Lead Machine Learning Engineer at Chevron

Houston, Texas, United States -

Full Time

Start Date

Immediate

Expiry Date

01 Jan, 26

Salary

0.0

Posted On

03 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Python, Generative AI, NLP, Computer Vision, Data Engineering, Cloud Computing, MLOps, Agile Methodologies, Statistical Analysis, API Development, Data Pipelines, Docker, Azure, Kubernetes, Object-Oriented Programming

Industry

Chemical Manufacturing

Description

Partner with Digital Innovation Teams to evaluate and test emerging AI and Machine Learning technologies. Explore and experiment with applications of Generative AI (GenAI), NLP, and computer vision. Stay current with the latest advancements in AI and integrate them into projects. Work collaboratively with a large variety of different teams, including data scientists, data engineers, and solution architects from various organizations within business units and IT Build and maintain robust data pipelines using platforms like Databricks. Deploy models in production using Docker and cloud platforms (e.g., AWS, Azure). Conduct ML tests and experiments to validate hypotheses and improve performance. Identify data, appropriate technology and architectural design patterns to solve business challenges using analytical tools, AI design patterns and architectures. Transform data science prototypes into appropriate scale solutions in a production environment. Orchestrate and configure infrastructure that assists Data Scientists and analysts in building low latency, scalable and resilient machine learning and optimization workloads into an enterprise software product. Run machine learning experiments and fine-tune algorithms to ensure optimal performance. Participate as an active member in embedded Agile team to enable cross training and keep skills current. Desire to learn new technologies and design patterns to continually improve delivery of AI Solutions at scale. BS in Computer Science, Mathematics, or related fields or equivalent experience. 5+ years' experience in Software Engineering. Significant experience engineering solutions in Python with strong understanding of control flow, functions, data structures and object-oriented programming concepts. Experience implementing machine learning frameworks and libraries (e.g. ML Flow, Kubeflow, Tensorflow. Keras scikit-learn, PyTorch, NumPy, SciPy, etc.). Development experience with a JavaScript framework (Angular, React, node.js etc.). Experience building machine learning pipelines in Microsoft Azure Machine Learning service. Experience developing cloud first solutions using Microsoft Azure Services (Azure Functions, Azure App Services, Azure Event hubs, Azure SQL DB, Azure Synapse etc.). Proficient in applying common design patterns, ability to communicate design ideas effectively. Must have a disciplined, methodical, minimalist approach to designing and constructing layered software components that can be embedded within larger frameworks or applications. Working knowledge of mathematics (primarily linear algebra, probability, statistics), and algorithms. Knowledge of data engineering and transformation tools and patterns such as DataBricks, Spark, Azure Data Factory. MS in Computer Science, Mathematics, or related fields. Excellent skills in statistics and machine learning applied to timeseries data analysis. Are proficient orchestrating large-scale ML/DL jobs, leveraging big data tooling and modern container orchestration infrastructure (e.g. Kubernetes), to tackle distributed training and massive parallel model executions on cloud infrastructure. Experience designing custom APIs for machine learning models for training and inference processes. Experience designing, implementing, and delivering frameworks for MLOps. Experience implementing and incorporating ML models on unstructured data using cognitive services and/or computer vision as part of AI solutions and workflows. History of working with large scale model optimization and hyperparameter tuning, applied to ML/DL models. Hands-on experience in deploying machine learning pipelines with Azure Machine Learning SDK. Exceptional object-oriented programming and debugging skills in Python. A keen eye for good architecture and the ability to develop new architectures and frameworks. Passionate and detailed approach to software development. Knowledge of enterprise SaaS complexities including security/access control, scalability, high availability, concurrency, online diagnoses, deployment, upgrade/migration, internationalization, and production support. Mature software engineering skills, such as source control versioning, requirement spec, architecture and design review, testing methodologies, CI/CD, etc. Proven ability to take leadership role in projects that span multiple teams, ability to deliver on time working in a fast-paced agile environment, ability to work with product managers to clarify and prune requirements, strong verbal and written communication. Experience working with data scientists in the integration of and delivery of models for advanced analytics use cases.

Responsibilities

The Lead Machine Learning Engineer will partner with Digital Innovation Teams to evaluate and test emerging AI and Machine Learning technologies, while exploring applications of Generative AI, NLP, and computer vision. They will build and maintain robust data pipelines, deploy models in production, and conduct ML tests to validate hypotheses and improve performance.