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Builds enterprise-grade Generative AI and AI/ML platform services, APIs, and agentic workflows using Python, FastAPI, Kafka, and Kubernetes to accelerate AI adoption for clients in finance and other industries.
Build and deploy production-grade AI systems, including LLM-powered agents and analytics, to enhance customer experience and workforce productivity using NLP, retrieval-augmented generation, and agentic workflows.
Build and maintain scalable MLOps pipelines on GCP and Vertex AI, automating model deployment, monitoring, and retraining for enterprise AI solutions.
Build and deploy production-grade AI systems, including LLM-powered agents and analytics, to enhance customer experience and workforce productivity.
Build and improve ML systems that make real-time underwriting decisions for Affirm’s buy-now-pay-later checkout, using Python, PyTorch, and distributed data tools.
Build and deploy AI/ML models and MLOps pipelines for government AI projects, integrating generative AI into scalable web applications and collaborating with Army stakeholders.
Build and deploy ML models to detect and prevent real-time debit transaction fraud using Python, Spark, and gradient boosting, while collaborating with engineers and product teams to protect customers.
Build, deploy, and scale AI/ML models and pipelines for Wave’s fintech platform, ensuring reliability, governance, and integration with AWS and MLOps tools.
Design and implement cloud infrastructure and MLOps solutions for AI teams, advising clients on GPU cloud technologies and optimizing ML pipelines using tools like Terraform, Kubernetes, and Python.
Role : The Manager AI Products is the primary builder within the AI Hub, responsible for turning architectural designs and product specifications into working, production-deployed AI systems. This role spans the full…
Designs and builds scalable GCP-based data pipelines, MLOps workflows, and cloud-native apps using Dataflow, Kubeflow, BigQuery, Python, and Django.
Build and maintain GCP data pipelines and MLOps frameworks using Dataflow, Apache Beam, BigQuery, Kubeflow, and Python for enterprise clients.
DevOps engineer to build and maintain CI/CD, monitoring, and Kubernetes-based infrastructure for an internal ML platform used for training models and AI agents.
Lead the design and deployment of large-scale generative AI voice and speech systems, building reusable ML infrastructure and guiding cross-functional teams to integrate cutting-edge AI into products.
Leads a team building and operating an MLOps platform and multi-agent environment, overseeing the full ML lifecycle, AI agent integration, and scalable infrastructure for a large corporation.
Build and maintain ML feature pipelines for batch, streaming, and nearline scenarios to power recommendations and analytics at a top Russian streaming platform.
DevOps engineer builds and maintains CI/CD, monitoring, and Kubernetes-based infrastructure for a company-wide ML platform used in decision-making, risk metrics, forecasting, and AI agents.
Build and maintain ML pipelines and AI infrastructure for banking products, using Kubernetes, Docker, Terraform, and cloud platforms to deploy and monitor ML models at scale.
Build and maintain automated tests and CI/CD pipelines for distributed AI infrastructure running on GPU/CPU clusters, Kubernetes, and Slurm.
Build and maintain ML models to analyze 4G/5G network performance, troubleshoot issues, and create dashboards for stakeholders using Python, Spark, and cloud tools.
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