Sr. AI/ML Ops Engineer

Blazop; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Blazop is seeking a Senior AI/ML Engineer focused on MLOps to drive end-to-end machine learning production workflows in the United States. You will work on deploying models, maintaining data pipelines and ensuring scalable, trustworthy AI systems.

Ideal candidates have 6–10 years in AI/ML, strong Python/DL skills with PyTorch, and hands-on experience with MLOps tooling across cloud platforms. This role emphasizes collaboration, code quality, and continuous learning.

Qualifications

• Bachelor's or Master's degree in data science or related field preferred.
• Experience with Python for ML; data processing with NumPy/Pandas is expected.
• Strong knowledge of NLP and LLM concepts.

Responsibilities

• Lead MLOps implementation using Docker, Kubernetes, Azure DevOps and AWS SageMaker.
• Develop scalable Python-based ML code with NumPy and Pandas.
• Collaborate with cross-functional teams using Git for version control.
• Coordinate CI/CD with Jenkins or GitLab.
• Implement observability tooling using Prometheus, Grafana, Azure Monitor and AWS CloudWatch.
• Utilize TensorBoard for model evaluation and monitoring.
• Document code, models and deployment steps in Confluence/Markdown.
• Provide training on MLOps and Python best practices.

Skills

Python

Jupyter Notebooks

PyTorch

LLM knowledge

GenAI

Education

Bachelor's/Master's in Data Science

Job description

We are actively seeking a highly skilled and experienced Senior AI/ML Engineer with a focus on MLOps to join our innovative team. If you have 6 to 10 years of hands-on experience in the AI/ML space and a passion for driving technological advancements, this role is for you.

Specific Skills

• Python Expertise: proficiency in Object-Oriented Python
• Data Science (Jupyter Notebooks): demonstrated expertise in data science, including analysis and modeling using Jupyter Notebooks
• Deep Learning (PyTorch): proven experience in deep learning, particularly with PyTorch, and familiarity with other frameworks
• Good LLM knowledge: solid understanding of Natural Language Processing (NLP) and Language Models (LLM)
• Any successful implementation of GenAI (LLMs) on custom data is preferred
• Bachelor's/Master's in Data Science is preferred

Responsible For

• MLOps Implementation (Docker, Kubernetes, Azure DevOps, AWS SageMaker): lead the implementation of MLOps practices, ensuring seamless integration of machine learning models into production systems using containerization and orchestration, and MLOps tooling from both Azure and AWS
• Code Development (Python, NumPy, Pandas): develop and maintain scalable and efficient Python code for machine learning applications, using NumPy and Pandas for effective data manipulation and analysis
• Collaboration (Git): collaborate with cross-functional teams to understand business requirements and seamlessly integrate machine learning solutions into software applications, using Git for version control
• DevOps Integration (Jenkins, GitLab): work closely with DevOps teams to streamline deployment processes, implementing CI/CD practices with tools like Jenkins or GitLab
• Observability (Prometheus, Grafana, Azure Monitor, AWS CloudWatch): focus on fine-tuning models and identifying data anomalies, implementing observability tooling for monitoring and troubleshooting across Azure and AWS
• Model Evaluation (TensorBoard): implement model evaluation tools such as TensorBoard to ensure models meet performance criteria
• Documentation (Confluence, Markdown): create comprehensive documentation for code, models, and deployment processes
• Training and Knowledge Sharing: provide training and knowledge-sharing sessions to team members on best practices in MLOps and Python coding