AIML Engineer

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

CEOs Office

Location

Hybrid (On-site Remote)

Employment Type

Full-Time Junior Level (13 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

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.

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.

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

13 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.

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.

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

Work at the cutting edge of AI applied to one of the worlds 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.