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.
