Job Description

Duties and Responsibilities:

• Provides deep technical expertise in the aspects of cloud infrastructure design and API development for the business environments.
• Bridges the gap between data scientists and software engineers, enabling the efficient and reliable delivery of ML - powered solutions
• Ensures solutions are well designed with maintainability/ease of integration and testing across multiple platforms.
• Possess strong proficiency in development and testing practices common to the industry

Summary of Principal Job Responsibility & Specific Job Duties and Responsibilities:

• Working closely with data scientists, ML engineers, and other stakeholders to deploy ML models
• Setting up and maintaining cloud and edge infrastructure for MIL models deployment
• Design, implement and maintain scalable infrastructure for ML workloads
• Good verbal and written communication skills
• Collaborative and oriented

Academic Qualification (s):
Bachelor's degree in computer science, Engineering or related subject and/or equivalent formal training or work experience
Work Experience / Skills Requirement(s):
1. Cloud Infrastructure & Kubernetes

• Minimum 2 years of hands-on experience managing cloud infrastructure (e.g. AWS,GCP,Azure) in a production environment
• Hands-on experience with Kubernetes for container orchestration, scaling and deployment of ML services
• Familiar with Helm charts, ConfigMaps, Secret and autoscaling strategies

2. API Development & Messaging Integration

• Proficient in building and maintaining RESTful or gRPC APIs for ML inference and data services
• Experience in message queue integration such as RabbitMQ or ZeroMQ for asyncronous communication, job queuing or real-time model inference pipelines

3. System Design, Database & Software Architecture

• Proven experience working with relational databases (RDBMS) such as Microsoft SQL Server and PostgreSQL.
• Proficient in schema design, writing complex queries, stored procedures, indexing strategies, and query optimization.
• Hands-on experience with vector search and embedding-based retrieval systems.
• Practical knowledge using FAISS, LanceDB, or Qdrant for building similarity search or semantic search pipelines.
• Understanding of vector indexing strategies (e.g., HNSW, IVF), embedding dimensionality management, and integration with model inference pipelines.

4. Programming Languages

• Demonstrated expertise in building scalable and maintainable API services using Python frameworks such as Flask, FastAPI, or Litestar.
• Fluent in HTML, CSS, and JavaScript for building simple web-based dashboards and monitoring interfaces.
• Experience with Go, C++, or Rust is a strong plus, especially for performance-critical or low-latency inference applications.

5. Edge AI Deployment

• Experience in integrating models using NCNN, MNN, or ONNX Runtime Mobile on mobile and edge devices.
• Familiarity with quantization, model optimization, and mobile inference profiling tools.

6. MLOps & Tooling

• Experience with Docker/Podman, CI/CD pipelines, Git, and ML lifecycle tools such as MLflow, Airflow, or Kubeflow.
• Exposure to model versioning, A/B testing, and automated re-training workflows.

7. Monitoring & Logging

• Ability to set up monitoring (e.g., Prometheus, Grafana) and logging (e.g., ELK stack, Loki) to track model performance and system health.

8. Soft Skills & Collaboration

• Strong analytical and troubleshooting skills.
• Able to work closely with data scientists, backend engineers, and DevOps to deploy and maintain reliable ML systems.
• Excellent communication and documentation habits.

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