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Project44

AI-powered platform for real-time supply chains

Staff Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • At least 8 years of experience in data science or applied machine learning and a strong track record building and deploying production-grade machine learning systems.
  • Deep expertise in tree-based models, transformers, probabilistic modeling, and feature engineering, with a data-centric mindset.
  • Proficiency in SQL and Python, with hands-on experience in Snowflake or Databricks, Spark, Airflow, Kafka, and MLOps tooling.
  • Experience building retrieval-augmented generation systems, working with embeddings and vector databases, and developing large language model applications and agentic workflows; understanding of evaluation, guardrails, and safe deployment of generative AI systems.
  • Familiarity with distributed systems, application programming interfaces, and deployment patterns, and ability to write production-quality code.
  • Ability to evaluate model performance, detect edge cases, conduct root cause analysis, and design monitoring and evaluation frameworks.
  • Experience handling real-world data quality issues, including drift, bias, missingness, and anomalies, and using tools and techniques to detect and address them.
  • Ability to choose between machine learning and heuristics and design hybrid solutions; understanding of architectural trade-offs across models and systems.
  • Understanding of how machine learning metrics translate to business outcomes and ability to balance accuracy, scalability, latency, and customer experience.
  • Ability to frame problems, influence stakeholders, communicate model behavior and decisions to technical and non-technical audiences, and drive measurable business impact.

Responsibilities

  • Lead end-to-end development of models for ETA, risk, anomaly detection, and fraud using techniques such as embeddings, transformers, and hybrid models.
  • Build reusable machine learning infrastructure for features, experimentation, deployment, and monitoring to scale model development and reduce time to production.
  • Build large language model-powered solutions, including retrieval-augmented generation, diagnostics, automation, and coding agents, and establish guardrails, evaluation, and explainability.
  • Translate customer workflows into well-defined machine learning problems and ensure measurable business impact.
  • Establish offline and online evaluation frameworks tied to business outcomes.
  • Partner with machine learning engineering and data engineering teams to build scalable, reliable systems across the machine learning lifecycle.
  • Deliver two high-impact machine learning capabilities with measurable customer value within 6–12 months.
  • Establish standardized experimentation, monitoring, and root cause analysis workflows.
  • Introduce generative AI and agentic capabilities to improve productivity and insight generation.
  • Reduce time to production and increase reuse across data science teams.
  • Drive measurable improvements in model performance and system reliability.

Desired Qualifications

  • Experience in logistics, including ocean or truckload, supply chain, or high-volume operational systems.
  • Experience with geospatial data, routing, and tracking systems.
  • Experience with anomaly detection, fraud modeling, or ETA prediction.
  • Experience building internal platforms or tools for data science teams.

About the Company

Project44

AI-powered platform for real-time supply chains

Project44 provides an AI-powered platform that gives real-time visibility into the movement of goods across the supply chain. It connects shipment data from carriers, ports, and logistics providers to detect risks, forecast delays, and suggest corrective actions, all shown on a global real-time dashboard. What sets it apart is the combination of continuous tracking with AI-driven risk detection and prescriptive insights across containers, ports, and vessels, helping customers reduce detention and demurrage fees, shorten lead times, and communicate disruptions proactively. The goal is to help customers optimize their supply chains, cut costs, and improve customer satisfaction by making logistics faster, more predictable, and more transparent.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$892.5M
Headquarters
Chicago, IL
Founded
2014

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