Senior MLOps Engineer - USA

Cogniify•Scottsdale, AZ

About The Position

We are looking for a Senior AI/ML Engineer to lead the design and delivery of production-grade machine learning systems and drive the maturity of our ML engineering and MLOps capabilities. In this role, you will own critical ML workstreams end to end, make key architectural decisions, mentor other engineers, and collaborate closely with cross-functional teams to ensure ML solutions are scalable, reliable, and aligned with business objectives. The ideal candidate brings deep technical expertise, proven production experience, and the ability to influence technical direction across the team.

Requirements

Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, or a related field. PhD is a plus.
8-10 years of professional experience in ML engineering, applied ML research, or a closely related role with significant production delivery.
Deep expertise in Python and advanced proficiency with ML frameworks such as TensorFlow, PyTorch, or JAX.
Extensive experience designing and operating production ML pipelines at scale.
Strong knowledge of MLOps principles and tools including MLflow, Kubeflow, Airflow, Argo Workflows, or similar.
Proven experience with cloud-native ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI) and infrastructure-as-code practices.
Experience with model serving at scale using frameworks such as TensorFlow Serving, Triton, BentoML, or Seldon.
Strong understanding of distributed computing, data engineering, and scalable system design.
Experience with monitoring, observability, and governance for production ML systems.
Demonstrated ability to mentor engineers and influence technical direction.
Excellent communication skills with the ability to present technical concepts to both technical and non-technical audiences.

Nice To Haves

Experience with LLM-based systems, RAG pipelines, or agentic AI architectures.
Experience with feature platforms (Feast, Tecton) and data quality frameworks (Great Expectations, Deequ).
Familiarity with model explainability and fairness tools (SHAP, LIME, Fairlearn).
Experience with real-time ML serving and streaming data pipelines (Kafka, Flink).
Contributions to open-source ML/MLOps projects.
Experience with GPU cluster management and cost optimization for training workloads.

Responsibilities

Lead the design, development, and production deployment of complex machine learning systems across multiple domains (NLP, computer vision, recommendation, forecasting, etc.).
Own the end-to-end ML lifecycle from problem framing and data strategy through model development, validation, deployment, and monitoring.
Architect scalable, fault-tolerant ML pipelines using modern orchestration and serving frameworks.
Drive the adoption and maturity of MLOps practices including CI/CD for ML, automated retraining, model registry, and governance.
Define and enforce engineering standards for model development, testing, code quality, and documentation.
Evaluate and introduce new tools, frameworks, and techniques to improve model performance, pipeline efficiency, and developer productivity.
Collaborate with data engineers, platform engineers, and product teams to align ML infrastructure with organizational goals.
Mentor and provide technical guidance to mid-level and junior engineers.
Conduct design reviews, code reviews, and architectural assessments for ML systems.
Contribute to technical roadmap planning and communicate tradeoffs and recommendations to engineering leadership.
Identify and mitigate risks related to data quality, model drift, bias, and security in production ML systems.