Applied AI Architect, Meta Enterprise Platform (India)

Meta; Gurugram District; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Meta is seeking an Applied AI Architect for its Enterprise Platform to serve as a strategic technical advisor. You will help enterprise customers understand Enterprise AI products (such as Model API) and design architectures to unlock value, guiding from discovery through deployment and ongoing adoption.

This role blends pre-sales technical leadership with post-sales customer adoption and expansion, crafting scalable architectures, reference implementations, and end-to-end AI solutions to drive

Qualifications

• Extensive experience in enterprise AI architectures and generative AI ecosystems.
• Ability to design scalable architectures and reference implementations.
• Experience engaging with enterprise customers across IT and leadership teams.

Responsibilities

• Lead pre-sales technical engagements and post-sales adoption.
• Design architectures, develop evaluation frameworks, and reference implementations.
• Collaborate with customers and internal teams to ensure successful deployment.

Skills

Generative AI

Enterprise architecture

Cloud-native architectures

Python

LLM APIs

AI safety

Communication to executives

Reference implementations

Education

Bachelor's degree in Computer Science, Engineering or related field

Tools

LangChain

LangGraph

Vector Databases

OpenAI API

Anthropic API

Job description

As an Applied AI Architect for the Meta Enterprise Platform, you are the strategic technical advisor who helps enterprise customers understand the full potential of our Enterprise AI products (such as Model API) and design the right architectures to unlock that value. You combine deep knowledge of Generative AI with enterprise architecture expertise and effective communication skills to guide customers from initial discovery through successful deployment, ongoing adoption and expansion.

This role combines pre-sales technical leadership with post-sales customer adoption and expansion. You will design scalable architectures, develop evaluation frameworks, create reference implementations, and serve as the technical authority that gives customers and internal stakeholders the confidence to purchase. You are the architect of the blueprint - ensuring the right technical foundation is in place before and during implementation. You will not just design architectures; you will help to deploy end-to-end AI solutions that drive actual AI usage such as API consumption and business ROI. You will own the customer's technical success from the initial scoping phase through to live production deployment, post-sales adoption and growth.

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 a highly technical Forward Deployed / Solutions Engineering role Experience engaging enterprise customers through the sales lifecycle, purchasing and deployment decisions, involving multiple stakeholders across IT, Security, Legal, and executive leadership Experience in consultative selling with a track record of driving technical wins in competitive enterprise sales cycles Strong understanding of cloud-native architectures (AWS, GCP, or Azure), API design patterns, modern deployment concepts, data integration, identity, security and privacy, and enterprise integration approaches (SSO, data governance, networking) Comfortable with Python and able to build lightweight demos, scripts, and reference implementations (not expected to write production-grade application code) Familiarity with the Generative AI ecosystem: APIs, prompt engineering, RAG architectures, vector databases, and evaluation frameworks. Ability to use LLM APIs to build and evaluate lightweight demos, prototypes and reference implementations Ability to design use-case-specific evaluation tests and assess quality, latency, cost and reliability against agreed customer requirements and business goals Experience planning and managing customer technical engagements, resolving blockers and coordinating customer and internal teams through evaluation, integration and adoption. A high degree of ownership, and the ability to thrive in highly ambiguous, fast-paced environments Experience communicating and translating complex AI/ML concepts for C-suite executives, IT leaders, engineering teams and non-technical business leaders while simultaneously working with customer engineering teams Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience with AI safety, responsible AI practices, and implementing guardrails for LLM applications in regulated industries Experience creating and delivering technical enablement programs for sales teams, partners, or customer communities Familiarity with enterprise security and compliance frameworks (SOC 2, ISO 27001, FedRAMP, HIPAA) in the context of AI deployments Experience architecting AI/ML solutions at scale in enterprise environments, including model deployment, monitoring, and governance. Experience helping customers move from evaluation to live applications with measurable business value Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience with AI observability, evaluation frameworks (evals) assessing quality, latency, cost and reliability, and implementing enterprise-grade security guardrails for AI applications Hands-on experience building applications using AI APIs (e.g., OpenAI, Anthropic) and familiarity with the Generative AI ecosystem (Vector Databases, RAG architectures, agentic workflows, tool use and frameworks like LangChain/LangGraph) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies