Take the Next Step Toward Your Future

Key Responsibilities

• Design, build, and maintain cloud infrastructure using Infrastructure as Code (IaC). 

• Develop and maintain infrastructure using Terraform and implement reusable Terraform modules. 

• Manage and administer Kubernetes clusters, deployments, services, ingress, ConfigMaps, Secrets, and Helm charts. 

• Build and maintain CI/CD pipelines for automated build, testing, deployment, and release processes. 

• Containerize applications using Docker and manage container-based deployments. 

• Work with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP). 

• Implement infrastructure automation, configuration management, and deployment automation. 

• Monitor application and infrastructure performance, availability, and reliability. 

• Implement logging, monitoring, alerting, and observability solutions. 

• Troubleshoot infrastructure, deployment, networking, and production issues. 

• Implement security best practices across infrastructure, containers, Kubernetes, and CI/CD pipelines. 

• Collaborate with developers, architects, QA, security, and operations teams. 

• Participate in production deployments, incident management, and root-cause analysis. 

• Continuously improve DevOps processes, automation, reliability, and deployment efficiency. 

Requirements

Infrastructure as Code 

• Strong hands-on experience with Terraform. 

• Terraform modules, state management, remote backends, and workspaces. 

• Infrastructure provisioning and automation. 

• Experience with CloudFormation is an advantage. 

Kubernetes & Containers 

• Strong experience with Kubernetes. 

• Kubernetes architecture and cluster management. 

• Pods, Deployments, Services, Ingress, ConfigMaps, Secrets, RBAC, namespaces, resource limits, probes, and autoscaling. 

• Helm and Kubernetes package management. 

• Docker and containerization. 

• Experience with Kubernetes distributions such as EKS, AKS, or GKE. 

Cloud Platforms 

Strong experience with at least one major cloud platform: 

• AWS – EC2, VPC, IAM, S3, RDS, EKS, CloudWatch, Lambda. 

• Azure – VMs, VNet, IAM, AKS, Azure Monitor, Storage. 

• GCP – Compute Engine, VPC, IAM, GKE, Cloud Storage, Cloud Monitoring. 

CI/CD 

Hands-on experience with one or more: 

• Jenkins 

• GitHub Actions 

• GitLab CI/CD 

• Azure DevOps 

• Argo CD 

• AWS CodePipeline / CodeBuild 

Experience designing automated CI/CD pipelines for application deployment is required. 

• Experience with Vault or cloud-based secrets management. 

• Knowledge of Prometheus, Grafana, and OpenTelemetry. 

• Experience with SonarQube, Trivy, Snyk, or similar security tools. 

• Knowledge of DevSecOps. 

• Experience with high-availability and disaster-recovery environments. 

• Experience working in Agile/Scrum teams. 

Key Responsibilities

Model Development & Optimization

• Research, fine-tune, and deploy Transformer-based models (e.g., LLMs, encoder-decoder architectures) for tasks like text generation, summarization, and entity recognition.
• Optimize models for low-latency inferencing (e.g., quantization, distillation, ONNX runtime).
•  Architect and deploy low-latency inference pipelines, optimizing for throughput (10K+ RPS) using Triton, vLLM, or TensorRT.
• Implement distributed training (PyTorch FSDP, Horovod) across GPU clusters (AWS/GCP/Azure).
• Implement distributed model serving (multi-GPU, multi-node) with Kubernetes/Knative and autoscaling.
• Pioneer hybrid cloud strategies (e.g., on-prem GPU clusters + cloud burst) for cost-efficient training/inference.

ML Pipeline & Infrastructure

• Architect scalable ML pipelines (feature store, model registry, CI/CD) using MLflow, Kubeflow, or TFX.
• Containerize models (Docker) and orchestrate via Kubernetes for high-availability deployments.
• Design microservices (FastAPI, gRPC) to expose models as APIs with <100ms latency.

Performance & Observability

• Monitor model performance (drift, accuracy) using Prometheus/Grafana.
• Implement A/B testing and shadow deployments for zero-downtime updates.

Collaboration & Leadership

• Mentor junior engineers and collaborate with Data Scientists, DevOps, and Product Teams.
• Translate business requirements into SOTA NLP solutions with measurable ROI.

Required Skills & Qualifications

Technical Expertise:

1. 7+ years in NLP, with 3+ years focused on Transformer models (e.g., BERT, RoBERTa, GPT variants).
2. Mastery of PyTorch/TensorFlow, Hugging Face transformers, and distributed training (DDP, DeepSpeed), distributed systems (Ray, Horovod, Spark).
3. Production experience with Kubernetes, Docker, and cloud ML services (SageMaker, Vertex AI).
4. Strong Python skills (asyncio, multiprocessing) and familiarity with Go/Rust for high-performance services.

Deployment & Ops:

1. Proven track record of deploying low-latency model APIs (FastAPI, Triton Inference Server).
2. Knowledge of ML observability tools (Weights & Biases, Evidently).

Key Responsibilities

• Design, develop, and maintain scalable web applications using React.js and Next.js
• Build robust backend services and REST APIs using Python (FastAPI)
• Develop responsive and user-friendly UI components using HTML, CSS, and modern JavaScript
• Ensure cross-browser compatibility and mobile responsiveness
• Implement authentication, authorization, and secure coding practices
• Work with Docker to containerize applications and manage deployments

Requirements

Frontend

• Strong experience in React.js and Next.js
• Proficient in HTML5, CSS3, JavaScript (ES6+)
• Experience with responsive and adaptive design
• Understanding of browser rendering behavior and performance optimization

Backend

• Strong experience in Python
• Hands-on experience with FastAPI
• Experience in building RESTful APIs and microservices architecture

DevOps & Tools

• Hands-on experience with Docker
• Experience in setting up and managing CI/CD pipelines
• Familiarity with version control systems like Git

System Knowledge

• Basic understanding of Linux systems
• Experience with server environments and deployment processes
• Set up and maintain CI/CD pipelines for automated builds and deployments
• Collaborate with UI/UX designers, product managers, and other developers
• Optimize applications for maximum speed and scalability
• Troubleshoot, debug, and upgrade existing systems