Build and optimize ML, deep learning, and GenAI models using modern frameworks (PyTorch, TensorFlow, scikit-learn)
Design and deliver RAG pipelines, hybrid retrieval systems, and vector-based search workflows
Develop Agentic AI solutions including tool orchestration, reasoning flows, and safe-execution strategies
Run structured experimentation using evaluation metrics (BERTScore, BLEURT, semantic similarity, retrieval precision/recall)
Integrate solutions into GCP (Vertex AI, Workbench, Vector Search) and AWS (SageMaker, Bedrock)
Build ingestion, enrichment, and semantic retrieval flows for high-quality knowledge and feature engineering
Partner with onshore and offshore DS/ML engineers to ensure quality, consistency, and shared technical patterns

3–5+ years in Data Science, ML Engineering, Applied AI, or GenAI roles
Strong Python and SQL skills; experience with deep learning and ML libraries
Hands-on experience building RAG systems, vector search pipelines, and GenAI applications
Familiarity with modern LLM platforms (Vertex AI, OpenAI, Bedrock, Azure OpenAI)
Experience implementing evaluation methodologies for both ML and GenAI
Ability to translate ambiguous business problems into well-structured ML/GenAI solutions
Experience designing multi-step agent workflows and reasoning pipelines
Strong grounding in statistics, experiment design, and feature engineering
Ability to present complex concepts clearly to technical and business stakeholders
Experience partnering with SMEs to validate and refine AI outputs

Applied AI Data Scientist

Key skills