Land a career in AI with these top artificial intelligence jobs.

Artificial intelligence job prospects have grown in recent years as industry demand has expanded. The rumor that AI will generate a slew of new jobs is correct. Artificial intelligence appears to provide more opportunities than any other profession now available.

We’ve compiled a list of Pepsico Artificial Intelligence Job Openings that you can apply for right now.

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Architect, AI/ML Developer

Location: Hyderabad, Telangana

Responsibilities:

• This role will lead technical development within Artificial Intelligence/Machine Learning for PBNA Advanced Analytics. This will include design, implement, and maintain digital solutions for the PBNA sector.
• Set up and manage the company’s AI development and facilitate production infrastructure.
• Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.

Architect – Robotic Process Automation (RPA) Lead Engineer

Location: Hyderabad, Telangana

Responsibilities:

• As an Automation Lead Engineer, you will be responsible for leading a team to the developer.
• You will work closely with teams to develop, test, implement new and existing automation processes.
• You will be exposed to a wide array of business applications that get tied together with the company’s robots and will create value for the organization by automating business tasks.

Architect – Data Science

Location: India

Responsibilities:

• This role will lead technical development within Artificial Intelligence/Machine Learning for PBNA Advanced Analytics. This will include design, implement, and maintain digital solutions for the PBNA sector. The role is responsible for:
• Set up and manage the company’s AI development and facilitate production infrastructure.
• Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
• Build data ingest and data transformation infrastructure.

Architect, Scrum Master, Azure, Data Science

Location: Hyderabad, Telangana

Responsibilities:

• As part of AMESA/APAC Data and Digital Solutions team, the job requires driving the automation, e-commerce, and Data Science, Data Modelling, Data Ingestion agenda.
• The person will deliver and lead development for EDW, Data Lake, Business Intelligence, Azure, and Advanced Analytics.
• One will be playing the role of scrum master across multiple projects.

Associate Director, SAP Cloud Infrastructure

Location: Hyderabad, Telangana

Responsibilities:

The Associate Director, Cloud Acceleration & Value Office will be responsible for the design, build, and deployment of infrastructure and environments required on private/public cloud for PepsiCo global SAP program – PepsiCo Global Template (PGT). This role is part of PepsiCo’s Acceleration & Cloud Office – a multi-discipline team responsible for architecting and delivering secure, robust, and innovative solutions which would enable the development teams to build and deploy new applications as well as migrate selected existing applications into the public cloud. The candidate will work alongside SAP solution architecture, Basis and Environment, Cloud Operations teams to design, build, deploy and sustain the systems and landscape.

Sr. ML Engineer

Location: Plano, TX

Responsibilities:

• Partner with a data scientist working on discovery, prototypes, and pilot. Focus on experiment tracking
• Partner with data engineers regarding data pipelines and data versioning.
• Coordinate work activities with IT services and as required.
• Drive the use of the Platform toolset.
• Build, deploy or publish machine learning models that run efficiently in cloud pipelines.
• Help drive optimization, testing, and tooling to improve quality (unit tests).

R&D Ana Tech Director (Director, Data Science – AI & ML)

Location: Valhalla, NY

Responsibilities:

• Provide leadership and strategy for AI/ML platforms to support R&D innovations and internal partners.
• Contribute to R&D digital strategy and be a liaison between AI/ML COE, the R&D stakeholders, IT, and vendors.
• Translate business needs and requirements into technical concepts and solutions.
• Work with Business as the AI/ML COE leader to understand the digital technology requirements, define the business problems and deliver solutions.
• Lead, develop, and inspire a high-performing team to build and scale solutions for R&D innovations.

ML Engineer

Location: Cornellà de Llobregat, Catalonia, Spain

Responsibilities:

• Partner with a data scientist working on discovery, prototypes, and pilot. Focus on experiment tracking.
• Work with data engineers regarding data pipelines and data versioning.
• Adopt the best MLOps standards to design and develop scalable end-to-end machine learning pipelines.
• Build and deploy machine learning models that run in cloud pipelines.

The post LATEST ARTIFICIAL INTELLIGENCE JOBS AT PEPSICO YOU CAN APPLY FOR RIGHT NOW appeared first on Artificial Intelligence.

Commentary: We like to imagine an AI-driven future, but it’s machine learning that will actually help us to progress, argues expert Michael I. Jordan.

We bandy about the term “artificial intelligence,” evoking ideas of creative machines anticipating our every whim, though the reality is more banal: “For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations.” This is from Michael I. Jordan, one of the foremost authorities on AI and machine learning, who wants us to get real about AI.

Augmenting people

“People are getting confused about the meaning of AI in discussions of technology trends—that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans. We don’t have that, but people are talking as if we do,” he noted in the IEEE Spectrum article.

Instead, he wrote in an article for Harvard Data Science Review, we should be talking about ML and its possibilities to augment, not replace, human cognition. Jordan calls this “Intelligence Augmentation,” and uses examples like search engines to showcase the possibilities for assisting humans with creative thought.

And, to be clear, machines are much better at some things. For instance, people could do low-level pattern-matching but at a significant cost, whereas machines are able to perform such mundane tasks at relatively little cost. Another example is that ML is broadly used for fraud detection in financial services. We could have people poring over millions upon billions of transactions, but it makes more sense to point computers at the problem.

We know that most AI projects fail. In Jordan’s emphasis on ML over AI, there’s perhaps a clue as to why AI projects fail (inflated expectations) and how to make ML projects succeed (tightly define projects to augment, not supplant, human actors). 

The more we get “real” with AI, in other words, the more likely we’ll find success. Fortunately, Jordan wrote, most of the time when we’re talking about AI, we really mean ML. “ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines to design algorithms that process data, make predictions and help make decisions,” he wrote in the Harvard Data Science Review. ML is essential to “any company in which decisions could be tied to large-scale data,” he added. 

So…the first rule for success in AI is to stop doing AI, and instead consider data science problems as fundamentally about ML, about finding patterns in large quantities of data. It’s not Jetsons, but it’s real.

The post Why machine learning, not artificial intelligence, is the right way forward for data science appeared first on Artificial Intelligence.