Platform Engineer

HCLTech; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

HCLTech in Bengaluru seeks an AI PRE Engineer to ensure AI/ML platforms are production-ready, highly reliable, observable, secure, and cost-efficient. This role bridges AI engineering, SRE, DevOps, and MLOps, defining standards across platform, data, model, application, and security layers.

The candidate will manage NVIDIA Enterprise AI Software, deploy GPU-enabled Kubernetes clusters, establish SLO/SLI frameworks, and drive PRRs, rollout safety, and cost-aware operations in a multi-tenant

Qualifications

• Bachelor’s degree in computer science, engineering, or information technology.
• 8-12 years of IT or platform engineering experience.
• 4-6 years implementing or managing enterprise platforms (AI, data, or cloud).
• 3-5 years in architecture or platform strategy roles supporting multiple teams.

Responsibilities

• Define and maintain production readiness standards across platform, data, model, application, and security layers.
• Manage the NVIDIA Enterprise AI Software stack and deployment of GPU enabled Kubernetes clusters.
• Establish SLO/SLI frameworks for latency, availability, quality, safety, and drift; implement error budget policies.
• Implement and configure the LLM inferencing services using kServe, NIM, AI Gateways.
• Lead incident response for AI workloads; perform post-incident reviews and drive systemic fixes.

Skills

Python

Go

Java

CI/CD

Terraform

Docker

Kubernetes

AWS/GCP/Azure

Prometheus

Observability

Education

Bachelor’s degree in CS/Engineering/IT

Master’s in systems architecture/cloud/AI

Tools

Terraform

Jenkins

GitHub Actions

Job description

AI PRE Engineer (Platform Reliability / Production Readiness Engineer)

The Role

An AI PRE Engineer ensures AI/ML platforms are production-ready, highly reliable, observable, secure, and cost-efficient, bridging AI engineering, SRE, DevOps, and MLOps disciplines.

Responsibilities:

• Define and maintain production readiness standards across platform, data, model, application, and security layers.
• Manage the NVIDIA Enterprise AI Software stack and deployment of GPU enabled Kubernetes clusters.
• Establish SLO/SLI frameworks for latency, availability, quality, safety, and drift implement error budget policies.
• Implement and configure the LLM inferencing services using kServe, NIM, AI Gateways
• Curate deployment blueprints (canary/shadow, blue-green, A/B) for models and prompts with rollback guidance.
• Standardize observability patterns for prompts, embeddings, latency, cost, quality, and safety telemetry.
• Own capacity engineering (token/concurrency budgets, GPU/CPU sizing, vector scaling, cache hierarchies).
• Define resilience patterns (timeouts, circuit breakers, fallbacks, idempotent retries, semantic/prompt caching).
• Set AI security baselines (secrets, private networking, egress controls) and mandate red-team & safety evaluations.
• Maintain compliance mappings (e.g., ISO 27001, SOC 2, GDPR/DPDP, HIPAA where applicable).
• Provide CI/CD pipelines, SDKs, Helm/Terraform templates, and policy‑as‑code for consistent delivery.
• Author PRR checklists, runbooks/playbooks, and DR/BCP blueprints (RTO/RPO, multi-region/site failover). Drive enablement (trainings, brown-bags) and maintain knowledge repositories and decision records.
• Partner with solution teams to validate architecture and non‑functional requirements (scale, latency, cost, safety).
• Conduct Production Readiness Reviews (PRRs) and certify releases across performance, security, privacy, and compliance.
• Implement observability (tracing, metrics, logs), dashboards, and SLO burn and cost anomaly alerting.
• Experience with different IDE such as Jupiter Notebook, Visual Studio Code, PyCharm, etc.
• Familiar with AI related libraries like LangChain, PandasAI, OpenAI
• Execute safe releases (canary/shadow/blue green), prompt/model versioning, feature flags, and rollback plans.
• Lead incident response for AI workloads; perform post‑incident reviews and drive systemic fixes.
• Govern token/cost budgets, autoscaling thresholds, and vector store performance for FinOps efficiency.

Qualifications & Experience

• Bachelor’s degree in computer science, Engineering, or Information Technology
• Master’s degree in systems architecture, Cloud Computing, or AI-related disciplines is preferred
• 8-12 years of overall IT or platform engineering experience
• 4-6 years implementing or managing enterprise platforms (AI, data, or cloud platforms)
• 3-5 years in architecture or platform strategy roles supporting multiple teams or business units
• Production readiness reviews, SLO/SLI/SLA design, incident management, RCA/postmortems, on-call support, and capacity planning for AI/ML platforms
• Hands‑on experience with AWS/GCP/Azure, GPU‑aware infrastructure, Infrastructure as Code (Terraform), Docker, Kubernetes (EKS/GKE/AKS), and managing large‑scale, multi‑tenant clusters
• Deploying ML/LLM workloads to production, model lifecycle management, RAG pipelines, safe rollouts (canary/shadow), rollback strategies, and managing inference scalability and latency
• Metrics, logging, tracing, and alerting using Prometheus/Grafana/OpenTelemetry or cloud-native tools; monitoring AI‑specific signals such as model drift, latency, token usage, and GPU utilization
• Strong coding (Python/Go/Java), CI/CD pipelines (GitHub Actions, Jenkins), GitOps, automated reliability tooling, security best practices (secrets management, access control, AI guardrails)

Certifications Required:

• NVIDIA Certified Professional: AI Infrastructure & Operations
• NVIDIA DLI - Deploying AI with Kubernetes & GPUs
• NVIDIA DLI - Building AI Infrastructure with NVIDIA Technologies
• Docker Certified Associate
• Red Hat Certified System Administrator (RHCSA)