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AI Applied Engineer

Voosh

Bengaluru

2-4 years

Today

$21.7K–34.9K/yr

Full-time

Onsite

Skills Required

LLM

RAG

OpenAI

LangChain

LlamaIndex

Embeddings

NL2SQL

Python

SQL

Machine Learning

Data Science

Analytics Engineering causal inference budget optimization segmentation

Description

Voosh is building an AI-first intelligence layer on top of its data and workflows for multi-location US restaurant brands. This role owns high-impact applied ML and LLM work that directly affects revenue, margin, and retention.

Company: Voosh

Role: AI Applied Engineer

Location: Bengaluru, Karnataka, India

Experience

- 2–4 years experience in Applied ML, Data Science, or Analytics Engineering
- Exceptional fresh grads with strong projects may be considered
- Strong in Python, SQL, and statistics
- Comfortable working with messy real-world data
- Hands-on experience with causal inference, budget optimization, segmentation or clustering, forecasting, or experimentation
- Hands-on with LLMs: prompting, tool calling, RAG or NL2SQL, and evaluations
- Bias for shipping: build, test, deploy, and iterate

Responsibilities

- Build response and uplift models using historical sales, orders, and spend data
- Build budget optimization models to allocate spend by location, channel, and time windows
- Create measurement frameworks such as test-control, DiD, and causal inference to estimate marketing impact
- Develop customer segmentation models to guide spend allocation and goals by segment
- Build AI copilots including chatbots, NL2SQL pipelines, and analytics agents
- Ship product AI upgrades that improve automation, accuracy, and decision quality across the platform
- Own the AI roadmap and build the core AI engine for the business
- Work directly with the founder and core client teams

Additional Responsibilities





- Deliver a baseline response model and a simple budget allocator that can run across multiple brands within the first 90 days
- Create a repeatable impact measurement template the team can use weekly
- Ship at least one AI workflow that meaningfully reduces analyst time
- Influence weekly spend decisions through model outputs
- Build, test, deploy, and iterate on solutions

Nice To Have

- AutoML tools to train, compare, and deploy models faster at scale
- Cloud basics for storage, compute, and deployment
- Containers

More Skills statistics, Applied ML, clustering, forecasting, experimentation, prompting, tool calling, evals, sklearn, statsmodels, forecasting libraries, causal libraries, feature pipelines, MLOps, model registry, catalog, experiment tracking, CI/CD, monitoring, drift alerts, open-source models, function calling, structured outputs, AWS, GCP, Azure, containers

Other

- On-site
- Full-time
- The role is described as high ownership early
- Work has direct impact on revenue, margin, and customer retention
- Rapid feedback loop from models to weekly spend decisions
- High visibility with founder and core client teams
- Clear upside measured in dollars and retention

Prepare for this role Recommended resources to build the skills for this position. Sponsored.

Generative AI with Large Language Models

Coursera

Comprehensive LLM course covering transformer architecture, fine-tuning, RLHF, and deployment.

Python 3 Programming Specialization

Coursera

Intermediate Python covering classes, inheritance, APIs, and data processing.

Python for Everybody Specialization

Coursera

Learn Python from scratch — variables, data structures, web scraping, and databases.

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