Sutherland

Company Description

Sutherland is at the forefront of AI-driven innovation, specializing in Generative AI (GenAI) and Large Language Models (LLMs). We build intelligent applications that transform industries by leveraging cutting-edge AI technologies. Join us to create tools that redefine developer productivity.

Job Description

Role Overview

We are seeking a Software Engineer with MLOps skills to contribute to the deployment, automation, and monitoring of GenAI and LLM-based applications. You will work closely with AI researchers, data engineers, and DevOps teams to ensure seamless integration, scalability, and reliability of AI systems in production.

Key Responsibilities

1. Deployment & Integration

• Assist in deploying and optimizing GenAI/LLM models on cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI).
• Integrate AI models with APIs, microservices, and enterprise applications for real-time use cases.

2. MLOps Pipeline Development

• Contribute to building CI/CD pipelines for automated model training, evaluation, and deployment using tools like MLflow, Kubeflow, or TFX.
• Implement model versioning, A/B testing, and rollback strategies.

3. Automation & Monitoring

• Help automate model retraining, drift detection, and pipeline orchestration (Airflow, Prefect).
• Assist in designing monitoring dashboards for model performance, data quality, and system health (Prometheus, Grafana).

4. Data Engineering Collaboration

• Work with data engineers to preprocess and transform unstructured data (text, images) for LLM training/fine-tuning.
• Support the maintenance of efficient data storage and retrieval systems (vector databases like Pinecone, Milvus).

5. Security & Compliance

• Follow security best practices for MLOps workflows (model encryption, access controls).
• Ensure compliance with data privacy regulations (GDPR, CCPA) and ethical AI standards.

6. Collaboration & Best Practices

• Collaborate with cross-functional teams (AI researchers, DevOps, product) to align technical roadmaps.
• Document MLOps processes and contribute to reusable templates.

Qualifications

Technical Skills

• Languages: Proficiency in Python and familiarity with SQL/Bash.
• ML Frameworks: Basic knowledge of PyTorch/TensorFlow, Hugging Face Transformers, or LangChain.
• Cloud Platforms: Experience with AWS, Azure, or GCP (e.g., SageMaker, Vertex AI).
• MLOps Tools: Exposure to Docker, Kubernetes, MLflow, or Airflow.
• Monitoring: Familiarity with logging/monitoring tools (Prometheus, Grafana).

Experience

• 2+ years of software engineering experience with exposure to MLOps/DevOps.
• Hands-on experience deploying or maintaining AI/ML models in production.
• Understanding of CI/CD pipelines and infrastructure as code (IaC) principles.

Education

• Bachelor’s degree in Computer Science, Data Science, or related field.

Preferred Qualifications

• Familiarity with LLM deployment (e.g., GPT, Claude, Llama) or RAG systems.
• Knowledge of model optimization techniques (quantization, LoRA).
• Certifications in cloud platforms (AWS/Azure/GCP) or Kubernetes.
• Contributions to open-source MLOps projects.

Additional Information