The Role
Design, build, and maintain production-grade data and machine learning systems. Responsibilities include scalable data pipelines, model deployment, CI/CD for ML workflows, MLOps infrastructure, observability, model optimization, automation, governance, and troubleshooting. Collaborate with data scientists, product managers, and engineering leads to productionize AI solutions while ensuring reliability, security, compliance, and performance across batch, streaming, and real-time environments.
Summary Generated by Built In
Project Role : Custom Software Engineer
Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Must have skills : Machine Learning Operations
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
As a Data Ops, ML Engineer, you will play a critical role in designing, building, and maintaining production-grade data and machine learning systems that power intelligent enterprise products and services. The individual will lead the development of scalable data pipelines, model deployment frameworks, and MLOps infrastructure to enable reliable, secure, and high-performing AI capabilities across business value streams.
ESSENTIAL JOB FUNCTIONS AND RESPONSIBILITIES:
Data Pipeline Development: Build and maintain robust pipelines to collect, process, and transform structured and unstructured data for model training and inference.
ML Model Deployment: Design, deploy, and manage machine learning models in production environments with a focus on automation, scalability, and performance.
CI/CD for ML Workflows: Develop and maintain continuous integration and delivery of pipelines for data and ML workflows, including testing, monitoring, and automated retraining.
MLOps Infrastructure: Implement and refine infrastructure components such as feature stores, model registries, and orchestration frameworks.
Cross-Functional Collaboration: Partner with data scientists, product managers, and engineering leads to translate experimental models into production-ready services.
Observability & Monitoring: Create frameworks to monitor data pipelines and ML models for drift, anomalies, and performance degradation.
Optimization: Tune model serving for latency and cost efficiency across batch, streaming, and real-time environments.
Automation: Identify and automate repetitive tasks to improve reliability and development velocity.
Governance & Compliance: Manage data lineage, versioning, and governance to ensure reproducibility and regulatory compliance.
Troubleshooting: Diagnose and resolve issues in real-time production environments, ensuring minimal disruption to business operations.
KNOWLEDGE, SKILLS, AND ABILITIES:
5+ years of hands-on experience in data engineering, ML engineering, or backend software engineering for AI-driven products.
Strong programming skills in Python, SQL, and at least one compiled language (e.g., Go, Java, Scala).
Proven experience with MLOps tools and platforms (e.g., MLflow, Airflow, Kubeflow, SageMaker, AI, Databricks).
Deep understanding of data processing frameworks (Spark, Flink, Beam) and cloud architectures (AWS, Azure, GCP).
Experience with containerization and orchestration (Docker, Kubernetes).
Familiarity with real-time data processing and event-driven systems (Kafka, Pub/Sub).
Expertise in feature store management, model versioning, and pipeline observability.
Knowledge of DevOps and infrastructure-as-code tools (Terraform, CloudFormation).
Strong analytical and problem-solving skills with attention to detail.
Effective communication and collaboration skills across technical and business teams.
EDUCATION AND TRAINING:
Bachelor s degree in Computer Science, Engineering, or a related technical field.
5+ years of experience in data engineering, ML engineering, or backend software development for AI-driven products.
Experience with CRM and ERP systems (e.g., Salesforce, Workday) preferred.
Experience implementing Responsible AI principles, including bias monitoring and explainability.
Other Qualifications
The Winning Way behaviors that all employees need in order to meet the expectations of each other, our customers, and our partners.
Communicate with Clarity - Be clear, concise and actionable. Be relentlessly constructive. Seek and provide meaningful feedback.
Act with Urgency - Adopt an agile mentality - frequent iterations, improved speed, resilience. 80/20 rule – better is the enemy of done. Don t spend hours when minutes are enough.
Work with Purpose - Exhibit a We Can mindset. Results outweigh effort. Everyone understands how their role contributes. Set aside personal objectives for team results.
Drive to Decision - Cut the swirl with defined deadlines and decision points. Be clear on individual accountability and decision authority. Guided by a commitment to and accountability for customer outcomes.
Own the Outcome - Defined milestones, commitments and intended results. Assess your work in context, if you re unsure, ask. Demonstrate unwavering support for decisions.
COMMENTS:
The above statements are intended to describe the general nature and level of work being performed by individuals in this position. Other functions may be assigned, and management retains the right to add or change the duties at any time.15 years full time education
Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Must have skills : Machine Learning Operations
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
As a Data Ops, ML Engineer, you will play a critical role in designing, building, and maintaining production-grade data and machine learning systems that power intelligent enterprise products and services. The individual will lead the development of scalable data pipelines, model deployment frameworks, and MLOps infrastructure to enable reliable, secure, and high-performing AI capabilities across business value streams.
ESSENTIAL JOB FUNCTIONS AND RESPONSIBILITIES:
Data Pipeline Development: Build and maintain robust pipelines to collect, process, and transform structured and unstructured data for model training and inference.
ML Model Deployment: Design, deploy, and manage machine learning models in production environments with a focus on automation, scalability, and performance.
CI/CD for ML Workflows: Develop and maintain continuous integration and delivery of pipelines for data and ML workflows, including testing, monitoring, and automated retraining.
