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Build AI-powered engineering tools that streamline storage device design, testing, and manufacturing using Python, React, REST APIs, and LLM/RAG pipelines.
Lead the engineering and deployment of production-grade generative AI systems, including LLMs and multimodal models, with a focus on scalability, reliability, and MLOps in an enterprise energy-tech environment.
Build and deploy ML/AI models (including GenAI) for pricing, personalization, and fraud detection in a restaurant-tech SaaS platform.
Lead a team building production-grade AI agents and applications for a regulated fintech platform, from POC to deployment, using LLMs, RAG, and vector databases.
Build and deploy enterprise-grade AI and ML solutions, including Generative AI apps with LLMs, RAG, and vector databases, to solve complex business problems for SAP customers.
Build and deploy enterprise-grade AI and ML solutions for SAP customers, integrating LLMs, RAG, and vector databases into production systems.
About ExTrac ExTrac is a decision intelligence company used by governments, defence organisations, financial institutions, and corporates operating in complex, fast-moving environments. Our capabilities fuse curated…
Own SQL Server databases and ETL pipelines, tune performance, and build vector-aware data stores to power AI features for an aerospace manufacturer.
Build and deploy LLM-based AI pipelines and services, integrating them into corporate products and systems using Python, RAG, and vector databases.
Build AI-powered finance copilots and chat experiences that help companies automate spend management using LLMs, retrieval, and structured financial data in a production-grade system.
Build and improve AI-powered products using LLMs, agentic frameworks, and vector databases to solve business problems across fintech, healthcare, and other industries.
Build and deploy AI-powered expert systems for legal, tax, and compliance using RAG, custom agents, and MLOps/LLMOps pipelines in Python and cloud environments.
Build and deploy ML models end-to-end for e-commerce pricing, forecasting, recommendations, and A/B testing to drive revenue and margin.
Builds and optimizes real-time AI teaching backends and prompt systems to improve conversational stability, safety, and educational outcomes using Java/Spring and LLM orchestration.
Build and deploy production-grade AI systems using RAG, agentic frameworks (LangGraph, AutoGen), and vector search (Azure AI Search, pgvector) with Python and cloud tools.
Secure AI systems by identifying and mitigating risks like prompt injection, jailbreaks, and data leakage in LLMs, RAG pipelines, and AI agents while enforcing responsible AI governance.
Build and deploy multi-step AI agent workflows using LangChain, LlamaIndex, and platforms like Azure AI Studio Agents or AWS Bedrock Agents, integrating APIs and databases for automation.
Build full-stack AI applications using .NET, C#, React/Next.js, and Node.js, integrating LLMs with RAG, vector search, and tool-calling for scalable business automations.
Build AI-ready data pipelines and domain products in Microsoft Fabric and Snowflake, focusing on semantic annotation, vector stores, and RAG foundations for enterprise reuse.
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