MLOps Engineer

About the Role :

We’re looking for a strategic Senior MLOps Engineer to lead the end-to-end design, implementation, and scaling of our AI infrastructure. You’ll partner with researchers, product teams, and DevOps to turn prototypes into production services that meet strict SLAs for latency, reliability, and cost efficiency.

Responsibilities :

• Core MLOps Pipelines : Design and implement scalable ML pipelines (training, evaluation, deployment) for LLMs, CV, and multimodal models .
• Model Serving & CI / CD : Lead efforts in model serving, versioning, automated CI / CD, and real-time monitoring of AI workflows .
• Inference-as-a-Service : Build and optimize GPU-backed serving infrastructure targeting p99 latency 80% GPU utilization .
• Governance & Drift Detection : Drive initiatives on model governance, automated drift detection (≤10% false positives), and data-management best practices .
• Vector Search & Agent Orchestration : Integrate vector databases (Qdrant, Pinecone) for low-latency semantic retrieval, and build agentic workflows using LangChain or similar frameworks.
• Enterprise Multi-Tenancy : Architect RBAC-driven, isolated ML services to securely serve 100–500+ organizations.

Observability & Logging : Design Prometheus / Grafana dashboards, ELK / Fluentd logging pipelines, and alerting for all ML workloads.

80% utilization.

Requirements :

Familiarity with distributed training, workload schedulers, and GPU-cluster orchestration

Plus These Critical Skills :

Minimum Qualification : Bachelor's or Master's Degree in Computer Science, Engineering, or a related field.

Preferred :