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Shipping a consumer facing AI product requires a complementary set of skillsets than what is required for setting up an AI inference pipeline / EDA dashboard for internal business stakeholders. If the aim is to differentiate your business on the basis of AI product offerings AND intend to take data backed smart decisions to drive day to day business decisions at the same time, both set of skillsets are equally important. Think of the following roles: - Data Engineer (Data Platform) - Analytics Engineer (BI & Automations) - Data Scientist (Research, EDA, inference pipelines, model CI pipelines) - AI / ML Engineer (Consumer facing Data Products) - Allied Roles (DevSecOps, SRE, PM, QA, Design, UX, Frontend) (Did I miss any? Add in the comments) The lines between each of these adjacent roles are extremely blurred, sometimes invisible - owing to the aim of businesses to have Data Generalists who could do anything and everything related to data. However there's always a trade-off that most businesses don't consider while asking their junior data scientists to expose mind-boggling inference results they saw during an internal meeting on a Data Scientist's Notebook to end consumers. What do you think these businesses are missing to consider? #foodforthought #aiproducts #datascience #analytics #ai #ml #data

I remember some years ago, with the hype of data science, organizations believed that if they did not employ data scientists, they would definitely lose the game. The market changed suddenly, and many people updated their LinkedIn profiles overnight to "Data Scientist," and organizations started hiring Data Scientists. Organizations then started doing science with the data, and the results were amazing. The next step was to deploy those experiments to production or to automate the whole process. Exactly at this point, organizations recognized that somebody was missing: the Data Engineer. The hardworking person in the backend, who is rarely appreciated and often forgotten because they work behind the scenes. What were the problems at that time? 1. Data scientists had used either sample data to do experiments or completely isolated data. 2. The models then needed to be integrated into the whole enterprise architecture. 3. Those models needed to be fed with reliable data. Data scientists could not do that; it was beyond the scope of their work. Many organizations then had a team of data scientists with inadequate data engineers (if any). The history seems to be repeating itself with AI. Again, organizations are massively employing AI engineers. AI engineers then create some isolated chatbots or other AI solutions. Such solutions are mostly isolated from enterprise production architecture, and after some time, the reliability of input data is left to the gods of bad data. I just wanted to use this chance to thank all the data engineers from the bottom of my heart and let them know that I am sure after the AI dust settles down, you backend guys will be more appreciated AGAIN. ;-) #dataai #advisory #datastrategy #aistrategy #dataplatform #dataarchitecture

#hiring Senior Machine Learning Engineer, Houston, United States, fulltime #jobs #jobseekers #careers #Houstonjobs #Texasjobs #ITCommunications Apply: https://lnkd.in/dwkbFGDK External Description:LIVING OUR VALUESAll associates at The Friedkin Group are guided by Our Values, which are the unifying foundation of our companies. We strive to ensure that every decision we make and every action we take demonstrates Our Values. We believe that putting Our Values into practice creates lasting benefits for all our associates, shareholders, and the communities in which we live.JOB SUMMARYAs a Senior Machine Learning Engineer, you will work on building AI/ML solutions across a wide range of business applications within The Friedkin Group of companies. You will drive the development and deployment of state-of-the-art AI services and analytic applications that support the needs of our business. We are looking for a driven and talented individual who has a strong background in software engineering and a deep understanding of AI/ML frameworks. You will work closely with data scientists, machine learning engineers, and other software engineers, using the latest tools and technologies to develop analytic solutions and integrate analytic models with existing business applications.ESSENTIAL FUNCTIONSWork closely with product managers to understand business requirements and translate them into technical solutions.Collaborate with data scientists, data engineers, data analysts, software engineers, IT specialists, and stakeholders to expand effective use of AI applications.Collaborate with cross-functional teams to design, develop, and maintain highly complex AI/ML systems.Develop and implement AI/ML interfaces, services, and analytic applications to support the company's initiatives and projects.Deploy machine learning models into production environments, ensuring scalability, reliability, and real-time performance. This may involve containerization, API development, and integration with existing systems.Optimize machine learning algorithms and infrastructure for performance, scalability, and cost-efficiency. This may involve parallelization, distributed computing, and resource management.Develop User Interfaces

