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Metro location and mid-level role balanced by specialized LLM/GenAI skillset, producing medium competition.
Core ML/GenAI engineering skills transfer across industries, though healthcare domain familiarity moderately matters.
Many mandatory specialized GenAI, deployment, and MLOps skills create stringent technical filters for candidates.
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Build and deploy scalable Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and agent-based AI systems, focusing on production-ready inference and deployment pipelines.
Optimize AI models for efficient, cost-effective production including tasks like quantization, pruning, and distillation.
Collaborate with data science, research, and product teams; mentor junior engineers; and ensure software engineering best practices including clean code, CI/CD, and ethical AI development.
Bachelor's degree in Information Technology, Computer Science, or related field.
Experience working on scalable LLM, RAG, and agent-based AI systems, including LLM inference and deployment pipelines.
Proficiency with AI model optimization techniques (e.g., quantization GPTQ/AWQ), multimodal AI frameworks (CLIP, BLIP, Whisper, LLaVA), and LLM serving tools like FastAPI and vector databases (FAISS, Pinecone, Chroma).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building production-grade AI systems that balance innovation with reliability, including rapid prototyping of GenAI and LLM applications.
Skilled in cross-functional collaboration with data science and product teams to deliver impactful AI solutions under tight deadlines.
Demonstrated ownership and accountability focused on engineering excellence, reproducibility, responsible AI practices, and mentoring junior team members.