logoAiPathly

Enterprise ML Platform Engineer

first image

Overview

An Enterprise ML (Machine Learning) Platform Engineer plays a crucial role in designing, building, and maintaining the infrastructure and systems that support the entire machine learning lifecycle within an organization. This role is pivotal in creating a seamless, efficient, and scalable environment for machine learning model development, deployment, and operation.

Key Responsibilities

  • Infrastructure Design and Implementation: Designing and implementing the underlying infrastructure that supports machine learning models, including hardware and software components, networking, and storage resources.
  • Automation and CI/CD Pipelines: Building and managing automation pipelines to operationalize the ML platform, including setting up Continuous Integration/Continuous Deployment (CI/CD) pipelines.
  • Collaboration: Working closely with cross-functional teams, including data scientists, ML engineers, DevOps engineers, and domain experts.
  • MLOps and Model Management: Managing the machine learning operations (MLOps) lifecycle, including versioning, data and model lineage, and ensuring model quality and performance.
  • Security and Governance: Implementing data and model governance, managing access controls, and ensuring compliance with regulations.
  • Efficiency Optimization: Automating testing, deployment, and configuration management processes to reduce errors and improve efficiency.

Technical Skills

  • Proficiency in programming languages such as Python, Java, or Kotlin
  • Experience with cloud platforms like AWS, Azure, or Google Cloud Platform
  • Knowledge of networking concepts, TCP/IP, DNS, and HTTP protocols
  • Familiarity with RESTful microservices and cloud tools
  • Experience with Continuous Delivery and Continuous Integration
  • Proficiency in tools like Databricks, Apache Spark, and Amazon Sagemaker

Role Alignment

  • ML Engineers: ML Platform Engineers support ML engineers by providing necessary infrastructure and automation pipelines.
  • Data Scientists: ML Platform Engineers ensure that the infrastructure supports data scientists' needs for data access, model development, and deployment.
  • DevOps Engineers: ML Platform Engineers work with DevOps engineers to ensure ML models integrate smoothly into the broader organizational stack. In summary, an Enterprise ML Platform Engineer ensures alignment with business outcomes and adherence to security and governance standards while supporting the entire ML lifecycle within an organization.

Core Responsibilities

Enterprise ML (Machine Learning) Platform Engineers have a wide range of responsibilities that are crucial for the successful implementation and maintenance of ML systems within an organization. These core responsibilities include:

Platform Development and Maintenance

  • Design, develop, and maintain robust and scalable ML platforms
  • Create and enhance reusable frameworks for AI/ML model development and deployment
  • Implement inference services, automated workflows, and data ingestion systems

Automation and CI/CD Pipelines

  • Build and manage automation pipelines for ML platform operationalization
  • Implement fully or partially automated CI/CD pipelines
  • Automate tasks such as Docker image building, model training, and deployment

Infrastructure and Resource Optimization

  • Provision and optimize infrastructure resources (servers, networking, storage, cloud services)
  • Maximize efficiency and minimize costs of infrastructure utilization

Collaboration and Communication

  • Work closely with cross-functional teams (ML Engineers, Data Scientists, Product Managers)
  • Translate team needs into technical solutions
  • Mentor and educate other engineers on current and upcoming tools and technologies

Security, Compliance, and Governance

  • Integrate security and compliance measures into the ML platform
  • Implement encryption, access management, and data/model lineage tracking
  • Ensure overall platform governance and adherence to regulations

Monitoring and Performance

  • Monitor system performance, security, and reliability
  • Oversee ML models and infrastructure to meet control requirements
  • Analyze performance metrics and implement improvements

Tool Development

  • Create and maintain tools for model experimentation, visualization, and monitoring
  • Streamline development and experimentation processes

Technology Research and Implementation

  • Stay updated with the latest advancements in AI, machine learning, and cloud technologies
  • Evaluate and implement new technologies to improve the platform

Documentation and Knowledge Sharing

  • Document configurations, processes, and best practices
  • Communicate complex ideas and technical knowledge through clear documentation
  • Facilitate knowledge sharing across different teams By fulfilling these core responsibilities, Enterprise ML Platform Engineers ensure the efficient development, deployment, and management of machine learning systems within their organizations, supporting data-driven decision-making and innovation.