Glad to meet you all with an new article on AI architect!! AI architects work in the field of information technology to develop and implement infrastructure for applications, databases, and computer networks. When it comes to governing and scaling AI efforts, they serve as the connecting tissue between data analysts, database administrators, programmers, operators (DevOps, DataOps, MLOps), and business unit executives. AI Architect Job Role: 1. Due to the fact that AI has a wide variety of deployment patterns and use cases, AI architects need to be capable of performing the following duties: 2.Assist digital transformation initiatives with the help of data scientists and AI experts by finding and testing use cases. Consult with business stakeholders on the viability of use cases and architectural style to help transform the objective of business executives into a technological execution that can be achieved. Also, draw people's attention to efforts that aren't complementary or use cases that won't work. 3.Gather feedback from a wide variety of parties, including corporate customers, data analysts, security specialists, data engineers and strategists, and the IT operations department, and use that information to shape the procedures and final products so that they are in line with current and future needs. Take the lead in deciding the open-source and commercial tools to use to build the AI and design its architecture. Choose a deployment type (cloud, on-premises, or hybrid) and make sure the new tools work well with the ones that are already there for data management and analytics. #snsinstitutions #snsdesignthinkers #designthinking

Confused About the Ecosystem of AI Jobs? Here's a guide to help you out : 1. Data Analysis & Business Intelligence: Roles: Data Analyst, BI Analyst, Business Analyst, Marketing Analyst, and more. Focus: Analyzing data, developing strategies, and optimizing performance. 2. Data Science & Machine Learning: Roles: Data Scientist, Machine Learning Engineer, Deep Learning Engineer, Predictive Modeler, and more. Focus: Developing algorithms, building models, and extracting insights from data. 3. AI & Robotics: Roles: AI Developer, AI Research Scientist, Robotics Engineer, Autonomous Systems Engineer, and more. Focus: Creating AI applications, designing robotic systems, and advancing AI technologies. 4. Data Engineering & Management: Roles: Data Engineer, Data Architect, Database Administrator, Data Governance Manager, and more. Focus: Designing data systems, managing data quality, and ensuring data privacy. 5. Research and Development: Roles: AI Research Scientist, Computational Linguist, Quantum Machine Learning Researcher, and more. Focus: Conducting AI research, developing computational models, and exploring emerging technologies. [Explore more in the post] If you found this helpful don’t forget to save this for later and comment your thoughts. Join my newsletter or send me a DM "Newsletter” for more posts like this. Follow Denis Panjuta on Linkedin : https://lnkd.in/eUHjTBUi

🧠 The Evolving Data Science Landscape: Who's Who? Recently, I was asked if our new GenAI Engineer would join my team. The answer? No - they'll be part of our data science team. This got me thinking about how we traditionally structure our data teams. Here's my current view: ✅ Data Scientist: The statistical backbone ✅ Data Engineer: The infrastructure builder ✅ Data Analyst: The insight extractor ✅ Machine Learning Engineer: The model deployer ✅ MLOps Engineer: The ML lifecycle automator ✅ Platform Engineer: The scalable foundation provider ✅ Research Scientist: The algorithm innovator Remember: It's not about titles, but the value each role brings. Flexibility and cross-collaboration are key! But here's the burning question: How do emerging AI roles fit into this established framework? We're seeing flashy new titles everywhere, but do we truly understand what's needed? What's your take? How is your organization integrating new AI roles? #DataScience #AIEngineering #FutureOfWork

Hiring...Hiring.... Job Title: Sr. Machine Learning Software Engineer Location: Remote OK but Locals to Cincinnati, OH HIGHLY PREFERRED.  Duration: 6 Month Contract + Possible extension USC/GC Email: akhil@aptoninc.com   Job Description: 6 month contract to hire (must be USC/GC Perm)   Must PASS video prescreening within 24 hours. Mention exact interview availability in submittal email.     Tech Stack/Requirements: on Key Responsibilities • Maintain expertise in a range of ML technologies and platforms, with a preference for Google Vertex AI, but open to other systems as needed. • Leverage support for open-source frameworks like TensorFlow, PyTorch, scikit-learn, and integrate them with ML frameworks via custom containers. • Stay updated with the latest trends in MLOps and ML technologies. Recommender System Design and Development: • Hands-on experience working on recommender systems, drawing from ML techniques such as embedding based retrieval, reinforcement learning, transformers, and LLMs. • Software engineering skills to work with teams integrating the recommender systems into customer facing products. • Experience in AB testing and iterative optimization using data driven approaches. • Understanding of infrastructure needs required to deploy ML systems (CPU/GPU, networking infrastructure). Feature Store Management: • Efficiently manage, share, and reuse machine learning features at scale using Vertex AI Feature Store. • Implement feature stores as a central repository for maintaining transparency in ML operations across the organization. • Enable feature delivery with endpoint exposure while maintaining authority and security features. Data Management and Collaboration: • Assist as needed with data labeling and management, ensuring high-quality data for ML models. • Collaborate with data engineers and data scientists to ensure the integrity and efficiency of data used in ML models. • Ensure end-to-end integration for data to AI, including the use of BigTable / BigQuery for executing machine learning models on business intelligence tools. Continuous Monitoring and Optimization: • Monitor ML systems in production, identify improvement opportunities, and implement optimizations. • Participate in support rotations and participate in support calls as necessary.  

