- Location: Bengaluru / Hyderabad / NCR (Hybrid / Remote Flexibility)
- Employment Type: Full-Time
- Department: Core ML Infrastructure &
- MLOps
- Experience Range: 3–6 Years

About The Company &

- Engineering Culture

Building high-performing machine learning models is only half the battle; reliably training, versioning, monitoring, and serving them at production scale is where true engineering excellence lies. As a high-growth AI and SaaS enterprise, our platform orchestrates complex machine learning pipelines that handle massive throughput, real-time feature transformations, and continuous model inference across multi-tenant environments. Our MLOps engineering culture focuses on immutable artifact versioning, zero-downtime model deployments, automated data drift monitoring, and optimized GPU/CPU infrastructure performance. We bridge the gap between data scientists and production software engineers. If you are passionate about building robust, scalable infrastructure that powers enterprise AI at scale, this is your arena.

Position Overview

We are looking for a hands-on, detail-oriented Senior Machine Learning Engineer (MLOps) to design, build, and scale our enterprise MLOps platforms, feature stores, and automated model deployment pipelines. In this role, you will own the technical execution of taking machine learning models from experimental Jupyter notebooks into robust, low-latency, monitored production microservices.

You will work closely with applied ML scientists, data engineers, and cloud infrastructure architects to ensure our AI systems maintain high availability, reproducibility, and optimal inference performance.

Key Responsibilities &

- Technical Ownership1. MLOps Pipeline Automation &
- CI/CD

- ML Pipelines: Design, build, and maintain end-to-end ML CI/CD pipelines for automated data ingestion, feature extraction,



model training, and containerized deployment using tools like MLflow, Kubeflow, or Airflow.
- Model Registry Governance: Implement secure model registries to track model lineage, hyperparameters, evaluation metrics, and artifact versions with strict auditability.
- Automated Testing: Set up automated regression and performance testing gates for models prior to production promotion.
- Feature Store &
- Real-Time Serving Architecture
- Feature Stores: Implement and manage centralized feature stores (Feast or custom Redis/Cassandra implementations) to serve consistent features for both batch training and real-time inference.
- Inference Serving: Deploy and scale high-throughput model serving infrastructure using NVIDIA Triton Inference Server, vLLM, BentoML, or FastAPI on Kubernetes clusters.
- Model Observability: Build monitoring frameworks to track data drift, concept drift, feature distribution changes, and production inference latency in real time.
- Infrastructure Optimization &
- Collaboration
- Compute Optimization: Optimize container resource allocation, GPU/CPU utilization, and auto-scaling configurations for training and inference workloads.
- Cross-Functional Enablement: Partner with data scientists to standardize model packaging formats, containerization, and API specifications.

Comprehensive Tech Stack &

- Technical RequirementsCore Technical Stack

- Languages &
- Scripting: Advanced Python, Bash scripting, and SQL.




- MLOps &
- Orchestration Tools: MLflow, Apache Airflow, Feast, BentoML, Ray.
- Model Serving &
- Acceleration: NVIDIA Triton, vLLM, ONNX Runtime, Docker, Kubernetes.
- Databases &
- Vector Stores: PostgreSQL, Redis, Qdrant, Milvus.
- Cloud &
- Infrastructure: AWS / GCP, Terraform, Prometheus, Grafana.

Experience &

- Educational Qualifications

- Experience: 3 to 6 years of professional software engineering experience, with at least 2+ years dedicated to MLOps, machine learning infrastructure, or backend data engineering.
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Information Technology, or a related discipline from a premier institution (IITs, NITs, IISc, or top-tier universities).

Competencies &
- Behavioral Traits

- Engineering Rigor: Strong emphasis on writing clean, maintainable, and well-tested code for infrastructure and pipeline automation.
- Team-oriented Mindset: Eagerness to support data science teams by removing deployment bottlenecks and building self-serve MLOps tooling.

Professional Interview Process
- Initial Screening: Discussion covering MLOps fundamentals, model serving patterns, and past infrastructure experience.
- ML Systems Design Round: Collaborative design exercise building an automated training pipeline or a low-latency serving endpoint.
- Coding &
- Scripting Assessment: Live coding session focusing on pipeline automation scripts, containerization, or API integration for ML endpoints.
- Culture Fit: Interview with engineering leadership focusing on operational excellence, execution ownership, and cross-functional collaboration.

Skills: bash,mlops,advanced python,machine learning,feast,bentoml,ray,ml,redis,qdrant,mlflow,sql

📌 Senior Machine Learning Engineer — MLOps & Feature Store Architecture (Bengaluru)
🏢 HYrEzy Tech Solutions
📍 Bengaluru