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Mid-level ML role, metro location, and broad generative-AI skillset increase applicant competition.
Technical ML skills are transferable across industries, though domain-specific generative-AI experience matters.
Explicit 3–5 years plus many mandatory generative-AI, tooling, and deployment skills make filtering strict.
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Design, develop, and deploy scalable AI applications using Generative AI, LLMs, VLMs, RAG, and AI agent frameworks.
Build intelligent chatbots, AI assistants, workflow automation solutions, and production-grade AI services using FastAPI/Django.
Develop and optimize AI/ML models for NLP, computer vision, and predictive analytics; implement vector databases and semantic search solutions.
3-5 years of experience in AI/ML Engineering with hands-on Generative AI and production AI applications.
Strong programming skills in Python and SQL; experience with FastAPI/Django, REST APIs, and Docker.
Experience with LLMs, LangChain, LlamaIndex, RAG architectures, and vector databases like Qdrant, ChromaDB, Pinecone, Weaviate, or PGVector.
Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or related field.
Experienced in building and deploying AI-driven products in production environments using modern AI frameworks and cloud infrastructure.
Comfortable working across NLP, computer vision, and predictive analytics domains with expertise in Generative AI and AI agent frameworks.
Proficient in data pipeline development, AI model fine-tuning, evaluation, and deployment using containerization (Docker) and REST API services.