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Lead Software Engineer
1 week ago
Responsibilities:
- Designing and developing scalable MLOps frameworks like Kubeflow, Ray and other supported frameworks to support models based solution as per client requirements.
- Managing Azure OpenAI, OpenAI, Bedrock and Vertex AI platforms.
- Managing RAG based solutions
- Managing hosted and selfhosted agents
- Hosted & Selfhosted LLM resources capacity planning
- ML & LLM Deployment and operationalizing MLOps, in particular, implementing:
~Model hyperparameter optimization
~Model evaluation and explainability
~Model training and automated retraining
~Model workflows from onboarding, operations to decommissioning using MLFlow or any other framework
~Model version tracking & governance
~Data archival & version management
~Model and drift monitoring
- Creating and using benchmarks, metrics, and monitoring to measure and improve services.
- Providing best practices and executing POC for automated and efficient model operations at scale.
- Being the MLOps expert for the sales team, providing technical design solutions to support RFPs.
- MLOps Engineers work closely with Data Scientists and Data Engineers in the Data Science Team from the start of the project.
Qualifications
- Deep quantitative/programming background with a degree (Bachelor's, Master's, or Ph.D.) in a highly analytical discipline, like Statistics, Economics, Computer Science, Mathematics, Operations Research, etc.
- Total of 6-10 years of experience in managing machine learning projects end-to-end, with the last 18 months focused on MLOps.
- Monitoring Build & Production systems using automated monitoring and alarm tools.
- Knowledge of machine learning frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn.
- Experience with MLOps tools such as ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC).
- Experience in supporting model builds and model deployment for IDE-based models and autoML tools, experiment tracking, model management, version tracking & model training ( Ray, Kubeflow, MLflow, neptune.ai), model hyperparameter optimization, model evaluation, and explainability (SHAP, Tensorboard).
- Experience in Model Benchmarking techniques
- Experience Prompt batching techniques and benchmarking
- Hands on experience in Managing LLMs (Hosted and Self Hosted) and Embeddings
- Experience in RAG setup & Vector DB concepts
- Experience GenAI Agents and Concepts
- Experience with container technologies (Docker, Kubernetes, EKS, ECS).
- Experience with multiple cloud providers (AWS, GCP, Azure, etc).
Additional Information
At Freshworks, we are creating a global workplace that enables everyone to find their true potential, purpose, and passion irrespective of their background, gender, race, sexual orientation, religion and ethnicity. We are committed to providing equal opportunity for all and believe that diversity in the workplace creates a more vibrant, richer work environment that advances the goals of our employees, communities and the business.
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