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Remote mid-level AI role with broad ML skillset yields moderate competition.
Specialized LLM/VLM skills transfer across industries but require ML-specific experience, so medium sensitivity.
Requires deep LLM/VLM expertise, PyTorch, RAG, vector DBs and production deployment, so high filtering.
Design, develop, and deploy production-grade AI solutions using Large Language Models (LLMs) and Vision-Language Models (VLMs).
Build and optimize RAG pipelines involving document ingestion, embeddings, retrieval, reranking, and generation across multimodal data (text, images, PDFs, charts, tables).
Develop APIs and production services focusing on inference optimization for latency, throughput, memory, and cost, and implement evaluation frameworks for accuracy, hallucination, relevance, latency, and safety.
Strong hands-on experience with LLM and/or VLM architectures and related frameworks (Transformers, PyTorch, Hugging Face).
Proficiency in Python programming, software engineering fundamentals, and API development using tools like FastAPI, Docker, and cloud platforms (AWS/Azure/GCP).
Experience with building RAG systems and vector-search solutions using vector databases such as FAISS, Milvus, Pinecone, Weaviate, or pgvector.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in multimodal AI, particularly integrating vision and language models (e.g., Qwen-VL, LLaVA, Gemini, GPT vision models).
Skilled in prompt engineering, supervised fine-tuning (LoRA/QLoRA), model evaluation, and optimization of inference metrics (latency, throughput, cost).
Proficient in building scalable AI pipelines combining vision, language, and retrieval components and deploying them in production environments collaboratively with cross-functional teams.