Digital Ocean

4 weeks ago

Senior Forward Deployed Engineer I (AI/ML)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 4 hours agoStatus: Live

Job Summary

Senior Forward Deployed Engineer (AI/ML) who partners with AI-native customers to deploy, optimize, and scale production AI systems on DigitalOcean’s AI-Native Cloud. You’ll work across AI infrastructure, runtime systems, and orchestration, validating platform capabilities with real workloads, building deployment frameworks and reference architectures, and aligning with product and research teams to accelerate customer adoption in Bengaluru.

Location: Bengaluru

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•Architect, deploy, optimize, and scale production AI and agentic systems on DigitalOcean’s AI-Native Cloud, including migrations, PoCs, deployment acceleration, and workload expansion across inference and runtime platforms
•Optimize distributed inference and runtime performance through benchmarking, GPU efficiency tuning, KV-cache optimization, and multi-node deployments with latency and cost considerations
•Serve as the “first customer” for new AI-native platform capabilities, surfacing operational insights and architectural gaps to product and research teams
•Build scalable deployment assets including benchmarking systems, automation tooling, AI starter kits, deployment frameworks, and reference architectures to accelerate platform adoption
•Collaborate with GPU vendors, model providers, infrastructure partners, and ISVs to validate deployment patterns and enable customer-facing teams; travel up to 30% for engagements

Travel: Medium travel

Pay and Benefits

Perks:EquityLearning Budget

Key Requirements

•Strong experience deploying and operating production AI systems at scale (AI-native workloads, agentic runtimes, and inference pipelines)
•Hands-on experience with inference/serving frameworks and GPU ecosystems (e.g., CUDA, TensorRT, Triton)
•Proficiency in Python or Go for tooling and automation
•Ability to travel up to 30% for customer engagements and collaborate across time zones
•Customer-facing, cross-functional collaboration with product, engineering, and ecosystem partners

Experience:AICloud computing

Skills:CommunicationCollaborationProblem-solvingCustomer-facingAdaptability

Languages:English

Tech Stack:PythonGoKubernetesCUDAROCmTensorRTTritonNCCLRay ServeLangGraphLlamaIndexOpenAI

Company Brief

Digital Ocean

Provides cloud infrastructure and developer-focused cloud services including scalable droplets, managed databases, Kubernetes, object storage, and networking to simplify deploying and managing applications for developers and small-to-medium businesses.

Industry: Cloud Computing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 250M to 500M

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: New York, United States

Founded: 2011

Glassdoor: 3.8

Website LinkedIn

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Digital Ocean

4 weeks ago

Senior Forward Deployed Engineer I (AI/ML)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 4 hours agoStatus: Live

Job Summary

Location: Bengaluru

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•Architect, deploy, optimize, and scale production AI and agentic systems on DigitalOcean’s AI-Native Cloud, including migrations, PoCs, deployment acceleration, and workload expansion across inference and runtime platforms
•Optimize distributed inference and runtime performance through benchmarking, GPU efficiency tuning, KV-cache optimization, and multi-node deployments with latency and cost considerations
•Serve as the “first customer” for new AI-native platform capabilities, surfacing operational insights and architectural gaps to product and research teams
•Build scalable deployment assets including benchmarking systems, automation tooling, AI starter kits, deployment frameworks, and reference architectures to accelerate platform adoption
•Collaborate with GPU vendors, model providers, infrastructure partners, and ISVs to validate deployment patterns and enable customer-facing teams; travel up to 30% for engagements

Travel: Medium travel

Pay and Benefits

Perks:EquityLearning Budget

Key Requirements

•Strong experience deploying and operating production AI systems at scale (AI-native workloads, agentic runtimes, and inference pipelines)
•Hands-on experience with inference/serving frameworks and GPU ecosystems (e.g., CUDA, TensorRT, Triton)
•Proficiency in Python or Go for tooling and automation
•Ability to travel up to 30% for customer engagements and collaborate across time zones
•Customer-facing, cross-functional collaboration with product, engineering, and ecosystem partners

Experience:AICloud computing

Skills:CommunicationCollaborationProblem-solvingCustomer-facingAdaptability

Languages:English

Tech Stack:PythonGoKubernetesCUDAROCmTensorRTTritonNCCLRay ServeLangGraphLlamaIndexOpenAI

Company Brief

Digital Ocean

Industry: Cloud Computing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 250M to 500M

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: New York, United States

Founded: 2011

Glassdoor: 3.8

Website LinkedIn