ROLE OVERVIEW

We are looking for a motivated and technically curious Junior AI/ML Engineer – Data Engineer to join our Retail Technology team. This is a high-impact, cross-functional role at the intersection of artificial intelligence, data engineering, and retail operations. The ideal candidate comes from a SaaS product background, has a solid foundation in ML and data pipelines, and is excited to apply cutting-edge AI (including LLM-based solutions such as Claude, GPT, Gemini, or similar) to real-world retail challenges — from store operations and maintenance to CEO-level escalation management and new store openings.

Job Title

Junior AI/ML Engineer – Data Engineer (Retail Intelligence)

Department

CEO's Office

Location

Hybrid (On-site + Remote)

Employment Type

Full-Time | Junior Level (1–3 years exp.)

Industry

Retail (Preferred: prior SaaS background)

Salary Band

Competitive – Based on experience

LLMs / GenAI

Data Engineering

ML Models

Retail Ops

AI Agents

SaaS Background

About the Role

This role bridges the gap between data infrastructure and intelligent AI-driven applications. You will work closely with retail operations, store maintenance, remote optimization (Optum), NSO (New Store Opening) teams, and senior leadership to build pipelines, AI agents, and dashboards that drive decisions at speed and scale. You will be hands-on with large language model (LLM) APIs — such as Claude, GPT-4, Gemini, or similar — to create intelligent agents and accuracy-enhanced workflows.

Key Responsibilities

1. LLM-Powered Dashboard Development

•      Design and build operational dashboards using LLM APIs (e.g., Claude, GPT-4, Gemini) and data visualization tools (e.g., Tableau, Power BI, Streamlit, or custom React/Python frontends).

•      Integrate LLM outputs into live dashboards to surface AI-driven insights for store ops, maintenance, and leadership teams.

•      Collaborate with business stakeholders to translate retail KPIs into automated dashboard metrics.

•      Build prompt pipelines and RAG (Retrieval Augmented Generation) workflows to power dashboard intelligence.

2. Model Accuracy & Improvement

•      Monitor and evaluate the performance of deployed ML/AI models against retail-specific metrics (accuracy, recall, F1, business KPIs).

•      Implement feedback loops, fine-tuning strategies, and prompt engineering improvements to iteratively improve model accuracy.

•      Conduct A/B testing and comparative evaluations of model versions.

•      Work with domain experts to curate retail-specific training data and validation sets.

3. AI Agent Development

•      Build and deploy autonomous AI agents using LLM frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen, or custom orchestration) with models such as Claude, GPT-4, or Gemini for:

–     CEO Escalation Handling: Triage, summarize, and route escalated issues to the correct teams with context and recommended actions.

–     Store Operations (Store Ops): Automate routine queries, anomaly alerts, and operational reporting for store managers.

–     Store Maintenance: Proactive issue detection, ticket creation, and maintenance scheduling agents.

–     Remote Optum: Agents that optimize staffing, remote task assignments, and operational efficiency recommendations.

–     NSO (New Store Opening): End-to-end checklists, document generation, vendor coordination, and readiness tracking agents.

•      Define agent architectures, tool integrations, memory strategies, and escalation rules.

•      Ensure agent outputs are reliable, auditable, and aligned with compliance/retail standards.

4. Data Engineering & Pipeline Development

•      Build and maintain robust ETL/ELT pipelines to ingest data from POS systems, ERP platforms, IoT sensors, ticketing tools, and SaaS platforms.

•      Work with cloud data warehouses (Snowflake, BigQuery, or Redshift) and orchestration tools (Airflow, dbt, Prefect).

•      Ensure data quality, lineage, and governance for all pipelines feeding AI/ML systems.

•      Design schemas and data models optimized for analytical and ML workloads in a retail context.

5. Retail Domain Applications

•      Develop AI use cases tailored to retail operational functions including store ops, maintenance workflows, NSO launch tracking, and leadership reporting.

•      Translate retail business problems into well-scoped ML/AI problem statements.

•      Support rollout of AI tools to non-technical retail staff with clear documentation and training materials.

Required Skills & Qualifications

Technical Skills

•      Python proficiency (pandas, NumPy, scikit-learn, FastAPI/Flask for APIs).

•      Experience with LLM APIs — such as Anthropic Claude, OpenAI GPT, Google Gemini, Mistral, or similar — and understanding of their capabilities and limitations.

•      Prompt engineering, RAG pipelines, and agent frameworks (LangChain / LangGraph / CrewAI or equivalent).

•      SQL and working knowledge of cloud data warehouses (Snowflake / BigQuery / Redshift).

•      Data pipeline tools: dbt, Airflow, Prefect, or equivalent.

•      Familiarity with vector databases (Pinecone, Weaviate, ChromaDB) for RAG architectures.

•      Dashboard/visualization experience: Streamlit, Tableau, Power BI, Metabase, or similar.

•      Version control with Git and experience in CI/CD pipelines.

•      Basic ML model lifecycle management (training, evaluation, deployment, monitoring).

Domain & Experience

•      1–3 years of professional experience in data engineering, ML engineering, or a related AI/software role.

•      Prior experience in a SaaS company is strongly preferred — you understand product thinking, iterative development, and API-driven architectures.

•      Exposure to or interest in retail operations, supply chain, or brick-and-mortar technology.

•      Ability to work cross-functionally with non-technical stakeholders (store managers, operations leads, C-suite).

Soft Skills

•      Strong problem-solving mindset with a bias for action.

•      Clear written and verbal communication — can translate AI concepts into plain language for retail teams.

•      Comfortable operating in ambiguous, fast-paced environments with evolving requirements.

•      Detail-oriented with a focus on data quality and responsible AI use.

Nice to Have

•      Experience with multi-modal AI (image/document understanding for store audits, maintenance photos, etc.).

•      Exposure to agentic frameworks and tool-use patterns (function calling, MCP, etc.).

•      Knowledge of retail-specific systems (SAP Retail, Oracle Retail, Manhattan Associates, etc.).

•      Experience with real-time data streaming (Kafka, Kinesis).

•      Prior work in building internal tooling or AI copilots for operations teams.

•      Familiarity with responsible AI, bias detection, and model governance frameworks.

What You Will Work On — Use Case Highlights

Domain

AI/Data Challenge

Your Role

CEO Escalations

High-volume escalations needing fast, intelligent triage and routing

Build escalation agent with LLMs — summarize, prioritize, and route with context

Store Operations

Real-time ops data across 100s of stores lacking unified visibility

Data pipelines + LLM-powered dashboards and anomaly detection agents

Store Maintenance

Reactive maintenance leading to store downtime and costs

Predictive maintenance agents, auto-ticketing, and scheduling workflows

Remote Optum

Suboptimal remote workforce task allocation and efficiency tracking

Optimization models + agent-driven task assignment recommendations

NSO (New Store Opening)

Complex, multi-team checklists and vendor coordination for new sites

NSO readiness agent — document generation, tracker, alert workflows

Why Join Us

•      Work at the cutting edge of AI applied to one of the world's largest industries — retail.

•      Hands-on with LLM APIs and agentic AI architectures (Claude, GPT, Gemini, and more) from day one.

•      Direct impact on operations serving thousands of stores and millions of customers.

•      Mentorship from experienced data and ML engineers; fast-track growth path.

•      Collaborative, cross-functional team that values curiosity, ownership, and speed.

•      SaaS-style product culture in a retail-tech environment — best of both worlds.

We are an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.