Main Responsibilities and Required Skills for AI Engineer
An AI Engineer is a professional who designs and implements AI models. They analyze, assess, and improve software elements for AI and Big Data services. In this blog post we describe the primary responsibilities and the most in-demand hard and soft skills for AI Engineers.
Get market insights and compare skills for other jobs here.
Main Responsibilities of AI Engineer
The following list describes the typical responsibilities of an AI Engineer:
Adhere to
Adhere to security and data protection policies.
Analyze
Analyze the software requirements and software elements for system design.
Collaborate
Collaborate well across the organization and with external partners.
Collaborate with the animation team to continue improving the visual fidelity of our AI.
Collaborate with traders and subject matter experts across the trade floor.
Communicate
Communicate to stakeholders outside the team project status & risks.
Configure
Configure intents, entities and utterances for conversations.
Connect
Connect and manage IoT devices, networks and gateways.
Contribute to
Contribute ideas for new AI architectures and technologies.
Contribute to discussions where the GNW LT establishes a vision.
Contribute to our company-wide python training and product codebases.
Contribute to software development at the product or platform level.
Contribute to team effort by Working with internal staff to resolve issues.
Convert
Convert Python based ML scripts to production quality level applications.
Create
Create APIs and help business customers put results of the AI models into operations.
Define
Define and lis a professional who designs and implements AI modelsaunch video data collection campaigns to satisfy defined needs.
Define the architecture to address functional, non-functional requirements and pain points.
Deploy
Deploy embedded computing and edge analytics to IoT systems.
Design
Design and configure the Dialog Component in Google DialogFlow.
Design and implement AI models for medical imaging.
Design medical image processing algorithms and systems.
Develop
Develop and maintain data ontologies for key market segments.
Develop, enhance and debug new and existing edge devices in C / C++ and Python.
Develop ML pipelines that can evolve rapidly to handle new technologies and modeling approaches.
Develop novel mechanisms by which to optimize supply chain.
Drive
Drive machine learning algorithms and data science solutions for integrated IoT and Cloud systems.
Establish
Establish the business logic within the use cases and connect it to Genesys Intelligent Automation.
Extend
Extend existing ML libraries and frameworks.
Help
Help automate our workflows.
Help design and improve our cloud-based computation environments.
Implement
Implement continuous improvements and best practices within the team, and larger organization.
Keep
Keep abreast with latest AI tools relevant to our business domain.
Lead
Lead in analyzing the software requirements and software elements for Big Data Platform design.
Lead in development of Big Data Platform incorporation with existing services.
Lead technical discussions and mentor junior team members.
Leverage
Leverage best practices in continuous integration and delivery, with a strong commitment to quality.
Leverage existing technical skillsets, develop new ones, or work with consultants to drive results.
Maintain
Maintain a repository of materials & information that document our work to educate senior leaders.
Orchestrate
Orchestrate life cycle management of AI Models.
Own
Own assignments and take full accountability for overall team success.
Participate
Participate in different open source and standard meetings to present solutions.
Perform
Perform statistical analysis and fine-tuning using test results.
Promote
Promote process continuous improvement through the discipline of integrated lean six sigma.
Read
Read the scientific literature.
Research
Research competing platforms and industry trends.
Research, design new ideas and develop them in real world.
Review
Review processes and recommend changes to improve operations.
Scope
Scope will be end to end and inclusive of all functions.
Secure
Secure IoT data with Blockchain.
Solve
Solve problems using mechanistic, bottom-up thinking and statistical, top-down approaches.
Take
Take charge in collaborative work with various universities.
Track
Track record of diving into data to discover hidden patterns.
Train
Train and mentor junior team members.
Train and retrain systems when necessary.
Understand
Understand and maintain data pipelines from raw sources to feature stores for models.
Use
Use of analytics to guide behavior.
Use Scala, Spark, GitHub, Maven, Jenkins and Airflow to develop and deploy AI models.
Work with
Work closely with fellow senior engineers, senior management, product management and customers.
Work closely with the design and content teams to bring new AI characters and behaviors to life.
Work closely with the other teams to ensure architectural integrity.
Work with cross-functional teams to integrate AI-based solutions into production SW Stack.
Work with imaging scientists to define requirements and identify suitable algorithms and libraries.
Work with the AI engineering team to craft best-in-class AI technology for our next AAA title.
Most In-demand Hard Skills
The following list describes the most required technical skills of an AI Engineer:
Natural Language Processing
Kafka
Pytorch
Storm
Java
Statistics
C
C++
Electrical Engineering
Linux
Atlassian Suite
Automated Machine Learning
BI
Bitbucket
Breeze
Business Intelligence Visualization Tools
Confluence
Confluent
Flat Files
Functional Components
Grafana
Hadoop Big Data Platform
Jira
Keras
Micro-Batch Application Development
MIS
Nifi
Predictive Analytics
Pyspark
Redash
Relational Database Structures
Spark Streaming
SQL
Streaming
Superset
Tableau
Translate Into Modular
Unix Operating Systems
Most In-demand Soft Skills
The following list describes the most required soft skills of an AI Engineer:
Written and oral communication skills
Collaborative
Solve deep technical problems
Leadership
Team player
