TensorFlow.
 </li>
 <li>
 Experience working with LLMs and modern AI frameworks.
 </li>
 <li>
 Strong understanding of system design and scalable architectures.
 </li>
 <li>
 Experience with cloud platforms such as AWS, GCP, or Azure.
 </li>
 <li>
 Experience with Docker and Kubernetes.
 </li>
 <li>
 Familiarity with MLOps tools such as MLflow, Airflow, or similar.
 </li>
 <li>
 Solid understanding of data pipelines and data engineering concepts.
 </li>
 </ul>
</div>

AI Architect

Finaira

<div>
 
 The MLOps Engineer II will independently design, implement, and manage the automation and streamlining of the machine learning lifecycle, ensuring reliable, efficient, and scalable deployment and monitoring of models. This role requires a solid understanding of DevOps practices, cloud technologies, and machine learning frameworks, with the ability to bridge the gap between data science and operations effectively.
 
 
 
 Focus:
 
 Independent automation of ML pipelines, deployment strategies, and monitoring in production.
 
 
 
 
 
 
 The difference you will make:
 
 
 
 &bull; Independently design and implement CI/CD pipelines for machine learning models.
 
 
 &bull; Automate the process of model training, validation, testing, and deployment using relevant tools. &bull; Develop and maintain automated testing frameworks for machine learning models.
 
 
 &bull; Develop and manage various model deployment strategies (e.g., A/B testing, canary deployments).
 
 
 &bull; Build and maintain scalable and reliable infrastructure for model serving (e.g., using Kubernetes, serverless functions).
 
 
 &bull; Implement and manage model versioning and rollback mechanisms.
 
 
 &bull; Optimize model serving for latency, throughput, and resource utilization.
 
 
 &bull; Implement and manage comprehensive monitoring and logging systems to track model performance and identify issues (e.g., model drift, data drift).
 
 
 &bull; Set up and manage alerting systems to notify the team of performance degradation.
 
 
 &bull; Contribute to the development and implementation of model governance policies and procedures.
 
 
 &bull; Ensure compliance with security and privacy requirements.
 
 
 &bull; Collaborate effectively with data scientists to understand model requirements and dependencies. &bull; Work with software engineers to integrate machine learning models into applications and services.
 
 
 &bull; Develop and maintain APIs and interfaces for model access.
 
 
 
 
 
 
 What you will bring to the role:
 
 
 
 
 Education:
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Engineering, or a related field.
 </li>
 </ul>
 
 
 
 
 
 Experience:
 
 
 <ul>
 <li>
 3+ years of experience in DevOps, software engineering, or a related role with a focus on machine learning deployment and operations. Proven experience with cloud platforms and containerization technologies. Experience in the financial services industry is a plus.
 </li>
 </ul>
 
 
 
 
 
 Technical Skills:
 
 
 <ul>
 <li>
 Strong proficiency in DevOps practices and tools (e.g., Jenkins, GitLab CI, Docker, Kubernetes).
 </li>
 <li>
 Solid knowledge of cloud computing platforms (e.g., AWS, Azure, GCP) and their machine learning services.
 </li>
 <li>
 Knowledge of MLflow or Kubeflow for model management, or similar.
 </li>
 <li>
 Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
 </li>
 <li>
 Strong experience with scripting and automation (e.g., Python, Bash).
 </li>
 <li>
 Solid understanding of software engineering principles and best practices.
 </li>
 <li>
 Experience with infrastructure as code (IaC) tools (e.g., Terraform, CloudFormation).
 </li>
 </ul>
 
 
 
 
 
 Soft Skills:
 
 
 <ul>
 <li>
 Strong ownership and accountability for assigned work.
 </li>
 <li>
 Excellent communication, interpersonal, and conflict resolution skills.
 </li>
 <li>
 Effective time management and task prioritization.
 </li>
 <li>
 Analytical thinking and problem-solving skills.
 </li>
 <li>
 Ability to work independently and collaborate with teams within and across functions.
 </li>
 <li>
 Ability to actively listen to customers and accurately understand their needs, expectations, and challenges.
 </li>
 <li>
 Adaptability and learning agility in a fast-paced environment.
 </li>
 <li>
 Strong attention to detail and a commitment to quality.
 </li>
 <li>
 Receptiveness to feedback and continuous improvement mindset.
 </li>
 </ul>
 
 
 
</div>

MLOps Engineer II

LSEG

<div>
 Job Summary:
 
 
 As a Junior Data Scientist, you will assist in delivering data-driven insights and solutions under the guidance of senior data scientists. This role is focused on applying basic machine learning and data analysis techniques to assist in developing solutions that address customer needs. You will collaborate with multi-functional teams, learning from experienced professionals and helping implement data science-driven products, particularly focusing on data preparation, model implementation, and data analysis tasks.
 
