Browse Jobs
Explore open roles across India.Explore active software roles. Sign up to apply and get AI match scores.
Top MatchesTop Job Matches
Loading jobs…
Back to Browse Jobs
Expert, AI Engineering Schneider Electric
AI/ML Enterprise Solutions Designer (India)
Expert, AI Engineering Schneider Electric
Bengaluru•On-site•3 days ago
Job description
Overview
As an ML Solution Architect, you will play a pivotal role in designing and implementing enterprise-grade AI/ML solutions that align with business objectives and technology strategy.
Responsibilities
- What’s the role?
- As an ML Solution Architect, you will play a pivotal role in designing and implementing enterprise-grade AI/ML solutions that align with business objectives and technology strategy. This is a Senior position requiring a blend of technical depth, architectural vision, and business acumen. You will define frameworks, guide engineering teams, and ensure scalability, security, and co mpliance across ML deployments.
- Lead technical design reviews, mentor engineering teams, and evaluate current AI/ML technologies (LLMs, RAG, AutoML).
- Drive POCs and convert prototypes into production-grade models using TensorFlow, PyTorch, LangChain, and modern cloud platforms (AWS, GCP, Azure).
- Develop APIs and microservices enabling seamless integration of AI capabilities into products.
- Implement strong MLOps practices—CI/CD for ML, automated retraining, model performance monitoring, and governance.
- Partner with data engineering to ensure robust pipelines, high-quality data, lineage, and feature engineering standards.
- Collaborate with product, engineering, consulting partners, and business stakeholders to align solutions with strategic goals.
- Advocate for responsible AI practices, explainability, ethical deployment, and build reusable frameworks/architectures.
- Choose what you’re giving feedback on
- Report this listing
Qualifications
- Strong experience with enterprise architecture frameworks, design patterns, cloud‑native architectures, and modern integration approaches.
- Proven ability to design, build, and deploy systems using diverse model types (language, vision, traditional ML) and the supporting infrastructure.
- Skilled across multiple programming languages, major cloud platforms (AWS/GCP/Azure), and architectural modelling tools.
- Background in consulting or prior roles with cloud providers (AWS/GCP), or a senior ML engineer/solution architect who has grown into a broader architectural role.
- AI/ML Tools: TensorFlow, PyTorch, Scikit-Learn, Keras, H2O, LLMs, RAG.
- Cloud Platforms: AWS, Azure, GCP with strong knowledge of cloud-native ML services.
- MLOps: CI/CD for ML, Docker, Kubernetes, MLflow, Kubeflow.
- Data Engineering: ETL pipelines, big data frameworks (Spark, Hadoop).
- Programming: Python, SQL; familiarity with API development and microservices.