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Siemens

<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

<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>
 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.
 
 
 </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

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

Si-Ware Systems

<div>
 Si-Ware Systems is a global leader in semiconductor and spectroscopy solutions. Our innovative devices and software enable material analysis across many industries.
 
 
 At Si-Ware, we foster a culture of innovation, collaboration, and continuous learning, empowering our people to push the boundaries of technology.
 
 
 As a
 
 Machine Learning Engineer
 
 at Si-Ware, you will design, implement, and deploy applied ML solutions that power real-world spectroscopy devices and emerging physical AI systems.
 
 
 You will work at the intersection of machine learning, software engineering, and intelligent hardware integration.
 
 
 initiatives.
 
 
 
 Responsibilities:
 
 
 
 
 Machine Learning &amp; Modeling
 
 
 
 <ul>
 <li>
 Perform data cleaning, preprocessing, and transformation for training and evaluation.
 </li>
 <li>
 Contribute to the development, training, and improvement of machine learning models.
 </li>
 <li>
 Design and execute structured experiments using appropriate validation strategies (cross-validation, hold-out testing, statistical comparison).
 </li>
 <li>
 Evaluate models using appropriate performance metrics (Accuracy, Precision, Recall, F1, RMSE, etc.).
 </li>
 <li>
 Optimize models and inference pipelines for performance, memory efficiency, and real-time constraints when required.
 </li>
 <li>
 Ensure reproducibility, traceability, and proper validation of models within regulated or industrial environments.
 </li>
 <li>
 Support chemometrics-related workflows, including spectral preprocessing, feature extraction, multivariate modeling, and validation for spectroscopy-based applications.
 
 
 
 </li>
 </ul>
 
 Production &amp; System Integration
 
 
 
 <ul>
 <li>
 Design and implement production-ready ML pipelines integrated with software applications and hardware systems.
 </li>
 <li>
 Ensure models are maintainable, version-controlled, and deployable within real-world products.
 </li>
 <li>
 Contribute to the design and evolution of ML system architecture and reusable pipeline components.
 </li>
 <li>
 Develop unit and integration tests for ML components to ensure reliability.
 </li>
 <li>
 Document ML modules and interfaces to support long-term maintainability.
 </li>
 <li>
 Work closely with software and firmware teams to integrate ML models into desktop applications, services, and embedded workflows.
 
 
 
 </li>
 </ul>
 
 Tools, Innovation &amp; Growth
 
 
 
 <ul>
 <li>
 Participate in the development of internal and customer-facing ML-driven tools and automation utilities.
 </li>
 <li>
 Stay updated with the latest machine learning research, tools, and industry practices.
 </li>
 <li>
 Contribute to exploratory and applied ML solutions in emerging physical AI systems, including robotics and sensor-driven intelligent platforms.
 </li>
 <li>
 Bachelor&rsquo;s degree in Computer Science, Engineering, Mathematics, or a related field.
 </li>
 <li>
 1 - 4 years of relevant industry or applied ML experience preferred.
 </li>
 <li>
 Strong focus on applied machine learning and engineering implementation.
 </li>
 <li>
 Hands-on experience with Python and its ML ecosystem (NumPy, Pandas, Matplotlib, Scikit-Learn).
 </li>
 <li>
 Familiarity with at least one deep learning framework (PyTorch or TensorFlow).
 </li>
 <li>
 Solid understanding of software engineering principles (modular design, version control, testing, debugging).
 </li>
 <li>
 Experience structuring modular Python codebases.
 </li>
 <li>
 Familiarity with packaging, model serialization, and reproducible pipelines.
 </li>
 <li>
 Experience working within larger software systems (not only notebooks).
 </li>
 <li>
 Strong analytical and problem-solving skills.
 </li>
 <li>
 Good communication skills and ability to work in a collaborative team environment.
 </li>
 <li>
 Proficiency in English (reading and writing).
 
