Responsibilities:

• Design and develop production-grade, end-to-end ML systems — encompassing automated data ingestion, feature extraction, model training, inference, recommendation engine, deployment, and monitoring.
• Refactor and harden Python-based ML and optimisation codebases into clean, modular, and easily testable services.
• Apply object-oriented principles and design patterns to create clean, reusable ML components.
• Collaborate with data scientists to translate research code into production-ready solutions.
• Develop test strategies and CI/CD workflows for ML components.
• Own the quality and reliability of ML services in production environments.
• Work with AI teams to adopt modern MLOps practices, including ML lifecycle management, monitoring, and deployment automation.

Requirements & Skills:

• 8+ years of professional experience as a software engineer, with a strong track record in building scalable, reliable, and maintainable systems.
• A degree in Computer Science, Software Engineering, Data Engineering, or a related field.
• Proven experience in working with ML systems or data engineering pipelines.
• Strong knowledge of object-oriented programming principles, design patterns, and software architecture.
• Expert-level proficiency in Python programming, along with ML frameworks and numerical computing tools (e.g., Pandas, Polars, Scikit-learn, PyTorch, TensorFlow, SciPy), and the ability to optimise models and code for speed and accuracy.
• Experienced with data orchestration tools such as Kedro, Airflow, or Dagster for developing data/ML pipelines.
• Practical experience in deploying ML models to production, with a strong understanding of core MLOps concepts including version control, CI/CD pipelines for ML, automated testing, monitoring, and end-to-end model lifecycle management.
• Proficient with containerisation and orchestration tools like Docker and Kubernetes.
• Practical experience with cloud platforms for data and ML (e.g., AWS, Azure, GCP) — AWS/SageMaker/MLflow is a plus.
• Background in mining, industrial applications, or resource-focused ML environments is desirable.

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Responsibilities:

• Write and test product or system development code.
• Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
• Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
• Design and implement GenAI solutions, leverage ML infrastructure, and evaluate tradeoffs between different techniques and their application domains.

Requirements & Skills:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages, and with data structures/algorithms.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• 3 years of experience with state-of-the-art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
• Master’s degree or PhD in Computer Science or a related technical field.
• 1 year of experience in a technical leadership role.
• Experience developing accessible technologies.

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Responsibilities:

• Design and implement robust pipelines to deploy machine learning models into production environments.
• Build and optimize scalable infrastructure for machine learning workflows, including data preprocessing, model training, and inference.
• Optimize models for latency, accuracy, and efficiency in real-world scenarios.
• Work closely with data scientists to transition research models into production-grade solutions.
• Develop and maintain automated workflows for version control, model monitoring, and retraining.
• Collaborate with data engineering teams to ensure efficient data pipelines and availability of high-quality datasets.
• Build tools to streamline machine learning development and deployment processes.
• Ensure deployed solutions adhere to responsible AI principles, focusing on safety.

Requirements & Skills:

• Bachelor’s or Master’s degree in Computer Science, Data Science, or AI-related fields
• Proficiency in Python and frameworks such as Tensorflow, PyTorch, or Scikit-learn.
• Experience deploying ML models in cloud environments like AWS, GCP, or Azure
• Familiarity with containerization tools (e.g., Docker) and orchestration frameworks (e.g., Kubernetes)
• Expertise in MLOps tools and practices, including CI/CD pipelines, model versioning, and monitoring
• Strong understanding of distributed systems and scaling machine learning workflows
• Solid knowledge of machine learning algorithms, model training, and evaluation techniques
• Experience working with NLP, computer vision, or generative AI models is a plus
• Ability to work in cross-functional teams and communicate effectively with data scientists, engineers, and business stakeholders.
• Strong debugging and problem-solving skills to address technical challenges in deployment environments.

The post AI/ML Engineer, Vanguard appeared first on AI Jobs Latest.

Responsibilities:

• Work with the senior engineering leaders, product management, and others to set the technical vision for the Meraki Assurance ML/AI roadmap.
• Guide the ML engineering team through defining short, medium, and long-term goals to achieve the vision.
• Help with the recruiting strategy to build a diverse and inclusive team: driving decisions on who the team needs to hire.
• Mentor and coach engineers through technical challenges.
• Work on projects that span numerous networking domains and systems to help our ML solutions scale by handling enormous amounts of data.

