Lead development of Agentic AI capabilities and LLM applications for multiple mission management applications. Mentor teams to implement ML algorithms addressing customer challenges.
AT
Hybrid Machine Learning Engineer III
Posted last month
About the role
- Machine Learning Engineer at Amex GBT utilizing AI techniques for business travel solutions. Collaborating globally to design, build, and optimize intelligent systems with measurable impact.
Responsibilities
- Utilize AI techniques, including machine learning, deep learning, generative AI, and statistical modeling, to develop and implement solutions for tackling real-world problems such as ranking, chatbot, intent recognition, agentic AI systems, recommender systems, computer vision, NLQ etc.
- Apply strong coding skills, analytical abilities, and innovative thinking to quickly understand new domains and transform creative ideas into functional solutions.
- Employ statistical and data science methods to make data-driven decisions.
- Manipulate large data sets for business insights and drive actionable solutions.
- Utilize appropriate methods and approaches to develop practical solutions for the business.
- Structure work, frame issues, and produce analyses/ML models/Compound AI systems that answer complex business questions in a pragmatic approach.
- Communicate complex data science topics in a clean & simple way to multiple partners and senior leadership.
- Collaborate with a global team across different continents to achieve project goals.
Requirements
- 8+ years of experience with a bachelor’s degree or equivalent, or 5+ years with a master’s degree or equivalent.
- Proven ability to conceptualize business problems and solve them through data science solutions.
- Proven knowledge of AI techniques such as Bayesian methods, Clustering, Ensemble tree models, NLP, etc., with an excellent grasp of statistical concepts and methods.
- Good understanding of LLMs, guardrails, RAG, agentic AI.
- Strong passion for solving problems and finding patterns and insights within structured and unstructured data.
- Industry experience in leveraging AI techniques on real-world large data sets.
- Understanding of the concepts and steps involved in working with real-world large data sets: from domain-specific problem understanding, data preparation, travel-related data processing, feature engineering, data structures for ML, data pipelining, modeling, offline evaluation, online evaluation, and monitoring.
- Strong knowledge of hands-on practice in Python.
- Familiarity with popular machine learning libraries and frameworks such as scikit-learn, Hugging Face, PyTorch and TensorFlow.
- Experience with MLFlow, AWS SageMaker and Bedrock is preferred.
- Familiar with MLOps concept.
- Comfortable with data cleansing using SQL and PySpark.
- Good written and oral communication skills, including the ability to communicate across business areas and increase overall knowledge across the organization.
- Experience with feature stores, machine learning models as service, and monitoring dashboards is a plus.
- Some infrastructure knowledge (AWS, Kubernetes) and cost-awareness are a plus.
Benefits
- Health and welfare insurance plans
- Retirement programs
- Parental leave
- Adoption assistance
- Wellbeing resources
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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