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Builds and deploys ML/DL models (classical, deep learning, LLMs) for structured/unstructured data, focusing on MLOps/LLMOps pipelines, model monitoring, and AIOps automation in a telecom/enterprise context.
Senior Learning Data Scientist building AI-driven analytics, predictive models, and dashboards to optimize training and certification programs using Python, TensorFlow, and Power BI.
Build and deploy NLP classification models for customer communications using Python, PySpark, and SQL, focusing on intent, topic, sentiment, and multi-label classification.
Build and industrialize ML and optimization models in Python for a full-stack decision-support product, integrating pipelines, testing, and cloud deployment.
Design and deploy production-grade AI agents and RAG pipelines for fintech workflows, integrating LLMs into core systems with monitoring and safety guardrails.
Build and maintain scalable ML pipelines and infrastructure for AI-driven projects using Docker, Kubernetes, and cloud platforms like AWS/GCP/Azure.
Build and maintain scalable data pipelines in Databricks, deploy ML models with MLflow, and create Power BI dashboards to support analytics and reporting.
Design and deploy AI/ML solutions—LLM-powered HR tools, semantic search, pricing analytics, and anomaly detection—using Python, MLflow, and Databricks to drive data-driven decisions.
Designs, builds, and deploys production-grade ML systems (LLMs, pipelines, and automation tools) for a mid-market professional services firm’s AI-driven internal workflows, collaborating with data scientists, engineers, and stakeholders.
Build and maintain the infrastructure and pipelines that deploy, monitor, and scale machine-learning models in production, using Docker, Kubernetes, and cloud ML platforms.
Build and optimize cloud-based data pipelines and analytics platforms using Databricks, PySpark, and Delta Lake to support real-time and batch processing for a fintech partner.
Design and maintain MLOps workflows for AI/ML and GenAI solutions, ensuring model reliability, performance, and governance in a production environment.
Lead the design and evolution of AI and data platforms using Kubernetes, microservices, and MLOps tooling to build scalable, production-grade systems.
Design and deploy ML models using Python, TensorFlow/PyTorch, and MLOps tools; collaborate with data scientists to build scalable AI systems for real-world applications.
Build and automate cloud infrastructure for AI/ML models and agents, deploying scalable pipelines on AWS/GCP/Azure with Kubernetes, CI/CD, and observability tools.
Senior MLOps Engineer builds and maintains scalable ML pipelines on AWS/Azure using SageMaker, MLFlow, and IaC tools like Terraform to deploy and monitor production models.
Builds and deploys AI/ML systems (classical models, LLMs, agentic workflows) in production using Databricks, Azure ML, and Kubernetes, with a focus on forecasting, prompt engineering, and observability.
Senior Data Engineer responsible for conceptualizing data solutions, modernizing data environments, and building data pipelines using technologies like Microsoft Azure, Power BI, Python, and Fabric/Databricks. Works with clients and cross-functional teams to deliver actionable insights and optimize data infrastructure.
Design and govern enterprise AI architectures on Databricks, including MLOps pipelines, Unity Catalog governance, and generative AI use cases, while aligning with business objectives and compliance standards.
Advises enterprises on AI/data architecture, MLOps, and GenAI orchestration. Works 10–20 hrs/month validating technical decisions for CTOs and data leaders.
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