The Role Gap
Sr.MLEngineer–ML&AppliedAI
“Sr. ML Engineer – ML & Applied AI at The Role Gap. Skills: production ML systems, Python, ML frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow), scalable ML pipelines and services, model serving frameworks, containerization (Docker), Kubernetes, cloud platforms (GCP, AWS, Azure), ML lifecycle management, CI/CD pipelines for ML, SQL, distributed data processing (Spark, PySpark). Architect and build scalable, production-grade ML systems from experimentation to deployment and lifecycle managem”
What You'll Achieve.
power data-driven decision making across the enterprise; end-to-end ML system ownership; enable reliable, high-performance model inference in both batch and real-time environments
Industry & Context.
Excellent problem-solving skills
What They're Looking For.
Must Have
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, 10+ years of experience in machine learning, software engineering, or related roles, significant experience in production ML systems, programming expertise in Python, solid software engineering fundamentals (data structures, system design, APIs), Extensive experience with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow, Proven experience designing and deploying scalable ML pipelines and services in production, Hands-on experience with model serving frameworks and API development (e. g. , FastAPI, Flask), experience with containerization (Docker), orchestration platforms such as Kubernetes, Experience working with cloud platforms (GCP, AWS, or Azure), building cloud-native ML solutions, Deep understanding of ML lifecycle management, including training, evaluation, deployment, monitoring, and retraining, Experience implementing CI/CD pipelines for ML workflows, managing version control systems (Git), experience with SQL, distributed data processing frameworks (e. g. , Spark, PySpark), Excellent problem-solving skills, ability to design scalable, maintainable systems
Nice to Have
modern applications involving large language models (LLMs), agent-based AI systems, vector databases, retrieval-augmented generation (RAG), agentic workflows
What You'll Do.
Architect and build scalable
production-grade ML systems from experimentation to deployment and lifecycle management
Design and implement end-to-end ML pipelines
including data ingestion
Develop and maintain high-performance model serving systems using APIs (e. g.
FastAPI) for real-time and batch inference
Lead the design and implementation of feature stores and reusable feature pipelines across teams
Build and optimize distributed data processing workflows using Spark
Implement and enforce MLOps best practices
including CI/CD pipelines
and experiment tracking
Design and manage model monitoring and observability frameworks to track performance
Drive strategies for model retraining
and continuous improvement
Contribute to the adoption of modern AI capabilities
retrieval-augmented generation (RAG)
and agentic workflows
Ensure high standards of code quality
How You'll Work.
Team & Collaboration
Collaborate closely with data engineers, platform teams, and product stakeholders to integrate ML solutions into production systems
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