Azure Cloud/DevOps Specialist Consultant Bangalore
Mandatory Skills
Bachelor's or Master's degree in Computer Science, Engineering, or a related field, with 3-7 years of hands-on experience in cloud, DevOps, MLOps, LLMOps, AgentOps
Proven experience as a DevOps Engineer, MLOps Engineer, Cloud Architect, or Software Engineer in an agile, cloud-native environment.
Hands-on experience implementing endtoend MLOps pipelines, including data ingestion, model training, model registry, CI/CD for ML, and feature stores.
Experience working with frameworks/tools like MLflow, Kubeflow, Feast, Airflow, Seldon, BentoML, or similar.
Knowledge of model monitoring, drift detection, model governance, and ML observability.
Experience deploying, monitoring, and optimizing LLMs and generative AI workflows, including prompt workflows, vector stores, evaluation pipelines, and safety guardrails.
Familiarity with LLMOps best practices, including model versioning, evaluation, deployment automation, and RAG-based systems.
Understanding of AI agents, orchestration frameworks, and agent workflows using tools like LangChain, Semantic Kernel, AutoGen, or similar.
Experience building taskoriented or multiagent systems, integrating LLMs with enterprise systems, APIs, and cloud-native services.
Ability to design scalable AI agent architectures with robust error handling, grounding, and monitoring
Hands-on experience with cloud platforms (AWS, Azure, GCP) - AI/ML services experience preferred.
Proficient in infrastructure automation tools such as Terraform, Helm Charts, Ansible, etc.
Strong understanding of DevOps practices/tools, including CI/CD, Pipelines, GitHub Actions, and Infrastructure as Code.
Proficient in Python, including automation scripting, data processing, and integration with cloud/ML workflows.
Basic scripting knowledge in Python, able to write automation scripts in Python and Groovy DSL.
Experienced in managing the delivery of high-availability infrastructure to support mission-critical systems and services.
Experience with source version control tools like Azure Repos and GitHub.
Experience with containerization (Kubernetes, Docker) and orchestration.
Strong knowledge of networking concepts (TCP/IP, VNet/Subnet, DNS, Load Balancers, Application Gateway, etc.) and security practices (VPN, Firewalls, encryption).
Familiarity with databases and caching mechanisms used in our stack, such as PostgreSQL and Redis.
Experience in handling complex IT infrastructure solution design and implementation.
Excellent communication and interpersonal skills, effective problem-solving skills, logical thinking ability, and strong commitment to professional and client service excellence.
Excellent teamwork skills and the ability to direct efforts of cross-functional teams for a collaborative proposition. - Strong synthesis and analytical skills.
Experience working in a consulting environment is an added advantage.
- Bachelor's degree in Computer Science, Engineering.
- 3+ years of experience in Cloud, DevOps and MLOps role
- Excellent problem-solving skills
Primary Roles and Responsibilities
Architect, design, and govern scalable, secure, and highly available cloud infrastructure across Azure, GCP, and AWS to support modern application stacks (Django, Python, Node.js, React.js).
Design and implement enterprise-grade MLOps pipelines, covering feature engineering, training workflows, model registry, CI/CD for ML, and automated deployment.
Architect and operationalize LLM and generative AI platforms, including RAG pipelines, vector databases, evaluation frameworks, and safety guardrails.
Build internal AI agent frameworks using LangChain, Semantic Kernel, AutoGen, or custom orchestration engines.
Oversee model monitoring, drift detection, model auditing, compliance, and ML observability tools (MLflow, Kubeflow, Feast, BentoML, etc.).
Partner with data science and product teams to accelerate ML lifecycle maturity and improve model delivery velocity.
Lead endtoend cloud transformation initiatives, ensuring alignment with organizational strategy, security, and costoptimization goals.
Provide technical leadership for hybrid/multicloud architectures, including VPC design, networking, IAM policies, and disaster recovery planning.
Establish cloud governance frameworks, standards, and best practices for infrastructure modernization.
Possess in-depth knowledge of Azure and GCP troubleshooting methodologies, including:
Deep diagnostic methodologies
Clusterlevel and nodelevel performance analysis
Distributed system debugging
Root-cause analysis across infra, networking, app, and data layers
Lead the design and optimization of CI/CD ecosystems using Azure DevOps, GitHub Actions, Jenkins, GitLab, or similar.
Drive organization-wide adoption of Infrastructure as Code, implementing reusable Terraform modules, Helm charts, and Kubernetes blueprints.
Establish DevOps standards for release governance, deployment automation, environment provisioning, and platform stability.
Implement automation at scale using Python, Bash, PowerShell, and Groovy DSL.
Architect and manage complex Kubernetes environments, including multi-cluster, multi-region, and hybrid setups.
Oversee workload security, cluster governance, autoscaling policies, service mesh strategies, ingress/East-West traffic control, and runtime optimizations.
Mentor teams in container best practices, image security, and enterprise-grade Helm/K8s deployments.
Preferred Skills
MustHave Technical Skills
- Azure DevOps (ADO), GitHub Actions, Bitbucket, Azure Repos
- Helm, Kubernetes, Docker, and enterprisegrade containerization practices
- Terraform and Infrastructure as Code principles
- Artifactory or similar artifact management solutions
- Strong Python and Groovy scripting experience for automation and troubleshooting
- Handson troubleshooting skills across Azure / AWS / GCP, including advanced diagnostics and cloud-native service debugging
- Expertise in scalable resource deployment, best practices, cost optimization, compliance, and operational governance
