Back
Mid-level ML/AI role, metro location, and recognizable global brand create high candidate competition.
Core ML/LLM engineering skills are transferable, though legal-domain experience moderately impacts fit.
Explicit years plus mandatory production ML/GenAI, LLM and vector DB skills increase shortlisting strictness to high.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, train, and optimize custom ML/DL models (Transformers, CNNs, RNNs) to solve complex predictive problems.
Build, orchestrate, and deploy production-level autonomous AI Agents and multi-agent architectures using Python.
Architect and manage advanced Retrieval-Augmented Generation (RAG) pipelines and production-grade scalable software environments, aligning models with cloud infrastructure.
3 to 4 years of professional experience as Data Scientist, ML Engineer, or AI Developer in production software environments.
Proficiency in Python and libraries such as scikit-learn, PyTorch, or TensorFlow with production-grade coding standards including unit testing and CI/CD.
Hands-on experience with generative AI including LLMs, vector databases (Pinecone, Qdrant, Milvus, Chroma), and prompt engineering.
Skilled in data engineering tasks including SQL, data cleaning, pipeline structuring with pandas or NumPy; AWS experience is a plus but not mandatory.
Experienced in end-to-end ML/DL system development with ability to transition models from research to scalable production.
Strong expertise in current generative AI technologies and frameworks, capable of building autonomous AI agents and advanced multi-stage pipelines.
Comfortable working in cloud-aligned environments with focus on robust, efficient, and maintainable Python code and software tooling.