Cloud & Platform Engineer Qualitykiosk Technologies

4.0 - 8.0 years

7 - 17 Lacs

Navi Mumbai

Remote

Job Title: Cloud & Platform Engineer AI Agent & Platform Deployment (GCP Preferred) Location: Remote / Hybrid (India preferred) About the Role: We are building an enterprise-ready AI agent and AI framework. This role is central to the success of the platform — you'll lead the design, deployment, and scaling of cloud-native infrastructure, enabling smooth operation of the agent across GCP and other cloud providers. You’ll work closely with AI engineers, product managers, and QA leaders to operationalize an LLM-based stack integrated with enterprise tools like Jira, Zephyr, and Selenium. Key Responsibilities: Design and deploy secure microservice-based architecture (Docker, Kubernetes, API Gateway) Build and manage CI/CD pipelines across cloud environments (GCP, AWS, Azure) Orchestrate cloud-native LLM endpoints (e.g., OpenAI, Claude, PaLM) Integrate external APIs securely (OAuth2 flows with Jira, TestRail, etc.) Manage vector DBs (Pinecone, FAISS, or Weaviate) and inference caching (Redis/in-memory) Set up observability: logging, alerting, audit trails (Cloud Logging, Prometheus, etc.) Implement TLS, JWT auth, secret management (Vault, Secret Manager) Optimize cost and scalability across multi-agent workflows Required Skills: 3–6 years in cloud infrastructure engineering or DevOps Proven experience with: Google Cloud Platform (GCP) Kubernetes & Docker API gateways (NGINX/Istio/Kong) Familiarity with modern LLM stacks (LangChain, Haystack, OpenAI APIs) Strong understanding of OAuth2, JWT, HTTPS/TLS, role-based access GitOps-style CI/CD (Cloud Build, GitHub Actions, ArgoCD) Nice to Have: Knowledge of LLMOps or MLOps principles Prior experience with test automation tools (Selenium, Playwright) Background in working with QA or developer productivity tools Contributions to open-source infra tools or AI orchestration frameworks Why Join Us? Be a founding infra engineer on an enterprise AI platform Work on the intersection of cloud, AI agents, and enterprise automation Fast-paced execution, ownership from day one, and cross-functional collaboration

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Sr. AI/ML Engineer Kezan Consulting

3.0 - 8.0 years

10 - 15 Lacs

Gurugram, Bengaluru, Delhi / NCR

Work from Office

Role & Responsibility Develop and maintain microservice architecture and API management solutions using REST and gRPC for seamless deployment of AI solutions. • Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization. • Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models. • Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements. • Familiarity with tools like Terraform, CloudFormation, and Pulumi for efficient infrastructure management. • Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub Actions, Jenkins) to ensure streamlined development workflows. • Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments. • Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. • Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development. • Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. • Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs. • Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling. • Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions. • Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function. • Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment. Technical Skills: • Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow) • Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques • Experience with big data processing using Spark for large-scale data analytics • Version control and experiment tracking using Git and MLflow • Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. • DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. • LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. • MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. • Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems. • LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. • General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. • Experience in creating LLD for the provided architecture. • Experience working in microservices based architecture.

Posted 3 weeks ago

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