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AI Engineer at Deloitte

DeloitteLagos, Nigeria Networking and Tech Support
Full Time

Akintola Williams Deloitte is the Deloitte Touche Tohmatsu Limited (DTTL) member firm in Nigeria and the oldest indigenous professional services firm in Nigeria. The firm was established in 1952 by Mr. Akintola Williams, FCA, CFR, CBE, the doyen of the accountancy profession in Nigeria. Our approach to corporate responsibility is shaped by the recognition that, because we are a professional services organization, our impact on society comes in large part from the way they serve clients

Job Description

We are seeking a highly skilled and motivated AI Engineer to join our dynamic team. The ideal candidate will have a strong background in machine learning, deep learning, natural language processing, and experience with various AI frameworks and tools. This role requires hands-on experience in developing and deploying AI models, along with proficiency in programming and cloud technologies.

Key Responsibilities:

  • Design, develop, and implement AI and machine learning models using supervised and unsupervised algorithms, including regression, classification, ensemble models, RNN, LSTM, and GRU.
  • Utilize deep learning algorithms and artificial neural networks to develop predictive and prescriptive analytics solutions.
  • Apply natural language processing (NLP) techniques for text analytics, document AI, OCR, sentiment analysis, entity recognition, and topic modeling.
  • Leverage LangChain and Open LLM frameworks for tasks such as summarization, classification, named entity recognition, and question answering.
  • Develop generative AI techniques, including prompt engineering and working with Vector DB, and various large language models (LLMs) like OpenAI, LlamaIndex, Azure OpenAI, and other open-source LLMs.
  • Gain hands-on experience with Generative AI technologies, including Retrieval-Augmented Generation (RAG) architecture, fine-tuning techniques, and inferencing frameworks.
  • Work with big data technologies and frameworks to manage and analyze large datasets.
  • Utilize Microsoft Azure for building and monitoring CI/CD pipelines, ensuring efficient and reliable deployment of AI models and applications.
  • Collaborate with cross-functional teams to understand business requirements and deliver AI solutions that meet organizational needs.
  • Stay updated with the latest advancements in AI and machine learning technologies, and implement best practices.

Qualifications

  • Bachelor’s degree (B.Sc., B.Eng, B.Tech., HND, etc.) in Computer Science, Engineering, Information Technology, or related field with a minimum of second class upper degree/upper credit.
  • Have minimum of a credit in five (5) O ’levels subjects including Mathematics and English in one sitting only.
  • Strong proficiency in programming languages such as Python and SQL.
  • A minimum of 3 years of experience in predictive and prescriptive analytics, including machine learning and deep learning algorithms.
  • At least 2 years of experience in NLP, text analytics, document AI, OCR, sentiment analysis, entity recognition, and topic modeling.
  • 1+ years of experience in using LangChain and Open LLM frameworks for various NLP tasks.
  • Proficiency in generative AI techniques and tools, including prompt engineering, Vector DB, and large language models.
  • Hands-on experience with generative AI technologies such as RAG architecture, fine-tuning, and inferencing frameworks.
  • Familiarity with big data technologies and frameworks.
  • Sound knowledge of Microsoft Azure, particularly in building and monitoring CI/CD pipelines.
  • Excellent problem-solving skills and the ability to work independently and as part of a team.
  • Strong communication skills and the ability to convey complex technical concepts to non-technical stakeholders.
  • Experience with additional programming languages and tools relevant to AI and machine learning.
  • Familiarity with other cloud platforms and AI frameworks.
  • Published research or contributions to the AI community through open-source projects or academic papers.

Method of Application

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