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Build and ship full-stack features for an AI platform that accelerates due diligence for private equity funds using Python, FastAPI, React, and LLM tooling.
Build and maintain APIs and services for a B2B fintech platform using Go, Python, VueJS, and Postgres, with exposure to AI/ML tooling and AWS infrastructure.
Lead full-stack Rails 8 development and integrate LLMs to build AI-powered investing tools for Dividend.com and MutualFunds.com, optimizing performance and user experience.
Build and deploy full-stack generative AI applications using LLMs, vector databases, and modern frontend/backend stacks to deliver AI-powered features for enterprise clients.
Lead a team building a knowledge graph that maps scientific experts from publications and clinical trials using graph databases, vector embeddings, and LLM workflows to power expert discovery and AI-driven insights.
Analyze and deploy GenAI-driven analytics to enhance decision-making in life insurance, using Python, Databricks, and prompt engineering to quantify business impact.
Build and deploy production-grade AI and GenAI solutions, integrating models into real-world systems using Python, TensorFlow/PyTorch, and cloud-native tools.
Build and deploy production-grade AI and Generative AI systems in Python, integrating LLMs and vector databases with cloud-native pipelines on AWS.
Build and operate an LLM-powered content generation pipeline with safety guardrails, RAG grounding, and image generation, deployed on AWS serverless services.
We are looking for a Full Stack Developer to build intuitive, high-performance user interfaces and robust backend services for our AI-powered products. You will work at the intersection of traditional software…
Design and implement cloud infrastructure, CI/CD pipelines, and GenAI solutions using Azure, AWS, Python, and Kubernetes.
Builds and maintains LLM training, fine-tuning, and inference pipelines on AWS using PyTorch, vLLM, and FastAPI to power a healthcare platform’s AI features.
Builds and deploys AI/ML models from prototype to production, focusing on MLOps pipelines, model monitoring, and scalable infrastructure for public-sector and energy clients using PyTorch, TensorFlow, and cloud platforms like AWS SageMaker.
Own AI product vision, turning business needs into measurable ML use-cases, and steer a cross-functional team to deliver sovereign-energy solutions using PyTorch, Hugging Face, and MLOps tooling.
Builds AI/ML models and data pipelines in Python to turn business problems into analytics solutions, from data collection to impact.
Build and deploy generative-AI features and agents using Python, LLMs, and cloud services; develop full-stack web apps with Django/Flask and CI/CD pipelines.
Build and deploy LLM-driven AI agents and generative solutions using Azure OpenAI, LangChain, and Hugging Face, focusing on RAG pipelines, multi-agent systems, and responsible AI.
Lead AI engineering and architecture for financial-services clients, designing GenAI and multi-agent systems, deploying RAG pipelines, and aligning solutions with EU regulations and enterprise standards.
Build, optimize, and deploy large language and multimodal models for industrial use, focusing on training, compression, RAG, and agent workflows.
Deploys and fine-tunes vision-language and visual foundation models for industrial clients, integrating them into manufacturing, logistics, and other operational systems.
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