Engineering, Product & Data Science Future Opportunities

About the role

We are seeking a driven Engineer Product Data Scientist to own the design and execution of agentic AI workflows that power next-generation supply chain intelligence. You will define the end-to-end logic for how intelligent agents access, transform, and act upon multi-tier supplier data in real time. This role owns the architecture connecting risk signals to autonomous decision engines across the Resilinc platform. You will collaborate closely with product leaders to translate complex operational requirements into scalable data science and engineering solutions. The position demands rigorous experimentation, continuous validation, and measurable impact on prediction accuracy and response automation. You will own the guardrails that ensure agent behavior aligns with enterprise policies and client compliance standards. Ultimately, this role shapes the future of self-healing supply chains by turning agentic concepts into production-grade reliability.

Key facts

What you'll do

Architect and implement agentic AI pipelines that ingest, correlate, and contextualize global supplier data across multi-tier supply networks.

Design and maintain the data models, feature stores, and transformation layers that feed real-time risk assessment and impact analysis for supply chain events.

Develop and iterate on algorithms that enable autonomous decision support, surfacing prioritized actions and recommended mitigations to operations teams.

Collaborate with product managers to define roadmap milestones, success metrics, and experiment frameworks for new intelligent capabilities in the Resilinc platform.

Partner with engineering teams to integrate agentic workflows with existing visibility, alerting, and orchestration services used by enterprise customers.

Lead the validation and testing of agent behavior, ensuring outputs are accurate, explainable, and aligned with domain-specific compliance requirements.

Analyze large-scale operational data to identify patterns, anomalies, and optimization opportunities that improve prediction quality and system performance.

Translate complex supply chain constraints and business rules into logic structures that guide autonomous agent actions and exception handling.

Work with security and reliability engineers to implement monitoring, observability, and audit trails for all agent-driven processes in production.

Contribute to internal tools and dashboards that enable stakeholders to inspect agent decisions, review historical outcomes, and refine strategies.

Support the definition of technical specifications for new data sources, APIs, and integrations that expand the scope of agentic reasoning.

Champion best practices in data governance, model versioning, and reproducibility to sustain long-term scalability and trust in the system.

Mentor junior engineers and data specialists by providing code reviews, design guidance, and clear documentation of agentic workflows.

Act as a technical liaison between data science, product, and client success teams to ensure agentic solutions meet evolving customer needs.

Requirements

Candidates must have a strong background in computer science, data engineering, or a closely related quantitative field with proven experience building data-intensive applications.

You must demonstrate expertise in designing and implementing data pipelines, ETL frameworks, and scalable data architectures that support real-time analytics.

Experience with machine learning and statistical modeling is required, including training, validation, and deployment of models into production environments.

You should have hands-on experience with agentic frameworks, workflow orchestration systems, and patterns for autonomous decision-making in distributed systems.

Strong proficiency in at least one modern programming language such as Python, Java, or Scala, along with solid software engineering practices and version control using Git.

You must be comfortable working with large datasets, complex data schemas, and relational as well as NoSQL data stores in cloud-native environments.

Experience with cloud platforms, containerization, and infrastructure as code is essential for deploying and operating resilient, secure services.

You must have a track record of solving complex problems, communicating technical concepts to non-technical stakeholders, and delivering reliable software under tight deadlines.

Nice to have

Preferred experience in supply chain, manufacturing, logistics, or other complex operational domains where multi-tier data and risk modeling are critical.

Familiarity with industry standards, regulatory requirements, and compliance frameworks relevant to life sciences, aerospace, high tech, or automotive sectors.

Experience with visualization tools, storytelling with data, and enabling self-service analytics for enterprise stakeholders.

Background in cybersecurity, threat modeling, or resilience engineering for distributed, high-availability systems.

Practical notes

This role is based in Bangalore and requires availability during standard business hours as determined by global team alignment.

Travel may be required occasionally for client engagements, partner meetings, or internal initiatives as outlined by Resilinc policies.

Employment is contingent upon successful completion of background checks and verification of provided information.

Candidates must adhere to all applicable visa and work authorization requirements for the location of assignment.

The position may be filled as soon as suitable candidates are identified, so early application is encouraged.

Resilinc is an equal opportunity employer and welcomes diverse talent to join our mission-driven team.