Machine Learning Engineer

Job Description

Job Summary :

- As a Senior Machine Learning Developer at Emerson, you will be responsible for developing, implementing, and optimizing machine learning models and systems.

- You will collaborate closely with data scientists, software engineers, and other collaborators to translate data insights into practical, scalable solutions.

In this Role, Your Responsibilities Will Be :

- Design, build, and deploy machine learning models and algorithms to solve specific business problems.

- Optimize models for performance and scalability.

- Work with large data sets to preprocess, clean, and transform data for use in machine learning models.

- Develop and maintain data pipelines.

- Monitor and evaluate the performance of deployed models, making adjustments and improvements as needed to ensure accuracy and reliability.

- Work with multi-functional teams such as data scientists, analysts, and product managers to understand requirements and deliver machine learning solutions that meet business needs.

- Keep up with the latest research and trends in machine learning and artificial intelligence.

- Explore and implement new techniques and technologies to enhance model performance.

- Document model development processes, code, and standard methodologies.

- Provide clear and comprehensive reports on model performance and metrics.

- Participate in regular Scrum events such as Sprint Planning, Sprint Review, and Sprint Retrospective.

Who You Are :

- You quickly and decisively act in constantly evolving, unexpected situations.

- You adjust communication content and style to meet the needs of diverse partners.

- You always keep the end in sight; puts in extra effort to meet deadlines.

- You analyze multiple and diverse sources of information to define problems accurately before moving to solutions.

- You observe situational and group dynamics and select best-fit approach.

For This Role, You Will Need :

- Bachelors degree in computer science, Data Science, Statistics, or a related field or a master's degree or higher is preferred.

- More than 5 years of experience in machine learning engineering or a related role, with a strong track record of developing and deploying machine learning models.

- Proficiency in programming languages such as Python or R.

- Experience with machine learning frameworks (e.g., Go, TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, LangChain, or similar).

- Experience designing and optimizing prompts for large language models (LLMs).

- Multimodal AI.

- Proficiency in React, Angular for building interactive AI-powered UIs.

- Experience integrating ML models into web applications using REST APIs, WebSockets, or GraphQL.

- Ability to fine-tune foundation models on domain-specific data.

- Understanding of ethical AI, bias mitigation, and model interpretability in generative systems.

- Deep understanding of transformer-based models (e.g., GPT, LLaMA, Claude), diffusion models, and generative adversarial networks (GANs).

- Experience with LangChain, Semantic Kernel, AutoGen, or similar frameworks for building AI agents.

- Designing agents that can plan, reason, and interact with APIs, databases, and external tools.

- Experience with data processing and manipulation tools and libraries (e.g., pandas, NumPy).

- Strong analytical and problem-solving skills, with the ability to handle complex and large-scale data sets.

- Experience with deploying machine learning models to production environments, including knowledge of containerization technologies (e.g., Docker or equivalent) and cloud platforms, Microsoft Azure is preferred.

- Hands-on experience with Azure Machine Learning, Azure OpenAI, Azure Cognitive Services, and Azure Functions.

- Excellent verbal and written communication skills, with the ability to explain technical concepts to non-technical collaborators.

Preferred Qualifications that Set You Apart :

- Prior experience in engineering domain, process control industry would be nice to have.

- Prior experience in working with teams in Scaled Agile Framework (SAFe) is nice to have.

- Possession of relevant certification/s in data science from reputed universities specializing in AI.

- Familiarity with version control systems (e.g., Git) and CI/CD pipelines.

- Understanding of standard processes in software development and methodologies.