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What We'll Bring:
As a Machine Learning Engineer as part of Intelligent Automation team, you will play a crucial role in designing, developing, evaluating and deploying advanced AI models that would enable higher success rate in automating complex processes involving structured and unstructured data, natural language processing.To thrive in this position, you must possess exceptional skills in statistics and programming, as well as a deep understanding of data science and software engineering principles.
Your ultimate objective will be to create highly efficient self-learning applications that can adapt and evolve over time, pushing the boundaries of AI technology. Join us and be at the forefront of innovation in the field of Intelligent Automation.
What You'll Bring:
Responsibilities
- Research and implement appropriate ML algorithms and tools
- Conduct technical feasibility studies for applying AI/Gen AI to use cases
- Develop proof-of-concepts and prototypes to demonstrate the potential of AI in solving client problems
- Select appropriate datasets and data representation methods
- Deploy AI models into production environments, ensuring scalability, efficiency, and reliability.
- Run machine learning tests and experiments
- Perform statistical analysis and fine-tuning using test results
- Train and retrain systems when necessary
- Extend existing ML libraries and frameworks
- Mentor and guide junior developers in AI techniques and best practices.
- Stay updated with the latest research in generative AI, RAG, and Graph RAG, and apply new methodologies to improve model outcomes.
- Experiment with different model architectures and approaches to achieve optimal results.
- Implement monitoring and observability solutions for deployed models
- Troubleshoot and optimize AI systems in production environments
Impact You'll Make:
Requirements and skills
- Proven experience as a Machine Learning Engineer or similar role
- Understanding of data structures, data modeling and software architecture
- Deep knowledge of math, probability, statistics and algorithms
- Ability to write robust code in Python, Java and R
- Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
- Excellent communication skills
- Ability to work in a team
- Outstanding analytical and problem-solving skills
- Bachelor’s degree in Computer Science or Mathematics or similar field; Master’s degree is a plus
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