Artificial Intelligence Engineer

Senior AI Engineer – AI Center of Excellence (AI CoE)

Location: New Jersey, Dallas TX ,Santa Clara CA, Ashburn VA, North Virginia(Hybrid)

Job Type: Fulltime

Domain - AI \& Data centers

Experience:

12\+ Years

Role Overview

This is a

strategic, hands-on senior engineering role

within the

AI Center of Excellence (AI CoE)

, responsible for designing, building, and operating

AI infrastructure and AI Factory platforms

across

hybrid environments (on‑prem, private cloud, and public cloud)

.

The role works closely with client

and leading OEM partners

as well as

internal Sales, Pre‑Sales, and Delivery teams

, to

identify, shape, and execute AI‑driven business opportunities

across the US and EU regions.

This is a

quota‑driven, techno‑commercial role

requiring

deep technical execution

along with

stakeholder interaction and customer‑facing leadership

.

Key Responsibilities

AI Infrastructure \& Platform Engineering

• Design, deploy, and operate

hybrid Kubernetes clusters

across

AWS, Azure, GCP, and on‑prem environments

(bare metal, NVIDIA DGX, Grace Hopper).

• Own production-grade

GPU infrastructure

using

NVIDIA GPU Operator

, including:

• CUDA, drivers, MIG
• GPU‑aware scheduling and resource isolation policies
• Build and maintain

high‑availability, scalable AI platforms

supporting enterprise workloads.

MLOps \& GenAI Platform Development

• Build

production‑grade MLOps pipelines

using:

• Kubeflow Pipelines
• GitOps (Argo CD / Flux)
• MLflow / DVC
• Deploy and operate

Large Language Models (LLMs)

using:

• NVIDIA Triton Inference Server
• TensorRT‑LLM
• vLLM
• Custom FastAPI / gRPC services
• Implement advanced inference techniques:
• Quantization, LoRA
• Dynamic batching
• Tenant‑level quota enforcement
• Safety \& content filtering integrations

Data \& Retrieval-Augmented Generation (RAG)

• Integrate and optimize

vector databases

for RAG and similarity search:

• Milvus, Pinecone, Qdrant, Weaviate, FAISS
• Enable scalable semantic search and GenAI-powered enterprise applications.

Observability, Security \& Reliability

• Implement full‑stack observability using:
• Prometheus, Grafana
• Loki / ELK
• OpenTelemetry
• Define and monitor

SLIs / SLOs

for AI platforms.

• Enforce

security and compliance

standards:

• Kubernetes RBAC
• OPA / Gatekeeper
• Vault / KMS
• Image signing, policy enforcement
• GDPR / HIPAA compliance

Cost, Performance \& Capacity Optimization

• Optimize GPU utilization through:
• Capacity planning
• Auto‑scaling \& spot instances
• Cost transparency and chargeback models
• Improve platform efficiency while maintaining performance SLAs.

Enablement \& Technical Leadership

• Convert experimentation into

reproducible production pipelines

.

• Enable engineering teams through:
• Technical documentation
• Tutorials and best practices
• Office hours and knowledge sessions
• Evaluate emerging technologies and lead

PoCs

across:

• NVIDIA innovations
• Open‑source ecosystems (Kubeflow, LangChain, vLLM, TGI, etc.)
• Drive the

AI Infra \& Platform technology roadmap

.

Required Experience \& Skills

Technical Expertise

• 8\+ years

of hands‑on experience designing and operating

production Kubernetes platforms

(cloud \+ on‑prem).

• Deep expertise in

NVIDIA GPU stack

(CUDA, MIG, GPU Operator).

• Strong hands‑on experience with:
• Kubeflow Pipelines or equivalent MLOps platforms
• Large‑scale LLM deployment and inference optimization
• Proficiency in

Python

and AI frameworks:

• PyTorch, TensorFlow
• Hugging Face, LangChain
• Infrastructure as Code (IaC):
• Helm, Kustomize, Terraform
• Experience with

vector databases

and RAG architectures.

• Strong

SRE / observability background

.

• Security‑first mindset with enterprise compliance exposure.

Nice to Have

• Experience with

NVIDIA DGX and Grace Hopper

platforms.

• Knowledge of

OpenShift, k3s

, or edge‑focused deployments.

• Experience with:
• KServe, LWS, serverless inference
• Contributions to open‑source projects (Kubernetes, Kubeflow, Triton, Milvus, vLLM).
• Certifications:
• CKA
• Cloud AI/ML certifications
• NVIDIA certifications

Qualifications

• B.E / B.Tech

with a minimum

60% across academics

.

• Proven experience delivering AI solutions across

on‑prem, cloud, and hybrid environments

.

• Strong analytical, strategic thinking, and stakeholder communication skills.
• Solid understanding of

data centers, cloud platforms, AI \& GenAI ecosystems

.

Role Specifics

• Hands‑on senior engineering role
• Strong

Techno‑Commercial orientation

• High ownership, visibility, and impact role

Disclaimer

*HCL is an equal opportunity employer, committed to providing equal employment opportunities to all applicants and employees regardless of race, religion, sex, color, age, national origin, pregnancy, sexual orientation, physical disability or genetic information, military or veteran status, or any other protected classification, in accordance with federal, state, and/or local law. Should any applicant have concerns about discrimination in the hiring process, they should provide a detailed report of those concerns to

[email protected]

for investigation.*

Compensation and Benefits

A candidate’s pay within the range will depend on their work location, skills, experience, education, and other factors permitted by law. This role may also be eligible for performance-based bonuses subject to company policies. In addition, this role is eligible for the following benefits subject to company policies: medical, dental, vision, pharmacy, life, accidental death \& dismemberment, and disability insurance; employee assistance program; 401(k) retirement plan; 10 days of paid time off per year (some positions are eligible for need-based leave with no designated number of leave days per year); and 10 paid holidays per year.