 
 
 </li>
 </ul>
 
 Nice to Have:
 
 
 
 <ul>
 <li>
 Basic understanding of chemometrics concepts (multivariate analysis, regression/classification, spectral data handling).
 </li>
 <li>
 Exposure to spectroscopy data (NIR, IR) and common preprocessing techniques (normalization, smoothing, baseline correction).
 </li>
 <li>
 Experience contributing to ML tools, internal platforms, or data analysis software.
 </li>
 <li>
 Knowledge of MLOps practices (Git, CI/CD, Docker).
 </li>
 <li>
 Experience with cloud platforms (AWS, GCP, Azure).
 </li>
 <li>
 Experience with robotics frameworks (ROS).
 </li>
 <li>
 Experience with sensor fusion.
 </li>
 <li>
 Experience with real-time ML inference.
 </li>
 <li>
 Basic understanding of control systems.
 </li>
 <li>
 Exposure to data visualization or BI tools.
 </li>
 <li>
 Contributions to Kaggle competitions, research projects, or open-source initiatives.
 </li>
 </ul>
</div>

Machine Learning Engineer

BioticsAI

<div>
 
 
 Software Engineer, Backend and Integrations
 
 
 
 
 Employment type:
 
 Part-time, Full-time | Remote.
 
 
 
 
 
 BioticsAI is a San Francisco Bay Area based healthtech startup partnering with medical professionals in the U.S. and Egypt to help bring machine learning to the field of fetal ultrasonography.&nbsp;
 
 
 
 
 
 The BioticsAI team of software engineers, machine learning engineers and doctors have developed algorithms for abnormality detection, anatomical region detection &amp; localization in fetal ultrasound images. BioticsAI is leveraging these algorithms to build products aimed at improving efficiency and minimizing misdiagnoses in clinical settings. These products will have the added bonus of automating time-consuming tasks including doctor-to-doctor documentation, communication and analysis.
 
 
 
 
 
 The Backend and Integrations team works closely with a product manager and the machine learning team to build backend features for the company&rsquo;s BioticsAI for Doctors product. As an early member of this team you will have a major and exciting role in creating significant impact for the BioticsAI for Doctors product.
 
 
 
 
 
 
 Responsibilities:
 
 
 
 
 
 <ul>
 <li>
 Ownership of new backend features including design, documentation, implementation and testing.
 </li>
 <li>
 Ownership of new backend integrations with different relevant medical APIs (for e.g: EHR FHIR APIs, DICOM, DICOMWeb, etc.).
 </li>
 <li>
 Ownership of integrations with the BioticsAI machine learning APIs.
 </li>
 <li>
 Evaluate and implement deployment options using public cloud services like Amazon Web Services and Google Cloud Platform.&nbsp;
 </li>
 <li>
 Actively participate in meetings with product management to help design new features, contribute feedback on prototypes and coordinate backend work.&nbsp;
 </li>
 </ul>
 
 
 
 
 
 Basic Qualifications:
 
 
 
 
 
 <ul>
 <li>
 BS in Computer Science or related technical field or equivalent technical experience.
 </li>
 <li>
 Excellent English communication skills (speaking, technical writing).
 </li>
 <li>
 1+ years of industry experience.
 </li>
 <li>
 Experience in backend web development.&nbsp;
 </li>
 <li>
 Familiarity with the basics of web development, attention to detail and following best practices for building clean code and web services.&nbsp;
 </li>
 </ul>
 
 
 
 
 
 Preferred Qualifications:
 
 
 
 
 
 <ul>
 <li>
 2+ years of relevant work experience.
 </li>
 <li>
 Backend development with Python/Flask
 </li>
 <li>
 Javascript development experience
 </li>
 <li>
 Experience with some or all aspects of the BioticsAI backend stack: Flask, mongo, Docker, Kubernetes, Amazon Web Services products, Google Cloud Platform products.
 </li>
 <li>
 Some familiarity with common healthtech standards: FHIR APIs, HIPAA and DICOM.
 </li>
 <li>
 Experience in designing and building infrastructure and web services at large scale.
 </li>
 </ul>
 
 
 
</div>

Software 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>

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