FDE Lead

Job Summary

The FDE Lead is a senior technical role responsible for owning and driving the end-to-end technical outcomes for strategic enterprise accounts. This role involves architecting and deploying customer-specific AI solutions, integrating with existing enterprise systems, and ensuring the successful transformation of customer workflows through AI agents. The FDE Lead acts as the primary technical owner, building deep customer trust, and fostering long-term relationships by delivering impactful results aligned with customer business metrics.

Responsibilities

• Own the Customer's Technical Outcome End-to-End: Serve as the single technical owner for strategic accounts, managing architecture, delivery commitments, and escalation paths throughout multi-year engagements.
• Build Customer-Specific AI Systems on Platform Primitives: Design and ship tailored AI applications utilizing platform components such as Vegh, Tapas-trained TSMs, and Tattva to solve specific business problems.
• Architect Integration with the Customer's Existing Stack: Lead technical discussions on integrating solutions with enterprise systems like ERPs, CRMs, data lakes, and identity providers, ensuring seamless connectivity.
• Tune, Fine-tune, and Evaluate for Domain Specificity: Drive customer-specific model fine-tuning, dataset creation, evaluation design, and prompt engineering to achieve high accuracy and operational effectiveness.
• Manage Customer Engineering Relationships: Maintain regular technical communication with the customer’s team, manage scope and expectations, and conduct post-mortems when issues arise.
• Lead a Small Team of FDEs: Guide and mentor 5-7 senior and mid-level engineers, review their work, set technical standards, and contribute directly to critical components.
• Partner with Customer Success: Collaborate closely with the Customer Success team on strategic initiatives, expansion opportunities, and alignment of technical and business narratives.
• Identify and Escalate Patterns: Recognize reusable patterns across accounts and escalate for productization to improve platform scalability and efficiency.
• Customer Outcome Reporting: Develop dashboards, define metrics, and present technical progress and impact in executive steering committees, translating technical results into business value.
• Hands-on Implementation: Maintain active involvement in coding, deploying, and shipping critical components, especially those with complex customer-specific requirements.

Qualifications

• Experience: 8-12 years in software engineering, AI engineering, or applied ML, with 2-3 years in customer-facing engineering roles owning outcomes.
• Technical Education: Degree in Computer Science, Engineering, or related field; advanced degrees preferred but not required.
• Agentic AI & ML Systems: Proven experience building production agent systems, with shipped LLM-powered or agent-based applications within the last 18 months.
• Full-Stack Engineering: Competence across frontend dashboards, backend services, data pipelines, and deployment infrastructure.
• Programming Skills: Expert in Python, with proficiency in ML frameworks like PyTorch; experience with TypeScript/JavaScript, SQL, Bash, Docker, Kubernetes.
• Enterprise Systems Integration: Hands-on experience integrating with 3-4 enterprise systems such as SAP, Salesforce, Oracle, ServiceNow, Workday, Snowflake, Databricks, or identity providers like Okta, Azure AD.
• Customer-Facing Maturity: At least 2 years of direct customer engagement, with experience managing technical relationships, escalations, and scope negotiations.
• Domain Expertise: Knowledge in one or two industry domains such as healthcare RCM, financial services, logistics, supply chain, or retail.
• Leadership & Mentorship: Demonstrated ability to lead small engineering teams, provide constructive feedback, and foster engineering excellence.
• Communication & Empathy: Strong written and verbal communication skills, capable of explaining complex technical concepts to non-technical stakeholders.
• Outcome-Oriented: Proven track record of delivering measurable business impact through technical solutions.
• Passion for AI in Business: Deep enthusiasm for applying AI to solve real-world operational problems and improve business metrics.

Preferred Skills

• Experience with frameworks like LangGraph, LlamaIndex, or similar orchestration tools.
• Familiarity with model fine-tuning, evaluation methodologies, and inference economics.
• Experience with customer-facing dashboards and monitoring tools.
• Knowledge of enterprise security, compliance, and certification standards.
• Ability to operate across multiple technical domains, including data engineering, infrastructure, and front-end development.

Experience

• 8-12 years of professional experience in software/AI engineering.
• At least 2-3 years in customer-facing roles owning technical outcomes.
• Proven experience leading small engineering teams in a client environment.
• Hands-on experience with enterprise system integrations and AI/ML deployment.

Environment

• The role involves working in a customer-facing, hybrid environment, often embedded within client sites or remote settings.
• Requires regular interaction with enterprise stakeholders, including CTOs, engineering teams, and business leaders.
• The work involves technical collaboration, code development, and system integration, often in fast-paced, high-stakes projects.