Requirements

To excel as an Enterprise ML Platform Engineer, candidates should possess a combination of technical expertise, operational skills, and collaborative abilities. The following requirements are essential for success in this role:

Technical Skills

  • Programming Languages: Proficiency in Python, Java, and/or Kotlin
  • Machine Learning Frameworks: Experience with TensorFlow, PyTorch, Keras, and Scikit-Learn
  • Cloud Platforms: Familiarity with AWS, Azure, or GCP, and their ML-related services
  • Containerization and Orchestration: Knowledge of Docker and Kubernetes (or similar technologies)
  • CI/CD and Infrastructure Automation: Experience with tools like Jenkins, Terraform, CloudFormation, or Ansible

Data Engineering and Management

  • Data Processing: Proficiency in SQL, NoSQL, Hadoop, Spark, and Apache Kafka
  • Data Governance: Understanding of data versioning, model tracking, and governance practices

Operational and Deployment Skills

  • Model Deployment: Experience in deploying and scaling ML models in production environments
  • Performance Optimization: Ability to ensure high availability and fault tolerance
  • Monitoring and Logging: Familiarity with tools like Prometheus and ELK Stack

MLOps and Best Practices

  • MLOps Tools: Experience with ModelDB, Kubeflow, Pachyderm, or Data Version Control (DVC)
  • Best Practices: Knowledge of model hyperparameter optimization, evaluation, and explainability
  • Automation: Strong understanding of CI/CD pipelines for ML workflows

Collaboration and Communication

  • Teamwork: Ability to work effectively with data scientists, DevOps engineers, and other stakeholders
  • Communication: Excellent interpersonal and written communication skills
  • Mentoring: Capacity to guide junior team members and share technical knowledge

Education and Experience

  • Education: Degree in Computer Science, Statistics, Mathematics, or a related field
  • Experience: 3-6 years of experience managing end-to-end ML projects
  • Specialization: At least 18 months of focused experience in MLOps
  • Senior Roles: 5+ years of experience in ML model deployment and scaling for higher positions

Additional Qualities

  • Problem-solving: Strong analytical and problem-solving skills
  • Adaptability: Ability to learn and adapt to new technologies quickly
  • Innovation: Creative approach to overcoming technical challenges
  • Project Management: Skills in managing complex, long-term projects By meeting these requirements, an Enterprise ML Platform Engineer can effectively design, build, and maintain scalable and efficient machine learning platforms, driving innovation and data-driven decision-making within their organization.

Career Development

Enterprise ML Platform Engineers can build successful careers by focusing on key areas:

Core Skills and Qualifications

  • Master programming languages like Java, Kotlin, or Python
  • Develop expertise in cloud platforms, especially AWS
  • Gain proficiency in machine learning concepts, MLOps, and data engineering

Technical Expertise

  • Acquire hands-on experience with AWS services (S3, Kinesis, EKS)
  • Familiarize yourself with tools like Databricks, Apache Spark, and Amazon SageMaker
  • Learn to scale and deploy machine learning models, including Large Language Models (LLMs)

Practical Experience

  • Design, build, deploy, and operationalize machine learning models
  • Participate in online communities and machine learning challenges
  • Develop personal projects to build a portfolio

Career Path

  • Start as a Junior Machine Learning Engineer
  • Progress to Senior Machine Learning Engineer or Machine Learning Cloud Architect
  • Consider mid-career transitions from software development

Continuous Learning

  • Stay updated with the latest technologies and methodologies
  • Pursue certifications like Google Cloud Certified Professional Machine Learning Engineer
  • Engage in online training and hands-on labs offered by cloud providers

Collaboration and Soft Skills

  • Develop effective communication and mentoring abilities
  • Learn to collaborate with cross-functional teams

Compensation and Growth

  • Average salaries range from $122,400 to $196,600
  • High demand for skilled professionals in this rapidly growing field By focusing on these areas, you can build a strong foundation and advance in the dynamic field of Enterprise ML Platform Engineering.

second image

Market Demand

The demand for Enterprise ML Platform Engineers is driven by several key factors:

Growing MLOps Market

  • Global MLOps market projected to reach $13,321.8 million by 2030
  • CAGR of 43.5% from 2023 to 2030
  • Driven by need for improved ML model performance and large-scale production rollouts

Expanding Machine Learning Adoption

  • Machine learning market expected to reach $225.91 billion by 2030
  • CAGR of 36.2% from 2022 to 2030
  • Increasing adoption across IT, telecom, healthcare, and other industries

Skill Gap and Talent Demand

  • Machine learning is the most in-demand AI skill
  • Job postings for AI specialists growing 3.5 times faster than all jobs
  • Key skills: Python, computer science, SQL, data analysis, and software engineering

Enterprise Needs

  • Large enterprises driving demand for ML Platform Engineers
  • Focus on handling large data volumes and optimizing ML model deployments
  • IT and telecom sectors are significant users of ML solutions

Cloud and Hybrid Deployments

  • Increasing preference for cloud and hybrid ML operations
  • Cloud segment expected to show remarkable growth
  • Hybrid deployments anticipated to grow with a leading CAGR The robust and growing market demand for Enterprise ML Platform Engineers is fueled by expanding markets, cloud adoption, and the ongoing need for skilled professionals across various industries.

