Lead AI Systems Architect, Data-Centric AI, Multimodal

AWISEE

. Design and build multimodal AI pipelines combining computer vision, speech-to-text (Whisper), and LLMs into automated ingestion workflows .

Posted 10/9/2026full-timeRemote • SwedenSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building multimodal AI pipelines that integrate computer vision, speech-to-text, and language models, with a strong focus on programmatic extraction frameworks and data-centric AI methodologies. Proven ability to guide junior developers and establish best practices in engineering for data enrichment.

Highest-signal resume keywords

Applied AINatural Language ProcessingComputer VisionPythonPyTorch/TensorFlow

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Multimodal Pipeline DevelopmentProgrammatic Extraction FrameworksFine-Tuning Open-Source ModelsData-Centric AIActive Learning

Soft Skills

MentoringCollaboration

Tools & Technologies

WhisperDSPyInstructorPydanticVLLM

Industry Keywords

Social Media Data PipelinesRAG ArchitecturesVector DatabasesHigh-Throughput Inference EnginesSynthetic Data Generation

Tech Stack

Tools & technologies

PythonPyTorchTensorflow

About the role

Key responsibilities & impact

Design and build multimodal AI pipelines combining computer vision, speech-to-text (Whisper), and LLMs into automated ingestion workflows
Implement programmatic extraction frameworks (Instructor, Pydantic, DSPy, Outlines) to enforce strict, validated JSON outputs from generative models
Transition workloads from commercial APIs to fine-tuned open-source models (Llama 3/4, Qwen, DeepSeek) running on local inference engines (vLLM, TGI, Groq)
Deploy data-centric AI frameworks (Cleanlab, Snorkel, Active Learning) to automate millions of labels and clean noisy datasets
Guide junior developers, establish engineering best practices, and set architectural roadmaps for data enrichment
Convert raw, unstructured social content (short-form video, audio, captions, and visual metadata) into high-precision structured data products

Requirements

What you’ll need

5+ years of experience in Applied AI, Natural Language Processing, or Computer Vision
Proven experience building multimodal pipelines integrating vision, speech, and text models
Mastery of Python, PyTorch/TensorFlow, and production LLM orchestration tools (DSPy, Instructor, Pydantic, LangChain/LlamaIndex)
Hands-on experience fine-tuning and serving open-source models using vLLM, TGI, or similar high-throughput inference engines
Experience with vector databases (Qdrant, pgvector, Pinecone) and RAG architectures at scale
Preferred: Prior experience with social media data pipelines (TikTok, Instagram Reels, YouTube Shorts)
Preferred: Background in data-centric AI, active learning, or synthetic data generation

Benefits

Comp & perks

Remote work arrangement