Senior Data Science & AI Engineer

About the job

Job description: We need a hands-on senior Datascience & AI engineer who can build deep analytics pipelines in Python and implement a GenAI Q&A layer over enterprise data. The work is highly technical: data wrangling, metric computation, anomaly detection/forecasting (light ML), retrieval-augmented generation (RAG), and local LLM inference using Llama + Ollama. Responsibilities (technical) • Build robust analytics code in Python using pandas/numpy to compute, validate, and reconcile KPIs (costing, margins, QBR metrics, operational metrics). • Write efficient transformations (vectorization, memory optimization), and implement repeatable pipelines with tests and data validation. • Develop SQL to extract/shape datasets from enterprise sources and/or a cloud data warehouse; optimize queries as needed. • Implement a governed GenAI “ask the data” prototype: • Use Llama-family models via Ollama (or llama.cpp/vLLM as needed) • Build RAG over structured + semi-structured data (chunking, embeddings, retrieval, reranking) • Produce structured outputs (tables/JSON) and drill-down-ready answers • Add basic guardrails: grounded responses, citations/traceback to data, and safe handling of sensitive fields. • Apply light-to-moderate ML where useful: • anomaly detection (cost variances, outliers, feed failures) • simple forecasting / trend analysis for key metrics • model evaluation and error analysis • Create reproducible experimentation and evaluation: • test question sets for the LLM • accuracy/groundedness checks • latency profiling and performance tuning • Package deliverables for deployment (Docker, config management), and produce technical documentation/runbooks. Required skills & experience • 7+ years hands-on in data science / analytics engineering / ML engineering (individual contributor). • Expert in Python, especially: • pandas, numpy • data cleaning, joins/merges, windowed calculations, time-series handling • performance optimization (vectorization, profiling, memory management) • Strong SQL (complex joins, aggregates, window functions; tuning mindset). • Solid fundamentals in statistics and ML: • feature engineering basics, evaluation metrics, overfitting awareness • scikit-learn (or equivalent) for quick modeling • GenAI implementation experience: • Llama models (or comparable open LLMs) • Ollama for local inference (or similar) • RAG frameworks (LangChain/LlamaIndex) or custom retrieval pipelines • embeddings + vector stores (FAISS/pgvector/Weaviate/Pinecone) • Good engineering habits: • unit tests, data tests, logging, error handling • Git, CI basics • Docker and environment management. Nice-to-have • Snowflake experience (or similar modern cloud data platform). • dbt experience (modeling, tests, docs). • Experience with enterprise “semantic layers” or metric definitions at scale. • Experience building lightweight APIs (FastAPI) for analytics/LLM endpoints. • Familiarity with security constraints (RBAC concepts, masking, audit logs).