• Own onboarding, handoffs, and lifecycle playbooks from close to renewal • Define customer engagement models by segment (SMB / Mid-Market / Enterprise) • Ensure clear ownership and accountability at every stage of the lifecycle • Define and own KPIs across all CS functions (adoption, health, churn, NRR, renewals) • Establish weekly and monthly operating cadence and performance reviews • Drive forecasting accuracy for renewals and expansion • Hire, onboard, and scale critical CS roles as the business grows • Coach and performance-manage CSMs, Support, and Solution Engineering • Build a high-accountability, customer-first culture • Own churn prevention strategy and early risk identification • Ensure every at-risk account has a documented mitigation plan • Partner with Sales on expansion readiness, timing, and qualification • Support renewals and expansions with clear value articulation • Partner closely with VP Sales on retention modelling, forecasting, and GTM alignment • Align CS with Product to close feedback loops • Act as the voice of the customer internally • Own CS tooling and data hygiene (CRM, health scoring, reporting) • Ensure consistent documentation, notes, and account plans • Drive operational discipline and eliminate reactive fire drills • Other duties as assigned.

• Partner with automotive OEMs and Tier 1 suppliers to identify, shape, and prioritize high-impact AI-driven use cases, guiding customers toward scalable and high-leverage solutions • Act as a trusted advisor to customers by shaping problem definition, challenging assumptions, and guiding technical decision-making toward scalable AI solutions • Drive alignment and adoption of solutions by clearly articulating trade-offs, ROI, and long-term architectural implications to technical and executive stakeholders • Design, integrate, and deploy AI/ML-based solutions across cloud and in-vehicle environments, enabling end-to-end vehicle-to-cloud use cases with reliable data flow and system interoperability • Develop and iterate AI models, PoCs, and production-ready solutions, continuously improving performance based on real-world data and customer feedback • Lead technical discussions and solution reviews focused on AI feasibility, model performance, trade-offs, and system limitations • Identify risks related to AI deployment and integration (e.g., data availability, security constraints, model reliability) and drive mitigation strategies • Collaborate with internal teams (AI Platform, Product, Engineering) to enhance AI capabilities, model deployment pipelines, and platform scalability

• Embed with strategic customers as a technical partner • Architect, build, deploy, and operate agentic Generative AI systems • Work in cloud, on-prem, and edge/air-gapped environments • Prototype and deploy systems leveraging the core products and design patterns of the Unicorn Delivery Service • Scope, sequence, and lead delivery of solutions • Codify reusable patterns for mission environments • Act as the technical face to customer leadership • Ability and interest to travel up to 25%

About CodeRoad CodeRoad provides end-to-end software development services, helping businesses scale with ideal infrastructure solutions. From staff augmentation to dedicated IT teams and general software engineering, our nearshore technology services empower businesses to thrive in an ever-evolving digital landscape. About the Role We’re looking for a Junior Agentic AI Engineer to co-build next-generation AI systems powered by RAG and agent-based architectures. You’ll work hands-on designing intelligent workflows, connecting LLMs with real data, and contributing to production-ready AI tools. This is not a “prompting-only” role — you’ll be coding, integrating, testing, and learning fast in a real-world AI environment. Key Responsibilities - Co-develop RAG pipelines and agentic workflows for real business use cases - Implement vector search, chunking strategies, and prompt orchestration - Integrate LLMs with APIs, databases, and external systems - Contribute to model deployment, monitoring, and evaluation pipelines - Write clean, maintainable code and support testing (unit & integration) - Collaborate with senior engineers on system design and improvements Requirements - 1–3 years of experience with Python and backend development - Hands-on exposure to LLM-based applications or AI tooling - Experience with: LangChain / LangGraph, FastAPI and REST APIs, SQL databases - Understanding of: RAG concepts and vector databases, Prompt engineering basics (ReAct, guardrails) - Familiarity with Docker and modern dev workflows - Strong problem-solving mindset and willingness to learn fast - Advanced English for working with US teams Nice to Have - Experience with MLFlow or evaluation frameworks - Exposure to embeddings (dense/sparse) and NLP preprocessing - Azure ecosystem (Blob Storage, Container Apps) - Memory frameworks for agentic systems What You’ll Love - 100% Remote - Holidays off - Paid Time Off - Health insurance assistance program - Competitive USD compensation - Strong team culture and collaborative environment - Ongoing training and growth opportunities