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AI/ML Engineer (R-00194)
Build and deploy AI/ML models and RAG systems in AWS GovCloud for secure government workflows, using Python and FedRAMP-authorized services.
Senior Machine Learning Engineer, Physical AI
Owns the full ML lifecycle for physical AI, from sensor data pipelines to deploying optimized models on constrained hardware, collaborating with embedded teams to ensure reliability and performance in real-world devices.
Solution Engineer Quantexa
Build and deliver transaction-monitoring solutions on the Quantexa platform, designing data pipelines, entity resolution, and analytics to meet AML/fraud use cases for a major bank.
Senior Data Scientist
Build and maintain AI/ML models for credit risk and pricing in a fintech lending platform, deploying production systems in Python/AWS and running rigorous A/B tests to validate business impact.
Senior Software Engineer (AI/ML) - Part-time | Remote
Build and scale AI agents and ML pipelines using Python, PyTorch/TensorFlow, and agent frameworks like LangChain; integrate LLMs, vector DBs, and cloud-native systems.
Senior Engineer - Data Science
Build and deploy ML/AI models (classical, deep learning, LLMs) with MLOps/LLMOps pipelines, focusing on production-grade solutions and API integration for enterprise use.
Associate Lead - Data Science
Lead AI/ML teams to build, deploy, and govern production-grade generative and agentic AI systems using LLMs, RAG, and orchestration tools.
Engineer - Data Science
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.
Data Engineer
Builds and maintains cloud-based data pipelines and warehouses on GCP for a telecom giant, enabling analytics and business insights via ETL, SQL, Python, and data governance frameworks while collaborating with data scientists and BI teams.
Senior Data Scientist - AdTech (6-month Contract)
Senior Data Scientist builds, deploys, and optimizes ML models at scale using Python, Spark, and AWS, driving data-driven solutions from experimentation to production.
Vice President, Data Science
About the Role: Grade Level (for internal use): 15 The Team: Enterprise Solutions (ES) Technology is a powerful combination of world class people and businesses with a shared mission and legacy of evolving markets and…
Machine Learning Engineer
Build and deploy AI agents, RAG systems, and multi-agent workflows using LLMs in a DevOps environment to solve complex business problems.
Lead Data Engineer - Experimentation Platform - 1633
Lead a data engineering team to design and build scalable experimentation platforms, batch/streaming pipelines, and analytics-ready datasets using Python, Spark, Databricks, Snowflake, and Kafka.
Data Engineer - Machine Learning
Build and maintain scalable data pipelines and ML workflows for a banking analytics project using PySpark, SQL, AWS SageMaker, and MLOps tools like MLflow.
Senior Data Scientist
Lead the design and deployment of predictive models and ML solutions in Python/SQL, collaborating with stakeholders to drive data-driven decisions and mentor junior team members.
Senior Machine Learning Engineer
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.
MLOps Engineer
Build and maintain the infrastructure and pipelines that deploy, monitor, and scale machine-learning models in production, using Docker, Kubernetes, and cloud ML platforms.
Lead Data Scientist
Lead the design and deployment of AI/ML models and advanced analytics to solve business challenges, mentor a data-science team, and drive enterprise-wide AI adoption.
0825 - 283(4LAT) | Machine Learning Engineer (On-Site Consultant)
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.
Mid AI & ML Engineer
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.