Sr. Engineer – Machine Learning || Dallas, TX(Day One Onsite)

Sr. Engineer – Machine Learning

Dallas, TX(Day One Onsite)

Contract

Description:

Collaborate with leaders, business analysts, project managers, IT architects, technical leads and other engineers, along with internal customers, to understand requirements and develop needs according to business requirements for AI solutions

Maintain and enhance existing enterprise services, applications, and platforms using domain driven design and test-driven development

Troubleshoot and debug complex issues; identify and implement solutions

Create detailed project specifications, requirements, and estimates

Research and implement new AI technologies to enhance current processes, security, and performance

Work closely with data scientists and product teams to build and deploy machine learning models, focusing on the technical aspects of model deployment.

Implement and optimize Python-based ML pipelines for data preprocessing, model training, and deployment.

Monitor model performance and implement strategies for bias mitigation and explainability.

Responsible for ensuring models are scalable and efficient in production environments. 

Write and maintain code for model training and deployment, collaborating with software engineers to integrate models into applications.

Partner with a diverse team of experts, leveraging cutting-edge technologies to build scalable and impactful AI solutions.

Minimum Qualifications – Education & Prior Job Experience:

Bachelor’s degree in computer science, Computer Engineering, Data Science, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training

7 to 9+ years of full Software Development Life Cycle (SDLC) experience designing, developing, and implementing large-scale machine learning applications in hosted production environments

2+ years of professional, design, and open-source experience 

Top Requirements:

Mixed-Integer Programming (MIP) modeling; formulating real-world business rules as decision variables, linear constraints, and objectives; LP/IP theory; branch-and-bound intuition.

Commercial solver experience; FICO Xpress (strongly preferred) or Gurobi/CPLEX/OR-Tools/Hexaly, including solver tuning (gaps, threads, seeds, determinism) and reading solver logs /.lp files.

Programming to implement model & visualize model solutions; Java (the model lives in Java behind a solver abstraction); Python (incl. Streamlit for the dashboard); able to code and ship changes in both programming languages.

Also required: comfortable using AI coding agents (Claude / Copilot) for fast changes; high coding standards; a collaborative team player (open to ideas/feedback, no solo work); a fast learner.

MS or PhD in Operations Research / Industrial Engineering / Applied Math (or related), plus 3+ years applied optimization  or equivalent demonstrated MIP experience.

Education:

MS/PhD in OR / IE / Applied Math / CS-optimization or equivalent.

Nice To Have:
Java 21 / Spring Boot familiarity; clean/hexagonal model design (ports & adapters); TDD (JUnit 5) for optimization models (LP-file integration tests); scheduling/assignment/routing problem experience; heuristics/metaheuristics/CP as complements to MIP; data analysis & debugging; mining datasets (incl. Azure ADLS) and solver logs to find issues and derive insights; data wrangling (CSV/Excel/SQL); Git/GitHub, MongoDB Compass, Maven, Docker; aviation / MRO dom