Build the Future of Supply Chain Intelligence

What You’ll Do

• Own the architecture, design, and evolution of Pull Logic’s enterprise supply-chain data model and data platform architecture.
• Understand complex customer data originating from ERP, WMS, CRM, planning, manufacturing, logistics, and other enterprise systems and map it into Pull Logic’s canonical data model.
• Design and maintain conceptual, logical, and physical data models covering key supply-chain domains including: products and product hierarchies, customers, locations, suppliers and sourcing relationships, inventory and inventory movements, sales and demand history, sales orders and purchase orders, shipments, receipts, and transfers, forecasts and planning data, production and assembly data, lead times and supplier performance, Bills of Material (BOM), and supply-chain network relationships.
• Define and evolve a canonical supply-chain data model that enables Pull Logic applications, optimization engines, analytics, and AI agents to operate consistently across customers and industries.
• Establish standardized customer-to-Pull Logic source-to-target mappings, transformation specifications, semantic definitions, and reusable onboarding patterns.
• Architect and build scalable data ingestion and transformation pipelines for batch files, APIs, databases, object storage, and enterprise systems.
• Design and govern Bronze / Silver / Gold data architecture, ensuring clear separation between raw customer data, standardized canonical data, and analytics/AI-ready datasets.
• Define and implement data contracts, including schema definitions, grain, keys, required attributes, refresh frequency, source lineage, business rules, and data-quality expectations.
• Build reusable frameworks for: data ingestion, schema validation, data transformation, data enrichment, incremental processing, data reconciliation, error handling and replay, and data quality monitoring.
• Establish robust data quality frameworks covering schema validation, completeness, referential integrity, business-rule validation, statistical anomalies, data freshness, and reconciliation.
• Design scalable data structures optimized for large-scale supply-chain analytics, forecasting, simulation, optimization, and AI workloads.
• Define appropriate table structures, partitioning strategies, incremental-load approaches, indexing, and performance optimization techniques.
• Drive adoption of modern Lakehouse and open-table architectures, including Apache Iceberg and cloud object storage.
• Establish standards for metadata management, data lineage, schema evolution, auditability, versioning, and data governance.
• Collaborate closely with Data Science, AI Engineering, Platform Engineering, Product, Customer Engineering, and Customer Success teams to ensure data architecture supports both current product requirements and future platform capabilities.
• Partner with customer technical and business teams to understand their systems, source data structures, supply-chain processes, data semantics, and data-quality issues.
• Lead technical data-discovery sessions during enterprise customer onboarding and translate business terminology into scalable technical data models.
• Identify opportunities to reduce customer-specific engineering through reusable connectors, mappings, canonical transformations, and common data services.
• Establish data engineering and modeling best practices including code reviews, testing standards, documentation, naming conventions, observability, and release management.
• Mentor Data Engineers and contribute to building a strong data architecture and engineering competency within Pull Logic.
• Ensure data architecture and engineering practices align with enterprise security, governance, and SOC 2 expectations.

What We’re Looking For

• 10+ years of overall experience in Data Architecture, Data Modeling, Data Engineering, Data Platforms, or related disciplines.
• Minimum 5+ years of experience in senior Data Architecture / Data Modeling roles, preferably supporting large-scale enterprise platforms.
• Proven experience designing and implementing enterprise canonical data models and reusable data architectures.
• Strong experience understanding customer/source-system data and translating it into standardized business and analytical models.
• Demonstrated ability to work directly with complex enterprise datasets and independently understand their business meaning, relationships, grain, keys, quality issues, and transformation requirements.
• Strong knowledge of conceptual, logical, and physical data modeling.
• Deep understanding of: relational data modeling, dimensional modeling, canonical data modeling, master and reference data, Slowly Changing Dimensions, event and transactional data modeling, data contracts, and metadata and lineage.
• Strong hands-on expertise with: SQL, Python, data transformation and ETL/ELT frameworks, cloud object storage such as GCS or S3, large-scale relational and analytical databases, API and file-based data integrations, and batch and incremental data processing.
• Experience designing and building production-grade data pipelines with technologies such as Prefect, Airflow, Spark, Dask, dbt, or similar platforms.
• Experience with modern data platforms and lakehouse technologies such as Apache Iceberg, Delta Lake, BigQuery, Apache Doris, or similar analytical platforms.
• Strong understanding of data quality, observability, lineage, reconciliation, schema evolution, and pipeline reliability.
• Experience designing data architectures for high-volume and high-scale analytical workloads.
• Strong understanding of cloud data architecture, with a preference for experience on Google Cloud Platform (GCP).
• Ability to remain hands-on and build or prototype data pipelines, transformations, and models when required.
• Strong preference for candidates with hands-on experience working with Supply Chain, Manufacturing, Distribution, Retail, or Planning data.
• Experience understanding data from enterprise platforms such as SAP, Oracle, Microsoft Dynamics, Infor, Epicor, Salesforce, Blue Yonder, Kinaxis, o9, Manhattan, or similar systems is highly desirable.
• Experience working with manufacturing, distribution, or retail data models and translating ERP terminology into standardized analytical models is strongly preferred.
• Experience building data platforms that support Machine Learning, optimization, simulation, forecasting, or Agentic AI systems.
• Familiarity with Knowledge Graphs, semantic models, ontologies, or graph databases.
• Experience building multi-tenant SaaS data platforms.

