Design and implement LLM‑powered applications: chatbots, virtual shopping assistants, internal copilots, summarization and insight engines.
Design and implement LLM‑powered applications: chatbots, virtual shopping assistants, internal copilots, summarization and insight engines.
Design and implement LLM‑powered applications: chatbots, virtual shopping assistants, internal copilots, summarization and insight engines.
Design and implement LLM‑powered applications: chatbots, virtual shopping assistants, internal copilots, summarization and insight engines.
Design and implement LLM‑powered applications: chatbots, virtual shopping assistants, internal copilots, summarization and insight engines.
Build and optimize RAG pipelines: document ingestion, chunking, embeddings, vector storage, retrieval strategies, and answer synthesis.
Build and optimize RAG pipelines: document ingestion, chunking, embeddings, vector storage, retrieval strategies, and answer synthesis.
Build and optimize RAG pipelines: document ingestion, chunking, embeddings, vector storage, retrieval strategies, and answer synthesis.
Build and optimize RAG pipelines: document ingestion, chunking, embeddings, vector storage, retrieval strategies, and answer synthesis.
Build and optimize RAG pipelines: document ingestion, chunking, embeddings, vector storage, retrieval strategies, and answer synthesis.
Integrate AI capabilities into existing platforms and microservices, exposing them via secure, scalable APIs.
Integrate AI capabilities into existing platforms and microservices, exposing them via secure, scalable APIs.
Integrate AI capabilities into existing platforms and microservices, exposing them via secure, scalable APIs.
Integrate AI capabilities into existing platforms and microservices, exposing them via secure, scalable APIs.
Integrate AI capabilities into existing platforms and microservices, exposing them via secure, scalable APIs.
Fine-tune or adapt models (instruction tuning, prompt tuning, adapters, or retrieval strategies) for specific customer domains and use cases.
Fine-tune or adapt models (instruction tuning, prompt tuning, adapters, or retrieval strategies) for specific customer domains and use cases.
Fine-tune or adapt models (instruction tuning, prompt tuning, adapters, or retrieval strategies) for specific customer domains and use cases.
Fine-tune or adapt models (instruction tuning, prompt tuning, adapters, or retrieval strategies) for specific customer domains and use cases.
Fine-tune or adapt models (instruction tuning, prompt tuning, adapters, or retrieval strategies) for specific customer domains and use cases.
Implement observability and evaluation for AI systems: logging, tracing, quality metrics, evaluation datasets, feedback capture, and guardrails.
Implement observability and evaluation for AI systems: logging, tracing, quality metrics, evaluation datasets, feedback capture, and guardrails.
Implement observability and evaluation for AI systems: logging, tracing, quality metrics, evaluation datasets, feedback capture, and guardrails.
Implement observability and evaluation for AI systems: logging, tracing, quality metrics, evaluation datasets, feedback capture, and guardrails.
Implement observability and evaluation for AI systems: logging, tracing, quality metrics, evaluation datasets, feedback capture, and guardrails.
Collaborate with Product Managers and Designers to refine requirements, craft prompts, and shape user interactions with AI.
Collaborate with Product Managers and Designers to refine requirements, craft prompts, and shape user interactions with AI.
Collaborate with Product Managers and Designers to refine requirements, craft prompts, and shape user interactions with AI.
Collaborate with Product Managers and Designers to refine requirements, craft prompts, and shape user interactions with AI.
Collaborate with Product Managers and Designers to refine requirements, craft prompts, and shape user interactions with AI.
Optimize latency, throughput, and cost for LLM‑backed services; experiment with model sizes, caching, batching, and routing strategies.
Optimize latency, throughput, and cost for LLM‑backed services; experiment with model sizes, caching, batching, and routing strategies.
Optimize latency, throughput, and cost for LLM‑backed services; experiment with model sizes, caching, batching, and routing strategies.
Optimize latency, throughput, and cost for LLM‑backed services; experiment with model sizes, caching, batching, and routing strategies.
Optimize latency, throughput, and cost for LLM‑backed services; experiment with model sizes, caching, batching, and routing strategies.
Work closely with Data Engineering and MLOps teams to operationalize models, automate training/inference workflows, and manage environments.
Work closely with Data Engineering and MLOps teams to operationalize models, automate training/inference workflows, and manage environments.
Work closely with Data Engineering and MLOps teams to operationalize models, automate training/inference workflows, and manage environments.
Work closely with Data Engineering and MLOps teams to operationalize models, automate training/inference workflows, and manage environments.
Work closely with Data Engineering and MLOps teams to operationalize models, automate training/inference workflows, and manage environments.
Stay current with developments in generative AI, open‑source models, vector databases, and LLMOps tools, bringing best practices into Mindstix projects.
Stay current with developments in generative AI, open‑source models, vector databases, and LLMOps tools, bringing best practices into Mindstix projects.
Stay current with developments in generative AI, open‑source models, vector databases, and LLMOps tools, bringing best practices into Mindstix projects.
Stay current with developments in generative AI, open‑source models, vector databases, and LLMOps tools, bringing best practices into Mindstix projects.
Stay current with developments in generative AI, open‑source models, vector databases, and LLMOps tools, bringing best practices into Mindstix projects.
