Senior Manger of AI/ML Ops

Description

Seeking a passionate, hands on technical AI /ML experienced engineer to join our growing team and solve exciting engineering problems in the semiconductor space. This position will work closely with multidisciplinary teams, product managers, Data engineers, data scientists, and business stakeholders to bring AI solutions to production.

Responsibilities

•  Architect and maintain scalable MLOps pipelines for model training, deployment, and monitoring.
• Lead the implementation of containerized ML workloads using Kubernetes.
• Design and Develop ML Models for high impact engineering solutions. Knowledge of python, Pytorch, agentic frameworks, Langchain, RAG, capability to understand and build deep learning models on cloud or on premises compute.
• Automate model lifecycle management including versioning, rollback, and performance tracking. 
•  Ensure high availability, security, and compliance of ML systems.
•  Develop infrastructure as code using tools like Terraform or Helm.
•  Establish and enforce best practices for model governance and reproducibility.
• Lead and mentor a team of AI/ML engineers and ML operations specialists, fostering a collaborative and innovative environment.
• Design, implement, and maintain scalable and reliable machine learning infrastructure and pipelines.
• Establish best practices for model development, deployment, monitoring, and lifecycle management.
• Collaborate with data scientists and engineers to productionize ML models.
• Manage and optimize cloud and on-premises resources for efficient training, serving, and experimentation of machine learning models.
• Ensure compliance with data privacy, security, and ethical standards in all AI/ML operations.
• Automate processes for continuous integration and deployment (CI/CD) of machine learning models.
• Drive adoption of MLOps tools and technologies, championing innovation and efficiency.
• Develop documentation, training programs, and onboarding resources for new team members and stakeholders.
• Establish metrics and KPIs to measure the quality and impact of AI/ML models in production environments.
• Stay current with industry trends, research, and emerging technologies, integrating relevant advances into operational practices.

Required Experience and Skills

• Bachelor with minimum 12+ years of professional experience in machine learning, artificial intelligence, or related fields. Preferably with a Master’s degree or A PhD or relevant research experience would be a plus. in Computer Science, Engineering, Mathematics, Statistics, or a related field.
• Extensive experience with Kubernetes and container orchestration. 
• Proficiency in Python and Bash scripting.
• Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Familiarity with cloud platforms (Azure(preferred), GCP).
• Knowledge of CI/CD tools and monitoring systems.
• Experience managing and guiding AI/ML engineering and operations team.
• Extensive expericence in MLOps practices: monitoring, scaling, and automating ML workflows
• Experience with big data platforms: Databricks, Hadoop, Spark, Dataflow, etc or comparable products
• Familiarity with advanced topics such as reinforcement learning, generative models, or explainable AI
• Knowledge of neural networks, deep learning architectures such as CNNs, RNNs, Transformers and Generative AI.

Desired Experience and Skills

• Experience with MLflow, or similar platforms.
• Exposure to data versioning tools like DVC or Databricks Delta Lake.
• Understanding of model explainability and compliance frameworks.
• Proficiency in programming languages such as Python (preferred), Java, or C++ .7 years experience
• Deep understanding of machine learning frameworks: PyTorch, scikit-learn, Keras, etc.
• Experience with data manipulation tools: NumPy, SQL, Pandas
• Familiarity with cloud and Data computing platforms:  Azure, Azure DevOps, DataBricks or comparable platforms like GCP, BIgQuery
• Knowledge of containerization and orchestration: Docker, Kubernetes
• Experience in deploying machine learning models to production
• Understanding of software engineering best practices: version control (Git), unit testing, CI/CD pipelines