This role is for of Weekday’s clients
Min Experience: 12+ years
Location: Mumbai, Maharashtra, India
JobType: full-time
The incumbent shall be responsible for leading AI/ML operational and platform engineering functions across the organization. The focus of this role is to provide and ensure technical excellence in the AI/ML technology landscape and drive the adoption of AI solutions across business units.
Requirements
- 10 years’ experience in IT, software engineering, or data science related positions
- 5 years of direct experience with AI/ML technologies, platforms, and solutions in production environments.
- 3 years in technical leadership, architecture, or senior engineering role.
- Experience in Designing and implementing large-scale AI/ML solutions and systems.
- Experience working with cloud platforms and understanding of cloud-native architectures.
- Experience with DevOps, CI/CD pipelines, and containerized deployments.
- Deep dive technical investigation, analysis and troubleshooting across AI/ML technology stacks, frameworks, and infrastructure using appropriate diagnostic and monitoring tools.
- Provide technical leadership and mentoring to AI Engineering, Data Engineering, and Platform teams, fostering a culture of technical excellence and continuous improvement.
- Drive the enablement of AI/ML platforms, tooling, and best practices across the organization.
- Provide architecture guidance and technical oversight for AI/ML solution design, implementation, and optimization.
- Lead the evaluation, selection, and integration of AI/ML tools, frameworks, and cloud services (e.g., Azure AI, AWS SageMaker, Google Vertex AI).
- Establish and maintain monitoring, logging, and observability standards for AI/ML systems and models.
- Investigate opportunities for optimization of AI/ML technology stacks, including model performance tuning and infrastructure efficiency.
- Work with solution architects and business stakeholders to translate business requirements into technical AI/ML solutions.
- Provide support and enablement for containerized and cloud-native environments, specifically Kubernetes and serverless platforms.
- Ensure compliance, security, and governance best practices are implemented across all AI/ML solutions.
- Stay current with emerging AI/ML technologies, frameworks, and industry best practices.
- Machine Learning Frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost)
- AI/ML Platforms (Azure AI, AWS SageMaker, Google Vertex AI, Databricks)
- Large Language Models and GenAI (transformers, RAG, prompt engineering, LLMOps)
- Data Processing and Analytics (Spark, Hadoop, pandas, SQL)
- Cloud Platforms (Azure, AWS, GCP)
- Container Orchestration (Kubernetes, Docker)
- MLOps and Model Deployment tools (MLflow, Kubeflow, DVC, Weights & Biases)
- Data Engineering and ETL tools
- Monitoring, Logging, and Observability tools (Prometheus, ELK, Grafana, DataDog)
- Scripting and Programming Languages (Python, Java, Scala, SQL)
- Git and Version Control Systems
Must-have skills
AI/ML technology, AI/ML operational
Good-to-have skills
Engineering Manager
