Crescendo Global Leadership Hiring India
Artificial Intelligence Engineer
Skills
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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