Senior Data and AI Engineer

2 weeks ago

Melbourne, Victoria, Australia Cognizant Full time

About the role:

At Cognizant, we're not just at the forefront of technological innovation – we're actively shaping the future of the digital landscape. As a rapidly expanding global leader in IT services, we're seeking a Senior Machine Learning/AI Engineer to join our growing Data & AI practice. In this role, you'll combine deep technical expertise with consulting skills to deliver cutting-edge AI solutions while also contributing to presales activities and architectural decisions.

The Senior Consulting Data & AI Engineer is responsible for:

  1. Lead complex machine learning projects, including those involving large-scale datasets, complex models, and distributed computing environments.
  2. Develop and deploy state-of-the-art machine learning models, incorporating techniques like deep learning, reinforcement learning, and generative AI to address challenging business problems.
  3. Conduct comprehensive data analysis to identify patterns, trends, and insights that inform model development, ensuring that the models are built on a solid foundation of data understanding.
  4. Implement robust data pipelines to ensure data quality, consistency, and efficiency, optimizing data flow and reducing errors.
  5. Optimise model performance through techniques like hyperparameter tuning, feature engineering, and model selection, ensuring that the models achieve the highest possible accuracy and efficiency.
  6. Deploy machine learning models into production environments and monitor their performance to identify and address any issues that may arise.
  7. Develop and maintain machine learning infrastructure and tools, providing a scalable and efficient environment for model development and deployment.
  8. Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions, ensuring that the machine learning models align with business objectives.
  9. Communicate technical concepts effectively to both technical and non-technical audiences, facilitating collaboration and understanding.
  10. Stay up-to-date with the latest advancements in machine learning, data engineering, and generative AI to ensure that you are at the forefront of the field.
  11. Mentor junior engineers and foster a culture of knowledge sharing, contributing to the development of the next generation of data scientists.

Base capability requirements include:

  1. Master's degree in Computer Science, Data Science, or related field
  2. 5+ years of experience in machine learning and AI development
  3. Strong foundation in machine learning algorithms and statistical modeling
  4. Expert-level proficiency in Python and related ML libraries (scikit-learn, TensorFlow, PyTorch)
  5. Experience with deep learning frameworks and architectures (CNNs, RNNs, Transformers)
  6. Practical experience with MLOps tools and practices
  7. Strong knowledge of data engineering principles and practices
  8. Experience with distributed computing platforms (Spark, Databricks)
  9. Proficiency in SQL and experience with both relational and NoSQL databases
  10. Experience with cloud platforms (AWS, Azure, or GCP) for ML workloads
  11. Strong software engineering practices and version control (Git)
  12. Excellent communication skills and ability to explain complex technical concepts
  13. Experience with containerization (Docker) and orchestration (Kubernetes)
  14. Familiarity with modern AI tools and frameworks (Hugging Face, OpenAI, etc.)

Nice to have:

  1. Experience with generative AI and large language models
  2. Contributions to open-source ML projects
  3. Published research papers or technical blog posts
  4. Experience with real-time ML systems
  5. Knowledge of AI ethics and responsible AI practices

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