Requirements & Skills:

• 12+ years of professional experience in machine learning, data engineering, software engineering, Dev/Ops, or related fields.
• Experience in building large-scale AI models and systems using MLOps practices, including deep learning models using frameworks such as Tensorflow, Keras, and PyTorch
• Experience working in a team to write robust, maintainable, and extendable code in Python and/or R.
• Experience with complex data sets, ETL pipelines, SQL, and general data engineering
• Expertise with cloud architecture and technologies like Amazon Web Services, Spark, Kubernetes, Docker, Jenkins, and Terraform

Bonus points:

• Experience building scalable and production-ready Generative AI solutions
• Have an understanding of real-time technologies such as Kafka, ClickHouse.

The post Lead Software Engineer – AI/ML, Cisco appeared first on AI Jobs Latest.

Responsibilities:

• Performing initial level analysis and troubleshooting issues in Apache Spark using Apache Spark UI metrics, DAG, Event Logs for multiple customer-reported job slowness issues.
• Troubleshoot, resolve, and suggest deep code-level analysis of Apache Spark to address customer issues related to Apache Spark core internals, Apache Spark SQL, Structured Streaming, Delta, Lakehouse, and other Databricks runtime features.
• Assist the customers in setting up reproducible Apache Spark problems with solutions in the areas of Apache Spark SQL, Delta, Memory Management, Performance tuning, Streaming, Data Science, and Data Integration areas in Apache Spark.
• Participate in the Designated Solutions Engineer program and guide one or two of the strategic customers’ daily Apache Spark and Cloud issues.
• Coordinate with Account Executives, Customer Success Engineers, and Resident Solution Architects for coordinating the customer issues and best practices guidelines.
• Participate in screen sharing meetings, answering Slack channel conversations with our team members and customers, helping in driving the major Apache Sparkissues at an individual contributor level.
• Build an internal wiki, a knowledge base with technical documentation, manuals for the support team, and for the customers. Help create company documentation and knowledge base articles.
• Coordinate with Engineering and Backline Support teams to help report product defects.
• Participate in weekend and weekday on-call rotation and run escalations during databricks runtime outages, incident situations, and plan day-to-day activities, and provide an escalated level of support for important customer operational issues.

Requirements & Skills:

• 3 years of hands-on experience developing any two or more of the Big Data, Hadoop, Apache Spark, Machine Learning, Artificial Intelligence, Streaming, Kafka, Data Science, ElasticSearch related industry use cases at the production scale. Spark experience is mandatory.
• Experience in the performance tuning/troubleshooting of Hive and Apache Spark-based applications at production scale.
• Real-time experience in JVM and Memory Management techniques such as Garbage collections, Heap/Thread Dump Analysis.
• Experience with any SQL-based databases, Data Warehousing/ETL technologies like Informatica, DataStage, Oracle, Teradata, SQL Server, MySQL, and SCD type use cases.
• Experience with AWS or Azure, or GCP
• Written and spoken proficiency in both Korean and English

The post Senior Technical Solutions Engineer, Databricks appeared first on AI Jobs Latest.

Responsibilities:

• Lead efforts to drive the design, development, prototype, and production of ML systems that address the customer needs
• Collaborate with the research team on developing the next generation of models, where you may assist with areas such as optimization of model training, model fine-tuning, dataset engineering, tooling, open-source efforts, etc.
• Work on the commercial side, productionizing generative model-based solutions, by partnering with platform and research teams, building the infrastructure to serve them at scale
• Write production-quality code and influence the next generation of Stability AI’s machine learning solutions
• Advocate for engineering best practices and upskill the team

Requirements & Skills:

• MS or PhD in Computer Science
• 1+ years of experience working on machine learning projects
• Familiar with diffusion models
• Proficiency with Python scientific stack, PyTorch, creating Jupyter/Colab notebooks
• Strong coding skills and comfortable with coding in production platforms
• Ability to diagnose technical problems, debug code, and automate manual routine tasks
• Ability to communicate machine learning concepts and results effectively through writing and visualization
• Track record of publication in top-tier machine learning conferences

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