AI/ML Engineer

About the role

AI/ML 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 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 - 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...

Key responsibilities

• check_circleCollaborate with the team on day-to-day project tasks
• check_circleLearn tools and processes used by the organization
• check_circleDocument work and participate in team meetings
• check_circleSupport quality checks and continuous improvement

Requirements

• check_circleLLM-Powered Dashboard Development
• check_circleDesign 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).
• check_circleIntegrate LLM outputs into live dashboards to surface AI-driven insights for store ops, maintenance, and leadership teams.
• check_circleCollaborate with business stakeholders to translate retail KPIs into automated dashboard metrics.
• check_circleBuild prompt pipelines and RAG (Retrieval Augmented Generation) workflows to power dashboard intelligence.
• check_circleMonitor and evaluate the performance of deployed ML/AI models against retail-specific metrics (accuracy, recall, F1, business KPIs).
• check_circleImplement feedback loops, fine-tuning strategies, and prompt engineering improvements to iteratively improve model accuracy.
• check_circleConduct A/B testing and comparative evaluations of model versions.
• check_circleWork with domain experts to curate retail-specific training data and validation sets.
• check_circleBuild 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:
• check_circleDefine agent architectures, tool integrations, memory strategies, and escalation rules.
• check_circleEnsure agent outputs are reliable, auditable, and aligned with compliance/retail standards.

Skills & keywords

Benefits & perks

• check_circleMentorship
• check_circleCertificate of completion
• check_circleFlexible work arrangement where applicable