AI / ML Engineer
Bengaluru Full-time Mid-level Machine Learning Engineering
At a glance
Mid-level Machine Learning Engineering role at Accenture. Bengaluru · full-time.
Pay not stated Machine Learning Engineering salary
RoleMachine Learning Engineering
SeniorityMid-level
LocationBengaluru
EmploymentFull-time
PostedOct 30, 2026 · 2d ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Build and maintain scalable model-training pipelines for machine learning projects
- Package models into secure services and support both batch and real-time predictions
- Automate ML testing, validation and releases through continuous integration and delivery workflows
- Track models from versioning through production, then watch for drift and trigger tuning or retraining
- Review team processes, share technical knowledge and help resolve issues across teams
What you bring
- At least five years working in Machine Learning Operations
- Hands-on experience with MLflow and Airflow is required
- You have completed fifteen years of full-time education
- You can work with cloud AI services, multiple cloud environments and machine learning frameworks
Who this fits
This mid-level role suits an engineer focused on putting machine learning models into production and maintaining them. The position is based at the Bengaluru office.
From the employer
Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
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 an Machine Learning Engineer/MLOps Expert, you will engage in the operationalization of Machine Learning Models that leverage artificial intelligence tools and cloud AI services. Your typical day will involve designing and implementing production-ready ML system, ensuring high-quality standards are met.
Roles & Responsibilities:
- Continuously evaluate and improve existing processes to enhance efficiency.
- Engage with multiple teams and contribute on key decisions.
- Provide solutions to problems for their immediate team and across multiple teams.
- Facilitate knowledge sharing sessions to enhance team skills and capabilities.
- Monitor project progress and ensure alignment with strategic goals.
Professional & Technical Skills:
- ML Pipeline Development: Design, build, and maintain scalable pipelines for model training to support our AI initiatives.
- Model Deployment & Serving: Deploy machine learning models as robust, secure services – containerize models with Docker and serve them via FastAPI on AWS – ensuring low-latency predictions for marketing applications. Manage Batch inference and Realtime inference.
- CI/CD Automation: Implement continuous integration and delivery (CI/CD) pipelines for ML projects. Automate testing, model validation, and deployment workflows using tools like GitHub Actions to accelerate delivery.
- Model Lifecycle Management: Orchestrate the end-to-end ML lifecycle, including versioning, packaging, and registering models. Maintain a model repository/registry (MLflow or similar) for reproducibility and governance from experimentation through production. Experience on MLFlow and Airflow is mandatory
- Monitoring & Optimization: Monitor model performance, data drift, and system health in production. Set up alerts and dashboards and proactively initiate model retraining or tuning to sustain accuracy and efficiency over time.
- Must To Have Skills: Proficiency in Machine Learning Operations.
- Strong understanding of cloud-based AI services and deployment strategies.
- Should have Multi Cloud skills
- Experience with Machine learning frameworks
- Ability to implement and optimize machine learning models for production environments.
Additional Information:
- The candidate should have minimum 5 years of experience in Machine Learning Operations.
- This position is based at our Bengaluru office.
- A 15 years full time education is required.15 years full time education
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
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 an Machine Learning Engineer/MLOps Expert, you will engage in the operationalization of Machine Learning Models that leverage artificial intelligence tools and cloud AI services. Your typical day will involve designing and implementing production-ready ML system, ensuring high-quality standards are met.
Roles & Responsibilities:
- Continuously evaluate and improve existing processes to enhance efficiency.
- Engage with multiple teams and contribute on key decisions.
- Provide solutions to problems for their immediate team and across multiple teams.
- Facilitate knowledge sharing sessions to enhance team skills and capabilities.
- Monitor project progress and ensure alignment with strategic goals.
Professional & Technical Skills:
- ML Pipeline Development: Design, build, and maintain scalable pipelines for model training to support our AI initiatives.
- Model Deployment & Serving: Deploy machine learning models as robust, secure services – containerize models with Docker and serve them via FastAPI on AWS – ensuring low-latency predictions for marketing applications. Manage Batch inference and Realtime inference.
- CI/CD Automation: Implement continuous integration and delivery (CI/CD) pipelines for ML projects. Automate testing, model validation, and deployment workflows using tools like GitHub Actions to accelerate delivery.
- Model Lifecycle Management: Orchestrate the end-to-end ML lifecycle, including versioning, packaging, and registering models. Maintain a model repository/registry (MLflow or similar) for reproducibility and governance from experimentation through production. Experience on MLFlow and Airflow is mandatory
- Monitoring & Optimization: Monitor model performance, data drift, and system health in production. Set up alerts and dashboards and proactively initiate model retraining or tuning to sustain accuracy and efficiency over time.
- Must To Have Skills: Proficiency in Machine Learning Operations.
- Strong understanding of cloud-based AI services and deployment strategies.
- Should have Multi Cloud skills
- Experience with Machine learning frameworks
- Ability to implement and optimize machine learning models for production environments.
Additional Information:
- The candidate should have minimum 5 years of experience in Machine Learning Operations.
- This position is based at our Bengaluru office.
- A 15 years full time education is required.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.
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