Forward Deployed Supply Chain Engineer (India)

• Own customer implementations from discovery through go-live — leading blueprinting sessions, technical design workshops, and build-in-public working sessions with customer stakeholders, turning abstract supply chain problems into concrete logic and model structures

• Translate messy, real-world supply chain constraints — network trade-offs, demand volatility, inventory risk, fulfillment complexity — into composable, AI-native solutions using Lyric's full platform stack: Optimization Notes, Prediction Notes, Green Notes, and Next Gen App UIs

• Get hands-on daily — writing SQL and Python, configuring business logic, and rapidly prototyping from customer pain point to working model; troubleshoot edge cases, validate outputs, and own the technical integrity of everything you deploy

• Partner with Product and Engineering when the platform needs to go further — you're the person in the field who knows exactly where the edges are and why they matter

• Mentor junior engineers, turn recurring field challenges into reusable design patterns and accelerators, and close out every project with a documented customer story — quantified outcomes and a short demo that captures what was built and the value it unlocked

• Act as a critical feedback loop for Product and Engineering — what you encounter in the field directly shapes what Lyric builds next

• Apply AI tools actively across every engagement — using LLM-assisted coding, automated documentation, and ML-powered workflows to accelerate delivery and move customers from reactive, siloed planning to autonomous decision-making

• Continuously experiment with how new AI/ML capabilities — probabilistic forecasting, anomaly detection, AI-assisted planning — can be embedded into customer operations, raising the bar on what a forward deployed engineer can build

Candidates should have strong working knowledge in at least one of the following domains. In both, we move beyond static snapshots toward live, high-fidelity representations of a global supply chain.

You'll help customers move from "what happened?" to "what if?" and "what's next?" — architecting supply chain digital twins that let leaders stress-test their networks against real-world volatility. Using Lyric's Network Optimization and Simulation capabilities, you'll deliver solutions across network redesign, RCCP, production sequencing, vessel scheduling, and capacity expansion, among others. Frontier use cases push further: probabilistic modeling that replaces single-point forecasts with distribution-based simulations, resilience mapping that identifies single points of failure and models the impact of disruptions, and sustainability optimization that embeds carbon and ESG constraints directly into network design logic.

You'll replace rigid, sequential planning — Demand → Supply → Logistics — with concurrent, connected workflows where a change in a shipping schedule ripples instantly through to inventory targets and replenishment logic. Deploying capabilities like Optimal Stock Targets and the Supply Planning Optimizer, you'll embed science directly into operational execution. Frontier use cases include AI-assisted demand sensing that ingests external signals to refine short-term forecasts autonomously, dynamic rebalancing that triggers optimized inventory transfers across multi-echelon networks, and anomaly detection that doesn't just alert a human to a problem — it proposes the best corrective action based on cost and service trade-offs.

We're looking for engineers who are as comfortable whiteboarding a network optimization model as they are debugging a Python script before a customer go-live — people who bring deep domain expertise, a hands-on builder mentality, and an AI-native way of working.

• 8–12+ years in supply chain consulting, professional services, or SaaS delivery — with deep, hands-on expertise in at least one of the two domain tracks above

• A portfolio of complex, enterprise-grade supply chain solutions you have personally architected and delivered — not just managed

• Deep, practical knowledge of at least one core discipline — Network Optimization, S&OP, Demand Planning, Inventory Management, or Production Scheduling — and fluency in the mathematical models that power them

• Proficiency in Python and/or SQL: you write, debug, and optimize the data transformation and modeling logic that underpins a customer's supply chain planning process

• The ability to translate dense technical concepts for a C-suite audience and translate ambiguous business requirements into precise technical architecture — often in the same meeting

• You've worked with legacy planning tools — SAP IBP, Kinaxis, o9, Blue Yonder — and can articulate with precision why a composable, AI-native approach creates durable value

• When a customer makes a feature request, your first instinct is to ask "what problem are you actually trying to solve?" — you design for outcomes, not outputs

• You integrate AI tools into your delivery workflow as a default: LLM-assisted modeling, prompt engineering, AI-generated documentation — not as experiments, but as standard practice

• You thrive in fast-moving, lean environments where the playbook doesn't exist yet and building it is part of the job