Data Engineering Analyst- Machine Learning Engineer

Employment Type

Permanent

Closing Date

30 Aug 2024 11:59pm

Job Title

Data Engineering Analyst- Machine Learning Engineer

Job Summary

As a Data Engineering Analyst, you create and provide access to high quality and reliable data solutions. In collaboration with your colleagues you deliver and develop best practice data solutions and pipelines. You are known for the integrity and accuracy of data that enables quality data-driven business decisions and equip Telstra to deliver better customer and business outcomes. In a DevOps model, you will develop the data pipelines using Continuous Integration; Continuous Deployment (CICD) techniques.
Job Description

We are seeking a talented and motivated Machine Learning Engineer to join our team. The ideal candidate will have a strong background in machine learning, data science, and software engineering. You will be responsible for designing, developing, and deploying machine learning models to support a variety of applications and business needs.

Key Responsibilities:
 Model Development: Design, implement, and train machine learning models and algorithms to solve real-world problems. This includes feature engineering, model selection, and hyperparameter tuning.

Design, implement, and train advanced deep learning models and large language models (e.g., GPT, BERT) to address complex problems and enhance product functionality.

Design, implement, and fine-tune Retrieval-Augmented Generation (RAG) models to improve information retrieval and generate contextually relevant responses.
 Data Management: Collect, preprocess, and analyze large datasets. Ensure data quality and integrity throughout the data pipeline.
 Algorithm Optimization: Continuously improve and optimize algorithms for performance, accuracy, and efficiency.
 Deployment and Integration: Deploy machine learning models into production environments. Collaborate with software engineers to integrate models into applications and systems.Deploy deep learning and LLMs into production environments. Collaborate with engineering teams to integrate models into applications and ensure seamless functionality.
 Research and Innovation: Stay current with the latest advancements in machine learning and artificial intelligence. Apply cutting-edge techniques to enhance our products and services.Stay abreast of the latest advancements in deep learning and natural language processing. Apply novel techniques and research findings to develop innovative solutions.Stay updated on the latest advancements in RAG and related technologies. Apply new techniques to enhance the effectiveness and efficiency of RAG models.
 Collaboration: Work closely with cross-functional teams including data scientists, engineers, and product managers to understand requirements and deliver solutions that meet business goals.
 Documentation and Reporting: Document processes, methodologies, and model performance. Communicate findings and insights to stakeholders effectively.

Qualifications:
 Education: Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or a related field. PhD is a plus.
 Experience: Proven experience as a Machine Learning Engineer or similar role. Experience with large-scale data processing and model deployment is highly desirable.Extensive experience with neural networks, natural language processing (NLP), and relevant deep learning frameworks (e.g., TensorFlow, PyTorch).
 Technical Skills:
 Proficiency in programming languages such as Python, R, or Java.
 Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
 Strong understanding of algorithms, statistical methods, and data structures.
 Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their ML services.
 Expertise in implementing and optimizing deep learning models and LLMs and its applications like RAG, Prompt Engineering Langchain, LlamaIndex.
 Strong understanding of machine learning algorithms, neural networks, and NLP techniques.
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Analytical Skills: Excellent problem-solving skills with the ability to analyze and interpret complex data.
 Communication: Strong written and verbal communication skills. Ability to present technical information to non-technical audiences.
 Team Player: Ability to work effectively in a collaborative, fast-paced environment.

Preferred Qualifications:
 Experience with deep learning, natural language processing, or computer vision.
 Familiarity with big data technologies (e.g., Hadoop, Spark).
 Experience in deploying and maintaining models in a production environment.
 Experience with advanced LLM architectures (e.g., transformer models).
 Experience in deploying models at scale and managing high-performance computing resources.,