DevOps Engineer

Job Title:

DevOps/MLOps Expert

Location:

Gurugram (On-Site)

Employment Type:

Full-Time (Immediate Joiner)

Experience:

6 + years

Qualification:

B.Tech CSE

About the Role

We are seeking a highly skilled DevOps/MLOps Expert to join our rapidly growing AI-based startup building and deploying cutting-edge enterprise AI/ML solutions. This is a critical role that will shape our infrastructure, deployment pipelines, and scale our ML operations to serve large-scale enterprise clients.

As our DevOps/MLOps Expert, you will be responsible for bridging the gap between our AI/ML development teams and production systems, ensuring seamless deployment, monitoring, and scaling of our ML-powered enterprise applications. You'll work at the intersection of DevOps, Machine Learning, and Data Engineering in a fast-paced startup environment with enterprise-grade requirements.

Key Responsibilities

MLOps & Model Deployment

• Design, implement, and maintain end-to-end ML pipelines from model development to production deployment

• Build automated CI/CD pipelines specifically for ML models using tools like MLflow, Kubeflow, and custom solutions

• Implement model versioning, experiment tracking, and model registry systems

• Monitor model performance, detect drift, and implement automated retraining pipelines

• Manage feature stores and data pipelines for real-time and batch inference

• Build scalable ML infrastructure for high-volume data processing and analytics

Enterprise Cloud Infrastructure & DevOps

• Architect and manage cloud-native infrastructure with focus on scalability, security, and compliance

• Implement Infrastructure as Code (IaC) using Terraform, CloudFormation, or Pulumi

• Design and maintain Kubernetes clusters for containerized ML workloads

• Build and optimize Docker containers for ML applications and microservices

• Implement comprehensive monitoring, logging, and alerting systems

• Manage secrets, security, and enterprise compliance requirements

Data Engineering & Real-time Processing

• Build and maintain large-scale data pipelines using Apache Airflow, Prefect, or similar tools

• Implement real-time data processing and streaming architectures

• Design data storage solutions for structured and unstructured data at scale

• Implement data validation, quality checks, and lineage tracking

• Manage data security, privacy, and enterprise compliance requirements

• Optimize data processing for performance and cost efficiency

Enterprise Platform Operations

• Ensure high availability (99.9%+) and performance of enterprise-grade platforms

• Implement auto-scaling solutions for variable ML workloads

• Manage multi-tenant architecture and data isolation

• Optimize resource utilization and cost management across environments

• Implement disaster recovery and backup strategies

• Build 24x7 monitoring and alerting systems for mission-critical applications

Required Qualifications

Experience & Education

• 4-8 years of experience in DevOps/MLOps with at least 2+ years focused on enterprise ML systems

• Bachelor's/Master's degree in Computer Science, Engineering, or related technical field

• Proven experience with enterprise-grade platforms or large-scale SaaS applications

• Experience with high-compliance environments and enterprise security requirements

• Strong background in data-intensive applications and real-time processing systems

Technical Skills

Core MLOps Technologies

• ML Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost

• MLOps Tools: MLflow, Kubeflow, Metaflow, DVC, Weights & Biases

• Model Serving: TensorFlow Serving, PyTorch TorchServe, Seldon Core, KFServing

• Experiment Tracking: MLflow, , Weights & Biases, Comet

DevOps & Cloud Technologies

• Cloud Platforms: AWS, Azure, or GCP with relevant certifications

• Containerization: Docker, Kubernetes (CKA/CKAD preferred)

• CI/CD: Jenkins, GitLab CI, GitHub Actions, CircleCI

• IaC: Terraform, CloudFormation, Pulumi, Ansible

• Monitoring: Prometheus, Grafana, ELK Stack, Datadog, New Relic

Programming & Scripting

• Python (advanced) - primary language for ML operations and automation

• Bash/Shell scripting for automation and system administration

• YAML/JSON for configuration management and APIs

• SQL for data operations and analytics

• Basic understanding of Go or Java (advantage)

Data Technologies

• Data Pipeline Tools: Apache Airflow, Prefect, Dagster, Apache NiFi

• Streaming & Real-time: Apache Kafka, Apache Spark, Apache Flink, Redis

• Databases: PostgreSQL, MongoDB, Elasticsearch, ClickHouse

• Data Warehousing: Snowflake, BigQuery, Redshift, Databricks

• Data Versioning: DVC, LakeFS, Pachyderm

Preferred Qualifications

Advanced Technical Skills

• Enterprise Security: Experience with enterprise security frameworks, compliance (SOC2, ISO27001)

• High-scale Processing: Experience with petabyte-scale data processing and real-time analytics

• Performance Optimization: Advanced system optimization, distributed computing, caching strategies

• API Development: REST/GraphQL APIs, microservices architecture, API gateways

Enterprise & Domain Experience

• Previous experience with enterprise clients or B2B SaaS platforms

• Experience with compliance-heavy industries (finance, healthcare, government)

• Understanding of data privacy regulations (GDPR, SOX, HIPAA)

• Experience with multi-tenant enterprise architectures

Leadership & Collaboration

• Experience mentoring junior engineers and technical team leadership

• Strong collaboration with data science teams, product managers, and enterprise clients

• Experience with agile methodologies and enterprise project management

• Understanding of business metrics, SLAs, and enterprise ROI

Growth Opportunities

• Career Path: Clear progression to Lead DevOps Engineer or Head of Infrastructure

• Technical Growth: Work with cutting-edge enterprise AI/ML technologies

• Leadership: Opportunity to build and lead the DevOps/Infrastructure team

• Industry Exposure: Work with Government & MNCs enterprise clients and cutting-edge technology stacks

Success Metrics & KPIs

Technical KPIs

• System Uptime: Maintain 99.9%+ availability for enterprise clients

• Deployment Frequency: Enable daily deployments with zero downtime

• Performance: Ensure optimal response times and system performance

• Cost Optimization: Achieve 20-30% annual infrastructure cost reduction

• Security: Zero security incidents and full compliance adherence

Business Impact

• Time to Market: Reduce deployment cycles and improve development velocity

• Client Satisfaction: Maintain 95%+ enterprise client satisfaction scores

• Team Productivity: Improve engineering team efficiency by 40%+

• Scalability: Support rapid client base growth without infrastructure constraints

Why Join Us

• Be part of a forward-thinking, innovation-driven company with a strong engineering culture.
• Influence high-impact architectural decisions that shape mission-critical systems.
• Work with cutting-edge technologies and a passionate team of professionals.
• Competitive compensation, flexible working environment, and continuous learning opportunities.

How to Apply

Please submit your resume and a cover letter outlining your relevant experience and how you can contribute to Aaizel Tech Labs' success. Send your application to , or