Farmerline Group IT Jobs in Accra
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We are seeking a highly skilled and innovative Machine Learning and Data Scientist to drive the evolution of our AI capabilities and support our expanding machine learning initiatives. In this role, you will focus on developing state-of-the-art Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) models tailored for low-resourced languages, addressing global challenges in linguistic diversity. Additionally, you will contribute to a wide range of AI-driven projects, including SME credit scoring, custom training of large language models (LLMs), and advanced predictive analytics.
As part of our team, you will leverage modern tools, frameworks, and techniques to build scalable, efficient, and explainable AI solutions. You will collaborate closely with multidisciplinary teams, ensuring that our models not only meet technical and business objectives but also deliver meaningful impact to our users and stakeholders.
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Responsibilities
- Build and optimize advanced Text-to-Speech (TTS) systems to deliver natural, high-quality speech synthesis for underrepresented languages, enhancing accessibility and inclusivity.
- Work closely with linguists, engineers, and data scientists to build diverse datasets and address challenges in speech recognition and synthesis for low-resourced languages.
- Lead machine learning efforts to develop and refine predictive models for SME credit scoring, incorporating explainability and fairness in algorithm design.
- Contribute to custom training of Large Language Models (LLMs) by fine-tuning models on domain-specific datasets and implementing efficient training pipelines.
- Stay ahead of AI trends by researching and experimenting with state-of-the-art algorithms, including neural architectures, transformers, and multimodal approaches, to enhance model performance.
- Develop, manage, and optimize data pipelines to enable seamless training, evaluation, and deployment of machine learning models.
- Analyze and interpret complex, large-scale datasets to derive actionable insights and inform AI-driven decision-making processes across the organization.
- Ensure deployed models are scalable, efficient, and reliable by monitoring performance, identifying potential issues, and implementing necessary improvements.
- Effectively communicate findings, progress, and technical concepts to both technical and non-technical stakeholders, bridging gaps between AI capabilities and business objectives.
Required Qualifications
- Bachelor’s degree or higher in Computer Science, Engineering, Data Science, or a related field. Advanced degrees (Master’s or Ph.D.) in a relevant discipline are a plus.
- Strong experience with modern machine learning frameworks (e.g., TensorFlow, PyTorch, Keras, Hugging Face Transformers, NVIDIA NeMo), including their deployment and optimization in production environments.
- Advanced skills in Python and SQL, with a focus on writing efficient, maintainable, and scalable code. Familiarity with additional languages like R or Julia is a plus.
- Demonstrated expertise in speech recognition (ASR) and speech synthesis (TTS), particularly in developing solutions for low-resource languages.
- In-depth understanding of Natural Language Processing (NLP) techniques and hands-on experience in fine-tuning and deploying custom Large Language Models (LLMs).
- Proven experience in leveraging techniques such as deep learning, transfer learning, reinforcement learning, and multimodal learning for diverse applications.
- Proficiency in working with large-scale datasets, including preprocessing, augmentation, and feature engineering, particularly for low-resource language data.
- Experience with cloud platforms (AWS, GCP, Azure) for deploying, scaling, and managing machine learning models. Familiarity with containerization tools like Docker and orchestration tools like Kubernetes is a strong advantage.
- Knowledge of ethical considerations in AI, including fairness, bias mitigation, and explainability in machine learning models.
- Strong analytical and problem-solving skills, with the ability to work autonomously and adapt to a fast-paced, dynamic environment.
- Excellent written and verbal communication skills, with the ability to explain technical concepts to technical and non-technical stakeholders alike.
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1. Patiently scroll down and read the job description below.
2. Scroll down and find how to apply or mode of application for this job after the job description.
3. Carefully follow the instructions on how to apply.
4. Always apply for a job by attaching CV with a Cover Letter / Application Letter.
We are seeking a highly skilled and innovative Machine Learning and Data Scientist to drive the evolution of our AI capabilities and support our expanding machine learning initiatives. In this role, you will focus on developing state-of-the-art Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) models tailored for low-resourced languages, addressing global challenges in linguistic diversity. Additionally, you will contribute to a wide range of AI-driven projects, including SME credit scoring, custom training of large language models (LLMs), and advanced predictive analytics.
As part of our team, you will leverage modern tools, frameworks, and techniques to build scalable, efficient, and explainable AI solutions. You will collaborate closely with multidisciplinary teams, ensuring that our models not only meet technical and business objectives but also deliver meaningful impact to our users and stakeholders.
ADVERTISEMENT - CONTINUE READING BELOW
Responsibilities
- Build and optimize advanced Text-to-Speech (TTS) systems to deliver natural, high-quality speech synthesis for underrepresented languages, enhancing accessibility and inclusivity.
- Work closely with linguists, engineers, and data scientists to build diverse datasets and address challenges in speech recognition and synthesis for low-resourced languages.
- Lead machine learning efforts to develop and refine predictive models for SME credit scoring, incorporating explainability and fairness in algorithm design.
- Contribute to custom training of Large Language Models (LLMs) by fine-tuning models on domain-specific datasets and implementing efficient training pipelines.
- Stay ahead of AI trends by researching and experimenting with state-of-the-art algorithms, including neural architectures, transformers, and multimodal approaches, to enhance model performance.
- Develop, manage, and optimize data pipelines to enable seamless training, evaluation, and deployment of machine learning models.
- Analyze and interpret complex, large-scale datasets to derive actionable insights and inform AI-driven decision-making processes across the organization.
- Ensure deployed models are scalable, efficient, and reliable by monitoring performance, identifying potential issues, and implementing necessary improvements.
- Effectively communicate findings, progress, and technical concepts to both technical and non-technical stakeholders, bridging gaps between AI capabilities and business objectives.
Required Qualifications
- Bachelor’s degree or higher in Computer Science, Engineering, Data Science, or a related field. Advanced degrees (Master’s or Ph.D.) in a relevant discipline are a plus.
- Strong experience with modern machine learning frameworks (e.g., TensorFlow, PyTorch, Keras, Hugging Face Transformers, NVIDIA NeMo), including their deployment and optimization in production environments.
- Advanced skills in Python and SQL, with a focus on writing efficient, maintainable, and scalable code. Familiarity with additional languages like R or Julia is a plus.
- Demonstrated expertise in speech recognition (ASR) and speech synthesis (TTS), particularly in developing solutions for low-resource languages.
- In-depth understanding of Natural Language Processing (NLP) techniques and hands-on experience in fine-tuning and deploying custom Large Language Models (LLMs).
- Proven experience in leveraging techniques such as deep learning, transfer learning, reinforcement learning, and multimodal learning for diverse applications.
- Proficiency in working with large-scale datasets, including preprocessing, augmentation, and feature engineering, particularly for low-resource language data.
- Experience with cloud platforms (AWS, GCP, Azure) for deploying, scaling, and managing machine learning models. Familiarity with containerization tools like Docker and orchestration tools like Kubernetes is a strong advantage.
- Knowledge of ethical considerations in AI, including fairness, bias mitigation, and explainability in machine learning models.
- Strong analytical and problem-solving skills, with the ability to work autonomously and adapt to a fast-paced, dynamic environment.
- Excellent written and verbal communication skills, with the ability to explain technical concepts to technical and non-technical stakeholders alike.