 
 Role &amp; Responsibilities:
 
 
 <ul>
 <li>
 Assist in the development and deployment of machine learning models under the guidance of senior data scientists.
 </li>
 <li>
 Apply basic machine learning techniques, including data preprocessing, feature engineering, and model evaluation, to solve business problems.
 </li>
 <li>
 Collaborate with the team to gather and analyze data, ensuring data quality and consistency.
 </li>
 <li>
 Support the creation of dashboards, reports, and data visualizations to communicate findings.
 </li>
 <li>
 Work with senior team members to understand client requirements and contribute to the design and implementation of data science solutions.
 </li>
 <li>
 Perform exploratory data analysis (EDA) to understand data patterns and trends.
 </li>
 <li>
 Assist in the maintenance and updating of models and algorithms in production.
 </li>
 <li>
 Develop scripts for automating data processing tasks and workflows.
 
 
 </li>
 </ul>
 Qualifications and Experience Required:
 
 
 <ul>
 <li>
 3-5 years experience in data science, machine learning, or a related field, ideally in a financial industry or familiar with finance.
 </li>
 <li>
 Bachelor&rsquo;s degree in Computer Science, Engineering, Data Science, or a related field.
 </li>
 <li>
 Basic understanding of machine learning algorithms and their applications.
 </li>
 <li>
 Proficiency in Python with knowledge of libraries such as Pandas, NumPy, and scikit-learn for data analysis and model building.
 </li>
 <li>
 Familiarity with SQL for querying relational databases and performing data manipulation.
 </li>
 <li>
 Understanding of data preprocessing techniques, including handling missing data, scaling, and encoding.
 </li>
 <li>
 Basic knowledge of cloud platforms (AWS, GCP, or Azure) and machine learning frameworks such as TensorFlow or PyTorch.
 </li>
 <li>
 Ability to learn quickly and apply new concepts in data science.
 </li>
 <li>
 Strong analytical and problem-solving skills, with the ability to communicate findings effectively.
 </li>
 <li>
 A passion for learning and staying updated with the latest developments in data science and machine learning.
 </li>
 <li>
 Experience with data visualization tools like Tableau, Power BI, or Matplotlib.
 </li>
 <li>
 Familiarity with NoSQL databases like MongoDB or Cassandra.
 </li>
 <li>
 Knowledge of basic NLP techniques for text data analysis.
 </li>
 <li>
 Interest in gaining exposure to deep learning frameworks like TensorFlow and PyTorch.
 </li>
 <li>
 Interest in gaining exposure to LLMs and agentic frameworks
 
 
 </li>
 </ul>
 LSEG is committed to encouraging a diverse, equitable, an inclusive work environment, ensuring equal opportunities for all employees, regardless of their background. We offer great employee benefits to make sure everyone performs to the best of their abilities. We offer a hybrid working model.
 
 
 
 Career Stage:
 
 
 
 Senior Associate
 
 
 
 London Stock Exchange Group (LSEG) Information:
 
 
 
 Join us and be part of a team that values innovation, quality, and continuous improvement. If you're ready to take your career to the next level and make a significant impact, we'd love to hear from you.
 
 
 LSEG is a leading global financial markets infrastructure and data provider. Our purpose is driving financial stability, empowering economies and enabling customers to create sustainable growth.
 
 
 Our purpose is the foundation on which our culture is built. Our values of
 
 Integrity, Partnership
 
 ,
 
 Excellence
 
 and
 
 Change
 
 underpin our purpose and set the standard for everything we do, every day. They go to the heart of who we are and guide our decision making and everyday actions.
 
 
 Working with us means that you will be part of a dynamic organisation of 25,000 people across 65 countries. However, we will value your individuality and enable you to bring your true self to work so you can help enrich our diverse workforce.
 
 
 We are proud to be an equal opportunities employer. This means that we do not discriminate on the basis of anyone&rsquo;s race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law. Conforming with applicable law, we can reasonably accommodate applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
 
 
 You will be part of a collaborative and creative culture where we encourage new ideas. We are committed to sustainability across our global business and we are proud to partner with our customers to help them meet their sustainability objectives. Our charity, the LSEG Foundation provides charitable grants to community groups that help people access economic opportunities and build a secure future with financial independence. Colleagues can get involved through fundraising and volunteering.
 
 
 LSEG offers a range of tailored benefits and support, including healthcare, retirement planning, paid volunteering days and wellbeing initiatives.
 
 
 Please take a moment to read this privacy notice carefully, as it describes what personal information London Stock Exchange Group (LSEG) (we) may hold about you, what it&rsquo;s used for, and how it&rsquo;s obtained, your rights and how to contact us as a data subject.
 
 
 If you are submitting as a Recruitment Agency Partner, it is essential and your responsibility to ensure that candidates applying to LSEG are aware of this privacy notice.
</div>

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