Forward Deployed Engineer

Overview

Forward Deployed Engineer embedded inside a client's enterprise to organize the deployment of AI workflows and architectures across diverse AI and technology platforms, and to facilitate solution design across the enterprise technology stack to integrate into the ecosystem of AI models and multi-provider platforms. Accountable for production outcomes such as reliability, time-to-value, adoption and scalability.

What you'll do

• Embed directly with client infrastructure and engineering teams to design, execute and operationalize end-to-end transformation spanning cloud foundation, hyperconverged infrastructure, platform engineering, application modernization, database transformation, SAP, network and security, and AI platform deployment.
• Move from ambiguous infrastructure problems to a working production environment through rapid iteration.
• Execute application migration hands-on, applying rehost, re-platform and redeploy strategies per workload to land on an AI-ready, modernized target platform.
• Architect and implement network and security foundations, including Zero Trust, SASE/SSE, micro-segmentation, container security and application AI governance.
• Design and govern transformation architectures across compute (HCI/cloud), platform engineering and Internal Developer Platforms, Kubernetes orchestration, CI/CD pipelines, network, security, storage, containers, databases, applications and AI platform layers.
• Build the Data and AI foundation layer, designing data architectures for structured and unstructured data and deploying the AI platform layer that enables modernized workloads to leverage AI at scale.
• Architect and modernize database estates, including relational and NoSQL migration, schema conversion, performance tuning and cloud-native database deployment.
• Leverage Python end-to-end for infrastructure automation, deployment scripting, data pipeline engineering and AI agent development.
• Translate technical architecture into business impact for client CTO, CFO and CISO, shaping modernization roadmaps, ROI backlogs and AI adoption strategy.
• Build reusable patterns, playbooks and accelerators through architecture workshops, proofs of concept and code-with sessions, leaving the client team able to operate, scale and extend the platform independently.

What you'll need

• Minimum 12 years of experience is required.
• 15 years full-time education.
• Cloud Design and Build.
• Minimum 2 years of working experience of AI platform ecosystems, including cloud-native AI services (AWS, Azure, GCP), open-source model deployment and the infrastructure layer required to run AI workloads reliably at enterprise scale.
• 3 to 5 years of engineering experience with cloud-native systems, with the ability to deploy and guide a team to deploy AI use cases.
• Minimum 1 year of experience designing, deploying and operating AI platforms and infrastructure, including model serving environments, AI-ready compute, data pipelines and platform integration, in production enterprise environments.
• 10+ years of infrastructure engineering experience, with demonstrable production delivery across cloud platforms, network, security, Kubernetes, containerization, platform engineering and enterprise application environments.
• Track record of owning and delivering infrastructure transformation outcomes inside live enterprise environments.
• Ability to connect infrastructure decisions to business and financial outcomes.
• Experience presenting to and building trust with senior client stakeholders at CTO, CFO or CISO level.
• Engineering mindset.
• Strong problem-solving skills.

Nice to have

• Cloud Automation DevOps.
• Cloud Strategy and Assessment.
• Docker Kubernetes Architecture.
• Infrastructure as Code (IaC).
• Database Architecture.

Details

• Location: Gurugram.
• Flexible to work in a 24*7 environment.