Senior MLOps Engineer

Company: RecruitBots AI

Location: Remote · India

Experience: 5–8 years

Type: Full-time

Salary: ₹25 - 45 LPA

How to apply

Login and apply at https://candidate.recruitbots.ai/?utm_campaign=SME-M-R-I. More detailed instructions on onboarding.

About the role

We are looking for a Senior MLOps Engineer with 5–8 years of experience to own the infrastructure, deployment, and reliability of our AI and ML systems at scale.

You will work closely with our engineering and product teams to design, build, and operate production-grade ML/AI infrastructure, while setting technical direction and mentoring engineers on MLOps best practices.

The role offers deep ownership across model deployment, infrastructure automation, observability, and scaling of LLM and ML systems in production.

Responsibilities

- Architect and own end-to-end MLOps infrastructure for deploying, serving, and scaling ML and LLM-based systems.
- Design and maintain CI/CD pipelines for model training, evaluation, versioning, and deployment.
- Build and manage model serving infrastructure for high-availability, low-latency inference at scale.
- Containerize and orchestrate AI/ML workloads using Docker and Kubernetes.
- Design and implement monitoring, logging, and observability systems for model performance, drift, latency, and cost.
- Own cloud infrastructure (AWS or similar) for ML workloads, including provisioning, scaling, and cost optimization.
- Build infrastructure for experiment tracking, feature stores, and model registries.
- Implement robust testing, rollback, and incident-response processes for production ML systems.




- Collaborate with AI/ML engineers to productionize LLM applications, AI agents, and multimodal models.
- Define and enforce MLOps standards, security practices, and infrastructure-as-code across teams.
- Debug complex production issues across the ML pipeline, from data ingestion to model serving.
- Mentor engineers on deployment practices, infrastructure design, and operational excellence.
- Stay current with developments in MLOps, LLMOps, infrastructure tooling, and AI platform engineering.

Required Skills

- 5–8 years of experience in MLOps, DevOps, or infrastructure engineering, with significant ML/AI systems exposure.
- Strong programming skills in Python, with solid software engineering fundamentals.
- Hands-on experience with Docker and Kubernetes in production environments.
- Proven experience building and maintaining CI/CD pipelines for ML workflows.
- Strong experience with cloud platforms (AWS preferred) — compute, storage, networking, and IAM.
- Experience with infrastructure-as-code tools (Terraform, CloudFormation, or similar).
- Experience with model serving frameworks and inference optimization.
- Strong understanding of monitoring/observability stacks (Prometheus, Grafana, or similar).
- Solid understanding of ML/LLM system architecture and lifecycle.




- Excellent problem-solving and debugging skills across distributed systems.

Good to Have

- Experience deploying and scaling LLM-based applications, AI agents, or multimodal AI systems in production.
- Experience with vector databases, RAG pipelines, or embedding infrastructure.
- Familiarity with LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks.
- Experience with GPU infrastructure and cost-optimized inference (batching, quantization, autoscaling).
- Knowledge of MLflow, Kubeflow, SageMaker, or similar MLOps platforms.
- Experience with API gateways, service mesh, or microservices architecture.
- Familiarity with FastAPI, Node.js, or similar backend frameworks.
- Experience leading infrastructure design reviews or setting org-wide MLOps standards.

Education

- Bachelor’s degree in Computer Science, Information Technology, AI/ML, or a related field.
- Candidates with a strong production track record in MLOps/infrastructure for AI systems are strongly preferred.

What We’re Looking For

- Strong infrastructure and systems engineering fundamentals combined with deep interest in AI/ML platforms.
- Ability to operate across the full ML lifecycle — from data to deployment to monitoring.
- Strong ownership mindset — able to drive infrastructure decisions with minimal guidance.
- Robust problem-solving skills on complex, distributed, high-stakes systems.
- Strong communication and cross-functional collaboration skills.
- A builder mindset with a track record of operating reliable AI systems at scale.
- Hands-on production experience matters more than certifications.

📌 Senior MLOps Engineer (India)
🏢 RecruitBots AI
📍 India