Experience: 3-6 years

Location: Pune

Role Overview:

This is the execution-focused counterpart to the Forward Deployed Engineer role. You will support and carry out platform installations, configuration and operational tasks in customer environments, troubleshoot issues across the application, platform and infrastructure layers, and act as a technical presence on customer sites. The Forward Deployed Engineer owns technical outcomes for an engagement and builds custom solutions; this role owns competent, reliable execution of defined technical work and is the natural path into that senior role. Travel and work from customer premises are mandatory.

Key Responsibilities:

- Support and execute cluster and AI platform stack installations in customer environments following defined procedures
- Configure platform components, endpoints, certificates, API keys, user groups and access policies as specified
- Deploy model bundles and apply configuration changes to deployments, replicas and service tiers under guidance
- Validate deployments against acceptance checks and document what was configured and why
- Handle day-to-day operational tasks: health checks, log review, dashboard monitoring, routine configuration changes and ticket resolution
- Troubleshoot issues across application, model serving, cluster and infrastructure layers, and escalating with the diagnostic detail needed to act
- Route faults correctly across the vendor boundary when the cause sits in hardware, on-premises network or local tunnel-endpoint scope
- Maintain and improve runbooks and knowledge base articles from what you encounter in the field
- Act as a technical representative on customer sites and in customer technical discussions
- Gather and document customer requirements, dependencies and environment constraints,



and pass them upstream in usable form
- Communicate status, findings and technical concepts clearly to customer technical teams and business stakeholders
- Support multiple customer projects in parallel, working with forward-deployed, ML systems, platform and project management teams
- Provide field feedback on recurring issues, documentation gaps, and product friction
- Work alongside agentic AI tooling for diagnostics, scripting and documentation, with human verification before production

Category

Tools and technologies

Operating systems and shell

RHEL and Linux administration, systemd, journald, Bash scripting, SSH and out-of-band console access

Kubernetes and containers

kubectl for logs, describe, events and exec, Helm release inspection and upgrades, Docker, namespace and RBAC basics, pod and node troubleshooting

Platform configuration

Helm values-file changes, secret and certificate configuration, user group and service tier setup, API key issuance, model bundle deployment through custom resource

Inference APIs

OpenAI-compatible chat completions and embeddings endpoints, streaming behaviour, cURL, Postman, Python and Node SDKs, interpreting response and usage fields

Observability

Grafana dashboard interpretation, Prometheus query basics, OpenSearch log search, alert triage, correlating symptoms across layers

Networking

DNS, TLS certificate validation, load balancers and ingress,



firewall and proxy behaviour, IPSec tunnel status checks, diagnostics with dig, curl, ss and tcpdump

AI and ML foundations

LLM inference concepts including tokens, context windows and latency metrics, retrieval-augmented generation pipelines, embedding and vector store basics, ML pipeline exposure

Automation, tooling and documentation

Python, Bash, Ansible exposure, Git, CI/CD familiarity, Jira, Confluence, ServiceNow or Freshservice, runbook authoring, agentic AI assistants for diagnostics and scripting

Minimum Requirements:

- Solid Linux administration and comfortable command-line troubleshooting
- Working Kubernetes knowledge: inspecting workloads, reading logs and events, and diagnosing common pod and node failures
- Understanding of AI and ML systems, ML pipelines and LLM inference concepts, with exposure to ML deployment and production environments
- Understanding cloud and infrastructure concepts, plus DevOps and CI/CD practice
- Ability to troubleshoot across application, ML and infrastructure layers and escalate with useful detail
- Experience across multiple customer projects or engagements, with prior client-facing exposure
- Scripting ability in Python or Bash, and solid documentation habits
- Excellent communication, professional customer-facing manner, and willingness to travel and work from customer premises

Preferred Requirements:

- Kubernetes certification such as CKA, or equivalent demonstrated skill level
- Experience supporting a vendor platform product in customer environments, including Helm-managed application deployment
- Experience with observability tooling in an operational support context
- Domain exposure to banking and financial services, public sector or telecommunications

📌 Solutions Architect Engineer (India)
🏢 Emergys
📍 India