Why Pull Logic?

• Be part of a mission-driven company redefining supply chain intelligence for the retail world.
• Own and shape the data architecture behind enterprise-scale supply-chain planning, optimization, analytics, and AI products.
• Work on cutting-edge technology with real-world impact and high visibility.
• Work at the intersection of Data Architecture, Supply Chain, Optimization, Machine Learning, and Agentic AI.
• Collaborate with researchers and engineers from Georgia Tech, one of the top engineering schools in the world.
• Grow with a dynamic, fast-paced team and play a key role in building the future of our platform.
• Performance-based ESOP opportunities for exceptional contributors who help drive Pull Logic’s success and growth.

We’re looking for a Lead AI Engineer to join our core technology team. This is a unique opportunity to shape the foundation of Pull Logic’s enterprise-grade Retail Tech products and work on solutions that have a direct impact on how modern retailers operate.

What You’ll Do

• Lead the architecture, design, and technical roadmap of Pull Logic’s AI initiatives
• Build a scalable conversational and agent-based AI platform using GCP, Gemini, LangChain, Google ADK, and related technologies
• Design multi-agent orchestration, safety guardrails, fallback strategies, and governance frameworks
• Convert prototypes into robust, secure, production-ready enterprise AI products
• Establish engineering best practices including code reviews, testing, release management, and observability
• Own AI reliability, latency, cost optimization, and performance
• Define evaluation frameworks for accuracy, safety, hallucination control, and continuous improvement
• Lead and mentor a growing AI engineering team
• Collaborate closely with Product Management, CTO, and Customer Engineering teams
• Ensure alignment with SOC2 and enterprise security expectations

What We’re Looking For

• 8–15 years of total engineering experience
• Minimum 4–6 years in applied AI, ML, or AI platform engineering
• Proven experience leading engineering teams or acting as a principal / technical lead
• Demonstrated experience building and deploying production-grade AI systems
• Strong hands-on expertise with LLMs, RAG, and conversational AI
• Experience with agent frameworks (LangChain or Google ADK preferred)
• Strong Python-based engineering experience
• Experience with cloud AI platforms (strong preference for GCP)
• Experience with vector databases and knowledge retrieval systems
• Familiarity with LLM optimization techniques such as prompt engineering at scale, fine-tuning, adapters, and distillation
• Experience with AI system observability, including hallucination detection, drift monitoring, and quality metrics
• Experience with Vertex AI Model Monitoring and Vertex AI Evaluation
• Strong understanding of cost, latency, and performance optimization for LLM-based systems
• Understanding of engineering rigor, stability, and scalability
• Experience in supply chain, manufacturing, retail, or analytics products (preferred)
• Experience building AI platforms in SaaS environments (good to have)
• Familiarity with enterprise governance, compliance, and SOC2 expectations (good to have)
• Experience working in startups or fast-moving innovation teams (good to have)

Why Pull Logic?

• Be part of a mission-driven company redefining supply chain intelligence for the retail world
• Work on cutting-edge technology with real-world impact and high visibility
• Collaborate with researchers and engineers from Georgia Tech
• Grow with a dynamic, fast-paced team and play a key role in building the future of the platform
• Performance-based ESOP opportunities for exceptional contributors

Ready to Build What’s Next?

Send your resume to careers@pulllogic.com and let’s start the conversation.

Key Responsibilities

• Design and develop deployment network design and optimization models, including multi-echelon inventory positioning and flow optimization
• Formulate and solve large-scale optimization problems involving service levels, costs, lead times, capacity, and risk
• Apply operations research techniques such as linear, mixed-integer, stochastic, and robust optimization
• Incorporate uncertainty from demand, supply, and lead times into network planning models
• Collaborate with data science and engineering teams to integrate optimization models with forecasting and AI components
• Translate research models into scalable, production-grade optimization engines
• Contribute to internal research, IP development, and technical documentation

Required Qualifications

• PhD in Operations Research, Industrial Engineering, or a related quantitative field
• Deep expertise in network design, supply chain optimization, and deployment planning
• Strong background in optimization modeling and algorithms
• Proficiency in Python and optimization frameworks (e.g., Pyomo, Gurobi, CPLEX, or similar)
• Experience working with complex, real-world supply chain data and constraints
• Ability to convert theoretical models into practical, business-ready solutions
• Experience with multi-echelon inventory optimization, service-level optimization, or distribution network design
• Knowledge of stochastic or robust optimization in supply chain contexts
• Prior industry experience applying OR models at scale

Why Join Pull Logic

• Work on core optimization problems that directly shape enterprise supply chain decisions
• Influence the architecture of an AI-native, agentic optimization platform
• Collaborate with leading researchers, engineers, and industry practitioners
• Build proprietary algorithms and IP with real commercial impact

Please reach out to careers@pulllogic.com with your resume.