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Machine Learning Engineer vs Data Scientist: Decoding the Differences In the rapidly evolving tech landscape, two roles often cause confusion: Machine Learning (ML) Engineer and Data Scientist. While both work with data and AI, their focus and responsibilities differ significantly. Let's break it do ● Data Scientist: The Explorer • Focus: Analyzing data to extract insights and create predictive models • Key Responsibilities: - Data cleaning and preprocessing - Exploratory data analysis - Statistical modeling and hypothesis testing - Data visualization - Communicating insights to stakeholders • Core Skills: - Strong statistical knowledge - Proficiency in Python/R and SQL - Data visualization tools - Business acumen ● Machine Learning Engineer: The Builder • Focus: Designing, building, and deploying scalable ML systems • Key Responsibilities: - Developing and optimizing ML algorithms - Creating data pipelines - Deploying models to production - Monitoring and maintaining ML systems - Scaling ML solutions • Core Skills: - Software engineering expertise - Proficiency in ML frameworks (TensorFlow, PyTorch) - Cloud platforms (AWS, GCP, Azure) - System design and architecture ● Collaboration and Overlap • Data Scientists and ML Engineers often work together on projects • A Data Scientist might develop a prototype model, which an ML Engineer then scales and deploys • Both roles require an understanding of ML algorithms and programming skills ● Career Implications • Data Scientists drive innovation through data-driven insights • ML Engineers bring these innovations to life at scale • Understanding the distinction helps professionals chart their career paths • Companies benefit from having both roles to build effective data teams As AI continues to reshape industries, both Data Scientists and Machine Learning Engineers play crucial, complementary roles in leveraging data for business success. #MachineLearningEngineer #DataScientist #MLvsDataScience #AIcareers #TechRolesComparison #DataScience #MachineLearning #CareerInTech #MLEngineering #DataScienceInsights #ArtificialIntelligence #TechIndustry #MLvsDS #DataDrivenDecisions #AIandDataScience #DanishAmmar Note:image used in this blog is ai generated

#ML #MLDeveloper #MachineLearning #SeniorML #USA #Remote #Job "Senior Machine Learning Software Engineer" We’re looking to hire our first machine learning engineer as we expand our data activation products to include an intelligence layer. While hundreds of companies sync data in SaaS systems to automate and improve operations, there’s a lot of surface area we haven’t touched in helping companies figuring out which customers to message, what content to put in messages, and when to send messages. A lot of this work today is done manually through intuition and guesswork, and we believe that adding machine learning could have a step function impact for our customers. And given our access to data warehouses and databases, this role will help to make use of a company’s customer data in building a powerful intelligence layer. Some of the problems we’ll be working on include: - Personalization and Product Recommendation: There are often many options for what content a company could message a user with, including which products to show from catalogues. Given this large state space, how can Hightouch help personalize messages with the most relevant content for each user? - Automated Experimentation: Helping companies intelligently navigate and automate experiments across the extensive number of options for messaging customers. - Predictive Audiences: Building models to predict which users are most likely to convert, churn, or take desired actions. - Content Generation: Particularly with recent advances in LLMs, how can we help marketers generate text, images, and creatives that are compelling to their customers? - Budget Optimization: Helping companies assess which marketing spend is driving the most incremental conversions, and where the marginal CAC is lowest. As our founding machine learning engineer, you will help build comprehensive solutions to the above domains from scratch. Responsibilities will be highly varied and include working on customer research, problem definition, predictive modeling, machine learning infrastructure, and partnering with customers.