Senior ML Ops Engineer - Dallas, TX
Photon
•$48,000 - $168,000•Onsite
About The Position
For the past 20 years, we have powered many Digital Experiences for the Fortune 500. Since 1999, we have grown from a few people to more than 4000 team members across the globe that are engaged in various Digital Modernization.
Requirements
- Hands on with designing end to end scalable ML system (should have worked on recent projects within last 12 months).
- Hands on with implementation of scalable ML system. Proven ownership across entire or partial ML and MLOps lifecycle: Evaluation Techniques, Machine Learning Algorithms, Statistical Modeling, End to end deployment, Metric generation, Model monitoring and deployment, Prompt Engineering.
- Hand on with ML Model optimization - quantization, pruning, or speculative decoding etc.
- Hand on with ML Model system optimizations ie ability to quickly identify bottlenecks and resolve them from System perspective.
- Programming & Frameworks: Hands on and have worked on recent projects (within last 12months) in: Java (library/dependency management, Package and distribution management, Algorithms), Python, Tensorflow / PyTorch.
- Knowledge of DevOps principles and tools (e.g., CI/CD pipelines, Terraform).
- Strong understanding of containerization technologies (e.g., Docker, Kubernetes).
- Good Team worker & good collaborations skills.
- Ability to abstract out details, define problem & have clear technical communication.
- Ability to lead inter team communication.
- Ability to write crisp and effective documentation.
- Ensures that deadlines are met.
Nice To Haves
- GCP ML Tech stack.
- Experienced with Infrastructure as Code (IaC).
- Experience with big data technologies such as Apache Spark or Hadoop.
- Stay informed about the ethical implications of machine learning eg: selection bias.
- Model Training
- Data Analytics - figure out anomalies , skew , discrepancies
- Hands on with developing on device ML System.
- Mentoring and Leadership.
- Project Management.
Responsibilities
- Designing end to end scalable ML system
- Implementation of scalable ML system
- Ownership across entire or partial ML and MLOps lifecycle including Evaluation Techniques, Machine Learning Algorithms, Statistical Modeling, End to end deployment, Metric generation, Model monitoring and deployment, Prompt Engineering
- ML Model optimization (quantization, pruning, or speculative decoding etc.)
- ML Model system optimizations (ability to quickly identify bottlenecks and resolve them from System perspective)
- Programming & Frameworks: Java (library/dependency management, Package and distribution management, Algorithms), Python, Tensorflow / PyTorch
- DevOps principles and tools (e.g., CI/CD pipelines, Terraform)
- Containerization technologies (e.g., Docker, Kubernetes)
- Good Team worker & good collaborations skills
- Ability to abstract out details, define problem & have clear technical communication
- Ability to lead inter team communication
- Ability to write crisp and effective documentation
- Ensures that deadlines are met
Benefits
- Medical, vision, and dental benefits
- 401k retirement plan
- variable pay/incentives
- paid time off
- paid holidays
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
Number of Employees
1,001-5,000 employees