Pull Logic Inc. is an Equal Opportunity Employer. No 3rd Party Agency Candidates.

We’re a fast-growing startup looking for a hands-on Team Lead (Full-Stack) to lead a small squad and ship secure, scalable features end-to-end building modern Vue.js front-ends on robust Frappe back- ends.You’llsetqualitybars,guidearchitecture,mentorengineers,andstillcontributemeaningfulcode every week.

WhatYou’llDo(Delivery+Leadership)

Build&Ship(Hands-on)

• Build end-to-end features: DocTypes, controllers, background jobs (RQ), real-time endpoints, and responsive Vue 3 (Composition API / Pinia) interfaces.
• Standardize SaaS base apps: design reusable, versioned Frappe custom apps across customers and multiple sites with clean tenancy boundaries, per-site configuration, feature flags, and safe migrations.
• Design clean domain models: DocType schemas, RBAC permissions, workflows, and validations for multi-tenant retail use cases.
• Author reliable Python services: async tasks, schedulers, caching (Redis), S3/Parquet flows, and large-data pagination/streaming.
• Make it fast & safe: parameterized SQL, index tuning (MariaDB/RDS), rate limits, CSRF/CORS, and tenant isolation.
• Quality first: pytest / frappe.tests, API contract tests, Playwright/Cypress for UI; ship via Azure DevOps pipelines.
• Own deployability: Docker images, environment configs, secrets; partner with DevOps on EKS / ACR / ECR.
• Analytics UX: build tables, charts, and filters wired to efficient Python data services.

Lead the Team

• Lead a squad of 3–6 engineers: planning, sprint execution, delivery tracking, and predictable outcomes.
• Translate product needs into well-scoped technical work: break down epics, write tickets with acceptance criteria, manage dependencies.
• Drive technical decisions: lightweight RFCs, trade-off analysis, and incremental architecture improvements.
• Set quality bars: code review standards, test strategy, performance and security checks, definition of done.
• Mentor and grow engineers through coaching, pairing, feedback, and skill development.
• Partner cross-functionally with Product, Design, QA, and DevOps for releases, incidents, and operational readiness.

What We’re Looking For

Core Requirements

• Experience: 7+ years overall engineering experience, with 2+ years leading engineers.
• Python: 6+ years hands-on production experience.
• Frappe / ERPNext: 3+ years building custom apps (DocTypes, hooks, RQ, migrations, multi-site).
• Vue.js: 2+ years (Vue 3 preferred – Composition API, Pinia, Vite).
• DataFrames & Numerics (must-have):
  • NumPy, pandas, Polars
  • Vectorization, groupby/joins, window ops, time-series resampling
  • Polars lazy/expression API, scan_parquet, predicate pushdown
  • Arrow/Parquet, chunked processing, large datasets
• API Design: Versioned REST APIs, pagination, filtering, idempotency, auth (JWT/session), rate limiting.
• Databases: Strong SQL, indexing, query plans, transactions; MariaDB/RDS and Redis.
• Security: CSRF/XSS/SQLi defenses, secrets handling, audit logging, SOC 2 familiarity.
• Dev Workflow: Git, code reviews, linting, tests, Azure DevOps CI/CD, Docker, Linux, shell scripting.

Nice to Have

• Backlog grooming, estimation, sprint ceremonies, and release planning.
• ERPNext domain objects, report builder, PDF/print at scale.
• Charts and dashboards (ECharts, Plotly), table virtualization, accessibility.
• Integrations: Infor SyteLine, Salesforce, S3, CloudFront.
• Observability: Sentry, OpenTelemetry, Prometheus, Grafana, centralized logging.
• Basic MLOps integration (model consumption, feature flags, A/B testing).

Why PullLogic?

• Mission-driven company redefining supply chain intelligence for retail.
• Work on cutting-edge technology with real-world impact.
• Collaborate with Georgia Tech researchers and strong engineers.
• High ownership, low bureaucracy, strong engineering culture.

Ready to Build What’s Next?

Send your resume to careers@pulllogic.com and let’s start the conversation.