1 day ago
Gurgaon, IndiaSenior
Responsibilities
- Partner with sales and enterprise customers to conduct technical discovery and design feasible AI solution architectures and integration plans.
- Own customer technical account plans, adoption milestones, API usage, blockers, use-case qualification, and expansion support.
- Advise customers on LLM selection, RAG, prompt engineering, agentic workflows, tool use, data pipelines, and enterprise integrations.
- Design evaluation frameworks and acceptance criteria covering quality, latency, cost, reliability, and business goals.
- Create demos, lightweight proofs of concept, reference architectures, technical content, and reference implementations.
- Guide enterprise security, compliance, governance, integration, and production deployment activities.
- Coordinate customers and internal Product, Engineering, PMM, sales, partner, legal, security, and executive stakeholders.
- Build and iterate on customer-facing prototypes, full-stack application integrations, RAG pipelines, and AI API solutions.
- Provide technical enablement and thought leadership for sales teams, partners, customers, and internal stakeholders.
- Travel up to 30% of the time.
Requirements
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent experience.
- 5+ years of experience in a technical customer-facing role such as AI Architect, Solutions Architect, Sales Engineer, Technical Account Manager, Enterprise Architect, or highly technical Forward Deployed/Solutions Engineering role.
- Experience guiding enterprise customers through sales lifecycles, purchasing decisions, deployment decisions, and multi-stakeholder engagements.
- Experience with consultative selling and driving technical wins in competitive enterprise sales cycles.
- Strong understanding of cloud-native architectures, API design patterns, deployment concepts, data integration, identity, security, privacy, SSO, data governance, networking, and enterprise integration.
- Comfort with Python for lightweight demos, scripts, and reference implementations.
- Familiarity with Generative AI APIs, prompt engineering, RAG architectures, vector databases, evaluation frameworks, LLM APIs, agentic workflows, and tool use.
- Ability to design use-case-specific evaluations and assess quality, latency, cost, and reliability against customer requirements.
- Experience managing technical engagements, resolving blockers, and coordinating customer and internal teams through evaluation, integration, and adoption.
- Ability to explain complex AI and ML concepts to executives, IT leaders, engineering teams, and non-technical business leaders.
- Preferred experience integrating AI tools into workflows, implementing AI safety and responsible AI practices, and building LLM guardrails.
- Preferred familiarity with SOC 2, ISO 27001, FedRAMP, and HIPAA in AI deployment contexts.
- Preferred experience architecting AI/ML solutions at scale, including model deployment, monitoring, governance, observability, evaluation, and production adoption.
- Preferred hands-on experience with AI APIs such as OpenAI and Anthropic and frameworks such as LangChain and LangGraph.
- Preferred experience creating technical enablement programs and maintaining current knowledge of prompt/context engineering and agent orchestration.
Benefits
- Travel requirement is up to 30% of the time.
Tech Stack
Categories
Solutions Engineering
About Meta
Meta builds social platforms and communication apps—including Facebook, Instagram, WhatsApp, and Messenger—and develops AR/VR hardware and software such as Quest to power immersive computing. It monetizes primarily through advertising tools for businesses, with additional revenue from devices and services, and operates a massive global infrastructure. Founded in 2004 and headquartered in Menlo Park, California, Meta Platforms, Inc. is a public company traded on Nasdaq under the ticker META.