Back
Mid-level ML title, 5–6 years, broad LLM and deployment skillset increase competition.
ML infrastructure and LLM specialization are transferable across industries but require domain expertise.
Explicit 5–6 years and mandatory ML/LLM, PyTorch, deployment, and vector DB skills make filters strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and maintain scalable AI/ML systems and pipelines using Python and relevant AI frameworks.
Build, deploy, and optimize LLM-based systems, RAG pipelines, AI-driven APIs, and backend services integrating vector databases and semantic search.
Collaborate with cross-functional teams and clients, lead technical architecture discussions, mentor juniors, and prepare technical documentation and client communications.
5–6 years of direct experience in Python-based AI/ML development and deployment.
Strong expertise with AI/ML tools: PyTorch or TensorFlow, Scikit-learn, Pandas, FastAPI/Flask/Django.
Hands-on experience with LLMs (OpenAI, Claude, Gemini, Llama etc.), RAG architectures, vector databases (Pinecone, Weaviate, FAISS, etc.), and cloud platforms (AWS/GCP/Azure).
Excellent English communication skills with proven client interaction and presentation experience.
Experienced in building production-scale AI systems, including model optimization, deployment, and monitoring within microservices architecture.
Comfortable leading or mentoring AI/ML teams and participating in solution architecture and technical planning.
Skilled at bridging technical and non-technical stakeholders, able to communicate complex AI concepts clearly, and comfortable working in collaborative fast-paced environments.