Experience:

• 6+ years of experience in Forward Deployed Engineering, ML Engineer, Applied AI Engineer, AI Infrastructure, Technical Consulting, or equivalent customer-facing engineering roles supporting production AI systems

Responsibilities:

• Architect, deploy, optimize, and scale production AI and agentic systems on DigitalOcean’s AI-Native Cloud
• Support complex migrations, production-ready PoCs, deployment acceleration, and long-term workload expansion
• Optimize distributed inference and runtime performance including GPU efficiency tuning and latency/cost optimization
• Act as the first customer for AI-native platform capabilities and provide operational insights and feedback
• Build scalable deployment assets such as benchmarking systems, automation tooling, AI starter kits, and operational playbooks
• Collaborate with GPU vendors, model providers, infrastructure partners, and ISVs on co-development and technical validation
• Enable customer-facing technical teams and partner teams through validated deployment patterns and technical guidance

Additional responsibilities:

• Travel up to 30% for customer engagements, workshops, conferences, and internal collaboration
• Consistently overlap with North American business hours including availability until at least noon Eastern Time
• Manage high-impact production deployments and strategic technical initiatives
• Establish technical credibility with CTOs, Principal architects, Product Engineering teams, and ecosystem partners

Nice to have:

• Experience building deployment standards, technical enablement programs, platform adoption frameworks, or ecosystem integration strategies
• Active contributor to open-source AI, infrastructure, orchestration, or developer tooling ecosystems
• Experience collaborating with GPU vendors, infrastructure providers, model vendors, or ecosystem partners on benchmarking, optimization, technical validation, or launch readiness

More skills:

AI-native applications, SGLang, Ray Serve, NVIDIA Dynamo, LLM optimization techniques, continuous batching, quantization, KV-cache optimization, speculative decoding, NVIDIA GPU platforms, AMD GPU platforms, CUDA, ROCm, TensorRT, Triton, NCCL, RCCL, NVLink, XGMI, RoCE, Kubernetes (K8s), distributed systems, networking, Infrastructure as Code, AI orchestration frameworks, agent frameworks, benchmarking, automation tooling, deployment workflows, performance optimization, runtime performance, latency optimization, workload economics

Other:

• Competitive benefits including Employee Assistance Program, flexible time off, and local employee meetups
• Reimbursement for relevant conferences, training, and education
• Access to LinkedIn Learning's 10,000+ courses for continued growth and development
• Salary based on market data, experience, and skills with potential bonus and equity compensation
• Equal opportunity employer with non-discrimination policy
• Hybrid work model requiring presence in Bengaluru and willingness to relocate if needed
• Application limit of 3 positions within any 180-day period