MLOps Infrastructure: Implement and refine infrastructure components such as feature stores, model registries, and orchestration frameworks.
Cross-Functional Collaboration: Partner with data scientists, product managers, and engineering leads to translate experimental models into production-ready services.
Observability & Monitoring: Create frameworks to monitor data pipelines and ML models for drift, anomalies, and performance degradation.
Optimization: Tune model serving for latency and cost efficiency across batch, streaming, and real-time environments.
Automation: Identify and automate repetitive tasks to improve reliability and development velocity.
Governance & Compliance: Manage data lineage, versioning, and governance to ensure reproducibility and regulatory compliance.
Troubleshooting: Diagnose and resolve issues in real-time production environments, ensuring minimal disruption to business operations.
KNOWLEDGE, SKILLS, AND ABILITIES:
5+ years of hands-on experience in data engineering, ML engineering, or backend software engineering for AI-driven products.
Strong programming skills in Python, SQL, and at least one compiled language (e.g., Go, Java, Scala).
Proven experience with MLOps tools and platforms (e.g., MLflow, Airflow, Kubeflow, SageMaker, AI, Databricks).
Deep understanding of data processing frameworks (Spark, Flink, Beam) and cloud architectures (AWS, Azure, GCP).
Experience with containerization and orchestration (Docker, Kubernetes).
Familiarity with real-time data processing and event-driven systems (Kafka, Pub/Sub).
Expertise in feature store management, model versioning, and pipeline observability.
Knowledge of DevOps and infrastructure-as-code tools (Terraform, CloudFormation).
Strong analytical and problem-solving skills with attention to detail.
Effective communication and collaboration skills across technical and business teams.
EDUCATION AND TRAINING:
Bachelor s degree in Computer Science, Engineering, or a related technical field.
5+ years of experience in data engineering, ML engineering, or backend software development for AI-driven products.
Experience with CRM and ERP systems (e.g., Salesforce, Workday) preferred.
Experience implementing Responsible AI principles, including bias monitoring and explainability.
Other Qualifications
The Winning Way behaviors that all employees need in order to meet the expectations of each other, our customers, and our partners.
Communicate with Clarity - Be clear, concise and actionable. Be relentlessly constructive. Seek and provide meaningful feedback.
Act with Urgency - Adopt an agile mentality - frequent iterations, improved speed, resilience. 80/20 rule – better is the enemy of done. Don t spend hours when minutes are enough.
Work with Purpose - Exhibit a We Can mindset. Results outweigh effort. Everyone understands how their role contributes. Set aside personal objectives for team results.
Drive to Decision - Cut the swirl with defined deadlines and decision points. Be clear on individual accountability and decision authority. Guided by a commitment to and accountability for customer outcomes.
Own the Outcome - Defined milestones, commitments and intended results. Assess your work in context, if you re unsure, ask. Demonstrate unwavering support for decisions.
COMMENTS:
The above statements are intended to describe the general nature and level of work being performed by individuals in this position. Other functions may be assigned, and management retains the right to add or change the duties at any time.15 years full time education
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
Equal Employment Opportunity Statement
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
Skills Required
- At least 5 years of experience in data engineering, ML engineering, or backend software development for AI-driven products
- Machine Learning Operations experience
- 15 years of full-time education
- Bachelor's degree in Computer Science, Engineering, or a related technical field
- Strong programming skills in Python and SQL
- Proficiency in at least one compiled language, such as Go, Java, or Scala
- Experience with MLOps tools and platforms, such as MLflow, Airflow, Kubeflow, SageMaker, or Databricks
- Understanding of data processing frameworks, including Spark, Flink, or Beam
- Experience with cloud architectures such as AWS, Azure, or GCP
- Experience with Docker and Kubernetes
- Familiarity with real-time data processing and event-driven systems, such as Kafka or Pub/Sub
- Expertise in feature stores, model versioning, and pipeline observability
- Knowledge of DevOps and infrastructure-as-code tools, such as Terraform or CloudFormation
- Experience implementing Responsible AI principles, including bias monitoring and explainability
- Experience with CRM and ERP systems, such as Salesforce or Workday
- Strong analytical and problem-solving skills
- Effective communication and collaboration skills across technical and business teams
Accenture Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Accenture and has not been reviewed or approved by Accenture.
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Healthcare Strength — Pay is considered competitive when paired with robust insurance options and other perks that compare well with large consulting and IT services peers. Multiple national medical plan options plus dental and vision are positioned as a core strength of the overall package.
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Retirement Support — Retirement support is positioned as a standout feature through a 401(k) dollar-for-dollar match up to a set percentage after eligibility. The package is reinforced by additional financial programs such as savings tools and related resources.
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Parental & Family Support — Parental and caregiving supports are presented as a meaningful benefit differentiator through substantial paid parental leave and multiple caregiver-oriented programs. Backup care and fertility/adoption/surrogacy navigation and reimbursements add breadth to family support beyond leave alone.
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The Company
What We Do
Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services—all powered by the world’s largest network of Advanced Technology and Intelligent Operations centers. Our 500,000+ people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Visit us at www.accenture.com.