Salary Ranges (US Market, 2024)

Enterprise ML Platform Engineers can expect competitive salaries in the US market:

Average Base Salaries

  • Range from $157,969 to $161,777 per year
  • Total compensation (including bonuses) can reach $202,331 to $250,000+

Salary by Experience

  • Entry-level (0-1 years): $120,571 to $152,601 per year
  • Mid-level (1-3 years): $144,572 to $166,399 per year
  • Senior (7+ years): $162,356 to $189,477 per year
  • Highly experienced (15+ years): $170,603+ per year

Salary by Location

  • San Francisco, CA: $158,653 to $179,061 per year
  • New York City, NY: $143,268 to $184,982 per year
  • Seattle, WA: $150,321 to $173,517 per year
  • Los Angeles, CA: $131,000 to $159,560 per year
  • Other major tech hubs: Generally $120,000 to $180,000 per year

Top-Paying Markets and Industries

  • Los Angeles: Up to $225,000 per year (Mobile and AI/ML industries)
  • New York: Up to $175,000 per year
  • Seattle and San Francisco Bay Area: Up to $160,000 per year
  • Specialized skills (TypeScript, Docker, Flask) can command $190,000+ per year

Enterprise ML Platform Engineer Estimates

  • Mid-level: $150,000 to $180,000 per year
  • Senior: $180,000 to $220,000+ per year Note: Total compensation may be higher when including additional cash and non-cash benefits. Salaries vary based on location, industry, company size, and individual skills and experience.

More Careers

Research Scientist Computer Vision

Research Scientist Computer Vision

A Research Scientist in Computer Vision is a specialized role focused on advancing technologies that enable machines to interpret and understand visual information. This overview provides insights into key aspects of the career: ### Key Responsibilities - Design and implement advanced algorithms for computer vision tasks such as object recognition, video analysis, and 3D reconstruction - Conduct experiments and analyze results to solve complex challenges in deep learning, machine learning, and augmented reality - Develop image and video analysis techniques for large-scale, real-world applications - Collaborate with multidisciplinary teams to create innovative technical solutions - Stay updated with the latest trends in computer vision and contribute to research publications ### Educational and Skill Requirements - Typically requires a Master's or Ph.D. in Computer Science, Electrical Engineering, or related fields - Proficiency in programming languages like Python, C++, and experience with frameworks such as OpenCV and TensorFlow - Strong analytical, problem-solving, and teamwork abilities ### Work Environment and Career Path - Opportunities in academia, industry research labs, and tech companies - Potential for career advancement to roles like Computer Vision Engineer or Principal Investigator ### Compensation and Job Outlook - Median annual wage for computer and information research scientists: $145,080 (May 2023) - Employment projected to grow 26% from 2023 to 2033, much faster than average This role offers a dynamic environment with continuous learning opportunities and the chance to make significant impacts in technology and society.

Technical Specialist Data and AI

Technical Specialist Data and AI

The role of a Technical Specialist in Data & AI is a multifaceted position that blends technical expertise, business acumen, and strong interpersonal skills. This overview outlines key aspects of the role, particularly in the context of major tech companies: ### Core Responsibilities - Technical Leadership: Provide competitive insights, contribute to tech communities, and represent the company at industry events. - Client and Sales Support: Offer technical support during sales cycles, including demos and proofs of concept. - Technical Expertise: Act as deep technical experts in data warehouse technologies, SQL, NoSQL data stores, and relevant AI technologies. - Client Relationship Management: Understand client needs, build relationships, and articulate business value for Data & AI solutions. ### Essential Skills - Communication: Convey complex technical information clearly to diverse audiences. - Creativity: Develop innovative solutions for unique client challenges. - Project Management: Handle multiple projects while meeting client expectations. - Business Acumen: Understand and address clients' business needs with tailored solutions. - Adaptability: Stay current with rapidly evolving technologies. ### Key Areas of Expertise - Data & AI Technologies: Proficiency in data analytics, machine learning, AI frameworks, and cloud services. - Database Systems: Familiarity with various database management systems. - Ethical and Responsible AI: Ensure fairness, transparency, and bias mitigation in AI systems. ### Career Development - Continuous Learning: Stay updated on latest technologies and engage in applied research. - Collaboration: Work closely with sales teams and build a strong personal brand. - Customer-Centric Approach: Provide strategic insights and tailored solutions. ### Work Environment - Company Culture: Diverse and inclusive environment with access to world-class resources. - Professional Growth: Opportunities for career advancement with continuous skill development. This role demands a balance of technical prowess, client focus, and adaptability to thrive in the dynamic field of Data & AI.

