Technology

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Machine Learning Engineers have expertise in statistics and programming, excels at managing large datasets, and can adeptly build, test, and deploy machine learning models. They keep up-to-date with AI advancements and communicate complex concepts clearly.

A general AI engineer is a professional skilled in designing, developing, and implementing artificial intelligence systems. They have a broad understanding of various AI domains, including machine learning, deep learning, natural language processing, and robotics. Their expertise often extends to programming in languages like Python and using AI frameworks such as TensorFlow or PyTorch. They approach problem-solving methodically, can work across different teams, and stay current with AI trends and innovations.

Data Science Engineer combines expertise in statistics, machine learning, and software engineering to analysis complex data and extract actionable insights. They're adept at data wrangling, visualisation, and building predictive models using programming tools and creating robust data pipelines for analysis.

Natural Language Processing (NLP) Engineer are specialists in building systems that understand and interpret human language.

A Gen AI Tech Lead is a strategic innovator and dynamic team leader, expertly navigating the complexities of artificial intelligence to drive technological advancement. With robust technical acumen and a forward-thinking mindset, they orchestrate the development and deployment of sophisticated AI systems, ensuring that projects align with business goals and ethical standards. By fostering collaboration and ingenuity within their teams, the Tech Lead plays a pivotal role in transforming AI concepts into real-world solutions that enhance efficiency, creativity, and decision-making in various industries.

A software engineer with Generative AI experience specialises in creating algorithms that produce new, synthetic data. They are skilled in machine learning, data processing, and various programming languages, often using these skills to innovate in fields like content creation and design.

A DevOps engineer bridges the gap between development and operations teams, ensuring seamless integration, continuous delivery, and automation of processes. They focus on developing and maintaining efficient CI/CD pipelines, infrastructure automation, and monitoring systems.

- Implementing CI/CD workflows

- Managing infrastructure as code

- Automating deployment and scaling

- Monitoring system performance and reliability

- Facilitating collaboration between development and operations teams

Tech Stacks:

- CI/CD: Jenkins, GitLab CI, CircleCI

- Configuration Management:Ansible, Puppet, Chef

Containerisation: Docker, Kubernetes

Cloud Platforms: AWS, Azure, GCP

Infrastructure as Code: Terraform, CloudFormation

Version Control: Git, GitHub, Bitbucket

Monitoring and Logging:Prometheus, ELK Stack (Elasticsearch, Logstash, Kibana), Grafana