AI / ML Engineer
Accenture
- Location
- Onsite (Bengaluru, Karnataka)
- Employment
- Full-time
- Level
- Senior Level
Posted Today
About the Role
Accenture is seeking an AI/ML Engineer to operationalize machine learning models and design production-ready ML systems. The role involves managing team decisions, mentoring junior professionals, and continuously improving workflows to deliver high-quality AI solutions.
Skills
Machine Learning Operations
Python
ML Pipeline Development
Model Deployment
Docker
FastAPI
CI/CD Automation
GitHub Actions
MLflow
Airflow
Model Lifecycle Management
Multi Cloud
Deep Learning
Neural Networks
Chatbots
Image Processing
Full job details
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:
- Expected to be an SME.
- Collaborate and manage the team to perform.
- Responsible for team decisions.
- Engage with multiple teams and contribute on key decisions.
- Provide solutions to problems for their immediate team and across multiple teams.
- Mentor junior professionals to enhance their skills and knowledge.
- Continuously evaluate and improve existing processes and workflows.
Professional & Technical Skills:
- Strong Engineering experience with advance python 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 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.
- Good exposure of cloud based services including AI services.
- Must have thorough understanding of infrastructure need for MLOps implementation.
- Must have python skills.
- 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:
- Expected to be an SME.
- Collaborate and manage the team to perform.
- Responsible for team decisions.
- Engage with multiple teams and contribute on key decisions.
- Provide solutions to problems for their immediate team and across multiple teams.
- Mentor junior professionals to enhance their skills and knowledge.
- Continuously evaluate and improve existing processes and workflows.
Professional & Technical Skills:
- Strong Engineering experience with advance python 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 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.
- Good exposure of cloud based services including AI services.
- Must have thorough understanding of infrastructure need for MLOps implementation.
- Must have python skills.
- 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
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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.