AI / ML Engineer
Accenture in India · Bengaluru, Karnataka, India
- On site
- Full-time
- ₹1,500,000 / year
- Bengaluru, Karnataka, India
Job highlights
- Develop and deploy AI/ML systems using cloud services.
- Apply GenAI models and deep learning techniques.
- Manage ML model lifecycle and production pipelines.
- Ensure high-quality standards and operational efficiency.
- Collaborate with teams to provide AI solutions.
About the role
AI / ML Engineer
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.
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 systems, 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 a minimum of 5 years of experience in Machine Learning Operations. This position is based at our Bengaluru office. A 15 years full-time education is required.
Key skills/competency
- Machine Learning Operations (MLOps)
- AI/ML Engineering
- Cloud AI Services
- GenAI Models
- Deep Learning
- Neural Networks
- Chatbots
- Image Processing
- ML Pipeline Development
- Model Deployment
Skills & topics
- AI Engineer
- ML Engineer
- Machine Learning Operations
- MLOps
- Cloud AI
- GenAI
- Deep Learning
- Python
- AWS
- Docker
How to get hired
- Tailor your resume: Highlight your 5+ years of experience in Machine Learning Operations, MLOps, and cloud AI services. Quantify achievements in ML pipeline development and model deployment.
- Showcase technical skills: Emphasize proficiency in tools like Docker, FastAPI, AWS, GitHub Actions, MLflow, and Airflow. Detail experience with multi-cloud environments and machine learning frameworks.
- Craft a strong cover letter: Express your understanding of operationalizing ML models and applying GenAI. Align your experience with Accenture's focus on production-ready AI solutions.
- Prepare for technical interviews: Be ready to discuss MLOps best practices, model lifecycle management, CI/CD for ML, and cloud deployment strategies. Expect scenario-based questions on optimizing ML models for production.
- Research Accenture: Understand Accenture's consulting services, particularly in AI and cloud transformation, and their commitment to innovation.
Technical preparation
Master Python for ML tasks.,Deep dive into MLOps principles and tools.,Practice Docker and FastAPI for deployment.,Familiarize with AWS ML services.
Behavioral questions
Describe a challenging ML deployment.,How do you ensure model reproducibility?,Share experience improving ML processes.,How do you handle production ML issues?
Frequently asked questions
- What are the key MLOps responsibilities for an AI/ML Engineer at Accenture India?
- As an AI/ML Engineer at Accenture India, you will be responsible for developing and deploying AI/ML systems, operationalizing machine learning models, managing the end-to-end ML lifecycle, implementing CI/CD pipelines, and ensuring robust model deployment and monitoring. This includes applying GenAI models and deep learning techniques in production-ready environments.
- What specific cloud AI services and tools are crucial for this AI/ML Engineer role at Accenture?
- This role requires strong experience with cloud AI services and deployment strategies, particularly on AWS. Proficiency in tools like Docker, FastAPI, GitHub Actions, MLflow, and Airflow is essential for building and managing ML pipelines, model deployment, CI/CD, and model lifecycle management.
- How does Accenture approach GenAI and deep learning within their AI/ML Engineer roles?
- Accenture seeks AI/ML Engineers who can apply GenAI models as part of solutions. This includes leveraging deep learning, neural networks, chatbots, and image processing techniques to build advanced AI applications and systems with production-ready quality.
- What is the expected educational background for an AI/ML Engineer at Accenture India?
- Accenture requires a minimum of 15 years of full-time education for this AI/ML Engineer position. This typically translates to a Bachelor's or Master's degree in a relevant field, following a structured academic path.
- Can candidates with experience in multiple cloud platforms apply for this AI/ML Engineer role at Accenture?
- Yes, candidates with Multi-Cloud skills are highly valued for this AI/ML Engineer role at Accenture. Experience across different cloud platforms demonstrates adaptability and a comprehensive understanding of cloud-based AI service deployment strategies.
- What is the minimum experience required for the AI / ML Engineer position at Accenture?
- The minimum experience required for this AI / ML Engineer position is 5 years, specifically in Machine Learning Operations (MLOps). This ensures candidates have a solid foundation in operationalizing ML models.
- What are the primary responsibilities related to model deployment in this AI/ML Engineer role?
- Primary responsibilities include deploying machine learning models as robust, secure services, containerizing them with Docker, serving them via FastAPI on AWS for low-latency predictions, and managing both batch and real-time inference scenarios.