Gen AI/Machine Learning Engineer

Location: Dallas/Fort Worth, TX (Locals, F2F interview)

Top 3 Mandatory Skills and Experience:

-Proficient in Python

-Experience/knowledge on designing and implementing Gen AI applications- Hands-on experience with Machine Learning and AI pipeline build, model deployment, orchestration, monitoring, and optimization

Job Description:

Minimum Qualifications – Education & Prior Job Experience

Master or PhD degree with 5+ years of experience in quantitative discipline (e.g., Data Science, Machine Learning Engineer, Computer Science, Applied Mathematics, Statistics, etc.)

Python proficiency (production-grade coding, modularization, testing, performance tuning)

Hands on experience with building Gen AI applications – prompt engineering, classifiers, knowledge bases, RAG solutions, LLMs as judges, etc.

Hands-on experience with ML/AI pipeline development and productionization (model deployment, orchestration, monitoring, and optimization)

Depth of knowledge in statistical and machine learning techniques

Preferred qualifications – Education & Prior Job Experience 

Experience with Azure ML, Databricks

Proficiency in SQL and working with data

Experience with ML lifecycle tools (e.g., MLflow, CI/CD, model monitoring)

Practical experience designing, building and deploying machine learning models

Domain knowledge in the airline industry

Experience working in a consulting role

Skills, Licenses & Certifications

Ability to effectively communicate both verbally and written with all levels within the organization

Demonstrated motivation and aptitude for logical analysis, problem identification, and problem solving

Ability to view data from different angles to employ feature engineering techniques to better represent models

Ability to work on a diverse team with diverse skillsets

5-7 years of experience required

Nice to Have Skills:

- Microsoft Dynamics

- Prompt Engineering

- Proficiency in SQL and working with data

-Experience with ML lifecycle tools (e.g., MLflow, CI/CD, model monitoring)