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Senior MLOps Engineer
PairSoftSenior MLOps Engineer at PairSoft designing, building, and operating ML infrastructure solutions. Ensuring reliability, scalability, and security of ML systems across the full lifecycle and mentoring junior engineers.
Tech Stack
Tools & technologiesAzureDockerPythonTerraform
About the role
Key responsibilities & impact- Design, implement, and maintain end-to-end ML pipelines covering training, validation, deployment, monitoring, and retraining — with a focus on production reliability and long-term maintainability.
- Own and operate production ML infrastructure using Infrastructure as Code (IaC), making architectural tradeoffs and enforcing best practices.
- Lead CI/CD practices for ML, including artifact/model versioning, promotion, rollout/rollback, and dev/test/prod parity.
- Deploy and run ML/GenAI workloads on Azure using Azure App Service and Azure Container Apps, with monitoring via Application Insights.
- Implement robust model observability: performance monitoring, data quality checks, drift detection, alerting, and dashboards.
- Drive compute and cost optimization for training and inference (scaling policies, capacity planning, cost/performance tradeoffs).
- Support GenAI operational needs, including LLM inference patterns, embeddings, and retrieval pipelines; enable hooks for evals/guardrails where required.
- Ensure ML systems meet security and governance requirements (RBAC/least privilege, secrets management, audit logging, encryption, secure access patterns).
- Partner with the Data & AI Architect to translate architecture standards into reusable pipeline templates and operational controls.
- Collaborate with and mentor AI engineers; contribute to model development/experimentation as capacity allows.
Requirements
What you’ll need- 5+ years of experience in MLOps, ML engineering, platform engineering, or a closely related role, with at least 2 years in a senior or lead capacity.
- Strong proficiency in Python for ML workflows, automation, and pipeline development.
- Hands-on experience building and operating ML systems on Azure (OCI exposure is a plus).
- Proven track record of owning production-grade MLOps pipelines end-to-end (training → deployment → monitoring → retraining) with measurable reliability or efficiency outcomes.
- Strong experience with Infrastructure as Code (Terraform or equivalent).
- Experience with MLOps tooling such as MLflow (or equivalent experiment tracking) and CI/CD pipelines.
- Experience containerizing services using Docker in production environments.
- Hands-on experience deploying and monitoring services on Azure using Azure App Service, Azure Container Apps, and Application Insights.
- Solid understanding of GenAI/LLM-based systems (inference workflows, embeddings, retrieval/RAG components) and their operational considerations.
- Strong communication and collaboration skills; comfortable working across functions and influencing technical decisions without direct authority.
Benefits
Comp & perks- Company Paid Group Mediclaim Insurance for employee, spouse and up to 2 Kids of INR 400,000 per annum
- Company Paid Group Personal accidental insurance for employees of INR 1,000,000 per annum.
- Company Paid & Manager approved Career Advancement Opportunities
- Best in the Industry referral bonus policy.
- 29 Paid leaves throughout the year
- Company-paid Maternity leaves for female employees
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
MLOpsML engineeringplatform engineeringPythonInfrastructure as CodeTerraformMLflowDockerGenAILLM
Soft Skills
communicationcollaborationmentoringinfluencing