Senior AI Engineer

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).

Tools/stack (typical) Python, pandas, numpy, SQL, scikit-learn, Jupyter, Git, Docker, FastAPI (optional), LangChain/LlamaIndex (optional), Ollama, Llama models, vector DB (FAISS/pgvector/Weaviate), cloud data warehouse (Snowflake or equivalent).