MLOps Engineer
Operationalizing high-stakes model deployments requires the precision of a seasoned production ML engineer who can bridge the chasm between experimental data science and hardened enterprise infrastructure. For those pursuing an elite MLOps career, Sabalynx provides the architecture to manage lifecycle orchestration, automated retraining, and real-time observability at a global scale. We translate fragmented prototypes into resilient, containerized pipelines that ensure model provenance and minimize inference latency.
Hardening the AI Lifecycle
Our engineers implement the full spectrum of production ML excellence, moving beyond manual deployments to automated continuous training (CT) systems.
Feature Store Implementation
Centralizing offline and online feature management to eliminate training-serving skew and accelerate the experiment-to-production loop.
Observability & Drift Detection
Advanced monitoring of statistical parity and data drift, triggering automated retraining pipelines before performance degradation impacts business outcomes.
Industrializing Intelligence
A production ML engineer at Sabalynx does not just monitor models; they architect the very systems that allow AI to scale without human bottleneck.
The complexity of modern enterprise AI demands more than just data science. It requires Infrastructure-as-Code (IaC), robust container orchestration, and integrated security. We address the “hidden technical debt” in ML systems by applying rigorous DevOps principles to the unique lifecycle of machine learning models.
Lead MLOps Engineer
Bridge the gap between experimental research and global production environments. We are looking for an elite MLOps architect to design, scale, and maintain the industrial-grade pipelines that power Sabalynx’s enterprise AI deployments across 20+ countries.
Scaling Intelligence
At Sabalynx, MLOps is not a support function; it is the backbone of our value delivery. You will be responsible for the architectural integrity of our production AI systems, ensuring that models developed by our data science teams are deployed with the rigor of mission-critical software. You will handle high-throughput inference, complex data drift scenarios, and multi-cloud orchestration for some of the world’s most demanding organizations.
Role Impact
Your work directly influences the ROI of 200+ global AI projects, optimizing for latency, throughput, and infrastructure overhead.
Key Responsibilities
CI/CD/CT Pipeline Architecture
Design and implement robust Continuous Integration, Continuous Deployment, and Continuous Training (CT) pipelines that automate the transition from model experimentation to production-grade serving.
Scalable Serving Infrastructure
Architect and manage high-performance inference clusters using Kubernetes, ensuring low-latency response times for LLMs and deep learning models at scale.
Observability & Monitoring
Implement comprehensive monitoring for model drift, concept drift, and performance degradation. Build automated alerting systems that trigger retraining or human-in-the-loop intervention.
Data & Feature Engineering
Standardize feature stores and data versioning (using DVC or similar) to ensure reproducibility of experiments and consistent data flow across the ML lifecycle.
Infrastructure as Code (IaC)
Utilize Terraform or CloudFormation to manage ephemeral training environments and production inference stacks across AWS, Azure, and GCP.
Cost & Resource Optimization
Analyze and optimize GPU/CPU utilization to balance performance requirements with budgetary constraints, implementing spot instance strategies where appropriate.
Security & Compliance
Ensure all AI deployments meet stringent enterprise security standards (SOC2, GDPR, HIPAA), including data encryption at rest/transit and robust identity management.
Cross-Functional Collaboration
Act as the technical bridge between Lead Data Scientists and DevOps Engineers to ensure model architectures are “production-ready” from the design phase.
Technical DNA
We are seeking a practitioner with a deep understanding of the full ML stack. You should have experience deploying models that handle millions of requests daily.
- Expert-level Python & Go
- Deep Kubernetes & Docker Mastery
- TensorFlow / PyTorch / JAX
- MLflow / Kubeflow / Neptune
- DVC & Feature Store Design
- Terraform & Ansible
- Prometheus / Grafana / ELK
- NVIDIA Triton Inference Server
Nice-to-Have Skills
- Experience with Edge AI deployment (ONNX, TensorRT).
- Contributions to open-source MLOps projects.
- Deep knowledge of Vector Databases (Pinecone, Milvus, Weaviate).
- Background in High-Performance Computing (HPC).
- Cloud Solutions Architect Certifications (AWS/GCP/Azure).
What We Offer
Global Impact
Work on projects that transform Fortune 500 companies and critical infrastructure across 20+ countries.
Elite Pedigree
Collaborate with PhDs, former Big Tech leads, and industry-recognized AI practitioners.
Unrivaled Stack
Access the latest hardware and unlimited cloud budgets to solve the hardest problems in AI.
Autonomous Growth
A “results-only” work environment with flexible hours and a strong focus on technical R&D time.
Elite Compensation
Highly competitive salary, performance bonuses, and a comprehensive global benefits package.
Education Fund
Unlimited budget for certifications, conferences (NeurIPS, ICML), and advanced technical training.
Ready to Architect the Future?
If you are an engineer who thrives at the intersection of infrastructure and intelligence, we want to hear from you.
Orchestrating the Future of Intelligence
At Sabalynx, MLOps is not a supporting function; it is the backbone of our global delivery engine. We operate at the intersection of high-availability systems engineering and frontier data science, solving the “last mile” problem for some of the world’s most complex organizations.
The Production-First Paradigm
We have moved past the era of experimental notebooks. Our engineers build robust, idempotent pipelines that handle multi-petabyte datasets across hybrid-cloud architectures. Working here means designing for 99.99% reliability of inference endpoints and implementing automated retraining loops that detect and mitigate feature drift before it impacts the bottom line.
State-of-the-Art Stack
Kubernetes-native orchestration, Kubeflow, MLflow, and specialized vector databases (Pinecone, Weaviate) integrated into high-throughput RAG architectures.
Governance & Security
Implementing SOC2-compliant data lineage and model provenance for highly regulated sectors in Finance and Healthcare.
Impact at Global Scale
Joining Sabalynx means your code manages models that drive hundreds of millions of dollars in ROI. Whether it’s optimizing supply chains for Fortune 100 retailers or powering real-time diagnostic AI, your work in MLOps directly correlates with organizational transformation.
The Selection Roadmap
We seek practitioners who possess “The Sabalynx Edge”: deep technical rigor combined with a sharp commercial intuition. Our process is rigorous, transparent, and designed for engineers, by engineers.
Architecture Discovery
A 45-minute technical discussion focusing on your experience with lifecycle management. We explore how you handle model versioning, data consistency, and environment parity across the ML stack.
Systems Design Deep-Dive
A hands-on session where you design a scalable MLOps pipeline for a hypothetical $50M enterprise use case. We evaluate your choices in infrastructure as code (Terraform/Pulumi), CI/CD/CT patterns, and observability.
Operational Excellence
Understanding your approach to “day two” operations. How do you manage cold-starts, p99 latency optimization, cost-attribution on GPU clusters, and ethical AI monitoring in production?
Strategic Alignment
Final meeting with our CTO or VP of Engineering to discuss Sabalynx’s long-term roadmap, your career trajectory, and how you will contribute to our mission of global AI transformation.
Ready to Deploy MLOps Engineering?
Bridge the gap between model development and operational excellence. Our MLOps specialists implement the robust CI/CD/CT pipelines, automated monitoring, and scalable infrastructure required to sustain high-performance AI in production environments. Stop dealing with model drift and manual retraining—engineer a self-healing intelligence layer that delivers predictable business value. Invite us to evaluate your architecture in a free 45-minute discovery call where we will identify pipeline bottlenecks and define your roadmap to scale.