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Artificial Intelligence Engineer

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Hybrid

Artificial Intelligence Engineer

Crescendo Global Leadership Hiring India

Artificial Intelligence Engineer

Skills

Google Cloud PlatformAIAWSAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

8 August 2026

What they need


Forward Deployed Engineer (FDE)

5+ Years

Noida/Gurugram/Kochi/Bangalore/Pune/Mumbai

Role Purpose The Forward Deployed Engineer builds and delivers production-grade, AI-powered systems directly with customers. This is a builder role: you will write, ship, and operate code in real customer environments. The FDE bridges product intent, engineering execution, and real-world deployment - owning solutions end-to-end. From discovery through production, the FDE ensures Agentic AI systems deliver measurable customer and business impact.

What Youll Do

Design and Deploy Production AI Systems

  • Design, build, and deploy production-ready AI systems in real customer environments.
  • Implement Agentic AI solutions (RAG, orchestration, tool/function calling, multi-step workflows).
  • Translate ambiguous customer problems into shippable technical architectures with clear trade-offs.
  • Move solutions from proof-of-concept to production with measurable adoption.
  • Engineer for enterprise-grade quality: secure, scalable, observable, resilient.

Own End-to-End Customer Delivery

  • Lead delivery from discovery and architecture through rollout, iteration, and operational readiness.
  • Embed directly with customer teams to understand tech stack, workflow constraints, and success criteria.
  • Make pragmatic architectural decisions under delivery pressure and evolving constraints in the customer stack.
  • Measure success by delivered impact and adoption - not effort or billable time.

Operationalize for Scale

  • Establish deployment patterns, evaluation loops, and monitoring frameworks.
  • Implement reliability patterns: retries, fallbacks, idempotency, and safe failure modes for agentic workflows.
  • Tune systems for performance and economics (latency, throughput, cost) without sacrificing quality.
  • Mentor engineers in production-grade AI delivery practices and reusable reference architectures.

Partner Across Product and Platform

  • Collaborate with AI Engineers, platform teams, and research to align architecture with long-term direction.
  • Provide real-world deployment feedback to strengthen platform capabilities and accelerators.
  • Balance experimentation velocity with enterprise-grade reliability.

Infuence at Executive and Customer Altitude

  • Communicate architecture, risks, and constraints clearly to executives and non-technical stakeholders.
  • Frame decisions across scope, reliability, speed, and cost in decision-ready language.
  • Build trust through transparency, delivery rigor, and measurable outcomes.

Transition Delivery to Durable Ownership

  • Ensure systems transition cleanly into sustained operation with clear ownership and documentation.
  • Define monitoring, support, and iteration plans; reduce ambiguity for inheriting teams.
  • Support long-term adoption and continuous improvement cycles.

Requirements

  • 5+ years in software engineering, architecture, or technical delivery; strong record of shipping production systems.
  • Demonstrated customer-embedded delivery: discovery-to-deployment in enterprise environments.
  • Hands-on backend and integration engineering (APIs, services, data integrations); strong debugging skills.
  • Experience building LLM applications (tool calling, RAG, orchestration) and creating eval/quality gates.
  • Production deployment discipline (CI/CD, environments, containers) and cloud experience (AWS/Azure/GCP).
  • Security and privacy fundamentals (secrets, access controls, PII handling) applied in implementations.
  • Strong communication and stakeholder influence; comfortable translating trade-offs to decision-makers.

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