Senior Power BI Developer

Senior Power BI Developer

A Senior Power BI Developer plays a crucial role in organizations that rely on data-driven decision-making and business intelligence. This position requires a blend of technical expertise, business acumen, and leadership skills. Here's a comprehensive overview of the role: ### Key Responsibilities - **Design and Development**: Create and maintain Power BI reports, dashboards, and data models, adhering to best practices and coding standards. - **Data Modeling and Visualization**: Transform raw data into meaningful insights using advanced data modeling techniques and Data Analysis Expressions (DAX). - **Stakeholder Collaboration**: Work closely with business stakeholders, data analysts, and IT teams to understand and meet business requirements. - **Data Quality and Security**: Ensure data accuracy, consistency, and security across all Power BI solutions. - **Performance Optimization**: Debug and enhance existing code to improve efficiency and data accuracy. - **Documentation and Training**: Create standardized documentation and provide user training on Power BI solutions. ### Skills and Qualifications - **Technical Proficiency**: Expert-level skills in Power BI development, SQL, data warehouse concepts, and ETL processes. - **Analytical Abilities**: Strong problem-solving skills and the capacity to derive actionable insights from complex data. - **Leadership and Communication**: Excellent leadership skills and the ability to communicate effectively with both technical and non-technical stakeholders. - **Education and Certifications**: Typically requires a Bachelor's degree in a relevant field, with additional certifications in Power BI, Azure, or related technologies highly valued. ### Additional Responsibilities - **Version Control and Project Management**: Utilize tools like Azure DevOps for project tracking and version control. - **User Experience Design**: Create intuitive and engaging user interfaces for reports and dashboards. - **Continuous Learning**: Stay updated with the latest Power BI features and industry trends. A successful Senior Power BI Developer combines technical expertise with business acumen to drive data-informed decision-making across the organization. They must be proactive, adaptable, and committed to delivering high-quality business intelligence solutions.

Staff Data Analyst

Staff Data Analyst

A Staff Data Analyst plays a crucial role in organizations by transforming raw data into actionable insights that drive strategic decision-making. This overview provides a comprehensive look at the key aspects of the role: ### Key Responsibilities - **Data Collection and Analysis**: Gather data from various sources, clean, process, and perform statistical analyses to identify trends, patterns, and insights. - **Data Quality and Integrity**: Ensure data accuracy, consistency, and reliability through cleansing, report scrutiny, and regular audits. - **Reporting and Visualization**: Create and maintain processes for accurate and timely reports, develop visualizations using tools like Tableau or Power BI to effectively communicate findings. - **Collaboration**: Work closely with cross-functional teams to provide data-driven insights and recommendations. - **Performance Tracking**: Monitor and evaluate product and service performance, conducting data-driven reviews and providing actionable recommendations. ### Required Skills **Technical Skills**: - Proficiency in SQL and NoSQL for querying large datasets - Knowledge of programming languages such as Python or R for statistical analysis and process automation - Expertise in data visualization tools like Tableau or Power BI - Robust understanding of statistical concepts and methods **Soft Skills**: - Strong analytical thinking and problem-solving abilities - Excellent communication skills for presenting findings to non-technical stakeholders - Effective project management skills ### Importance of the Role - Facilitates improved decision-making by identifying data trends and patterns - Increases organizational efficiency through streamlined data processes - Provides a competitive advantage through effective data utilization ### Work Environment Staff Data Analysts typically work in collaborative environments, interacting with multiple stakeholders throughout the entire data analysis lifecycle. They are integral to driving data efforts and supporting strategic decision-making across the organization. By combining technical expertise, analytical thinking, and strong communication skills, a Staff Data Analyst is pivotal in helping organizations leverage data to achieve their strategic goals.