Gainwell Hiring LLM & ML Ops Engineer | Finsplitz

Introduction

Are you a pioneering engineer passionate about bringing cutting-edge machine learning models, especially Large Language Models (LLMs), from research to robust production environments? Gainwell Technologies, a leading provider of technology solutions and services for the healthcare and human services industries, is seeking talented LLM & ML Ops Engineers to join its innovative data science and engineering teams. This is a unique opportunity to build and manage scalable ML pipelines, integrate powerful LLMs into real-world applications, and contribute to solutions that enhance public health and human services. If you thrive on operationalizing AI and ensuring the reliability of intelligent systems, Gainwell offers a challenging and impactful career.

Roles and Responsibilities

As an LLM & ML Ops Engineer at Gainwell, you will play a critical role in the end-to-end lifecycle of machine learning models, with a special focus on Large Language Models. Your responsibilities may include:

Designing, building, and maintaining scalable and automated ML pipelines for model training, testing, deployment, and monitoring.
Implementing MLOps best practices for versioning, reproducibility, lineage tracking, and continuous integration/continuous delivery (CI/CD) for ML models.
Deploying and managing Large Language Models (LLMs) in production environments, ensuring their performance, reliability, and cost-effectiveness.
Developing tools and frameworks to facilitate the fine-tuning, customization, and integration of LLMs into various applications.
Monitoring the performance, drift, and bias of deployed ML models and LLMs, setting up alerts for anomalies.
Collaborating closely with data scientists to transition models from experimentation to production.
Ensuring data quality and managing feature stores for ML models.
Troubleshooting and resolving issues related to ML model serving, infrastructure, and data pipelines.
Researching and evaluating new MLOps tools, platforms, and LLM technologies.
Ensuring compliance with data privacy regulations and security standards for ML systems.

Salary and Benefits

Gainwell Technologies offers a competitive salary and comprehensive benefits package for LLM & ML Ops Engineers, reflecting the specialized and high-demand nature of this role within the healthcare technology sector. While specifics can vary by experience and role level, typical offerings in India include:

Competitive base salary.
Performance-based bonuses or incentives.
Comprehensive health, life, and accident insurance coverage.
Provident Fund (PF) and Gratuity benefits as per Indian regulations.
Paid time off, including holidays and vacation.
Opportunities for extensive professional development, including certifications in cloud AI/ML services and MLOps platforms.
Employee assistance programs and wellness initiatives.
A collaborative and mission-driven work environment with exposure to cutting-edge AI technologies in healthcare.
Potential for significant career growth in a rapidly evolving field.

Application Process

Ready to operationalize advanced AI for better healthcare? Here’s how to apply for an LLM & ML Ops Engineer position at Gainwell:

Online Application: Visit the Gainwell Technologies Careers website and search for “LLM Engineer,” “ML Ops Engineer,” “Machine Learning Engineer,” or similar relevant titles.
Specialized Resume/CV: Prepare a detailed resume emphasizing your experience with MLOps platforms (e.g., Kubeflow, MLflow, AWS Sagemaker, Azure ML, GCP Vertex AI), cloud technologies, Docker, Kubernetes, scripting languages (Python, Go), and experience with LLMs (e.g., fine-tuning, prompt engineering, integration of models like OpenAI, LLaMA, BERT). Highlight any relevant projects, academic research, or contributions to open-source ML.
Technical Assessments (if applicable): You may be asked to complete online assessments testing your coding skills, understanding of ML concepts, or practical MLOps scenarios.
Interview Scheduling: Successful candidates will be invited for interview rounds.

Interview Process

The interview process for an LLM & ML Ops Engineer at Gainwell will be comprehensive, designed to assess your technical depth in both machine learning and operational best practices. It typically includes:

HR Screen: An initial discussion about your background, career aspirations, and cultural fit within Gainwell’s mission.
Technical Phone Screen(s): These rounds will likely cover fundamental ML concepts, Python programming, cloud basics, and questions related to MLOps principles.
Onsite/Virtual Technical Interviews (multiple rounds, often 4-6): Expect several in-depth technical discussions and problem-solving sessions:
- Machine Learning Fundamentals: Deep dive into common ML algorithms, model evaluation metrics, and challenges in real-world ML applications.
- LLM Specifics: Questions on transformer architectures, fine-tuning techniques, prompt engineering, RAG (Retrieval-Augmented Generation), and use cases of LLMs.
- MLOps & Productionization: Discussions on CI/CD for ML, model monitoring (drift, bias), feature stores, model versioning, and A/B testing of ML models.
- Cloud & Infrastructure: Your experience with relevant cloud services (e.g., compute, storage, serverless, managed ML services) and containerization/orchestration (Docker, Kubernetes).
- Programming/Scripting: Practical coding challenges in Python, often involving data manipulation, ML model implementation, or automation scripts.
- System Design: Designing scalable and reliable ML infrastructure or an LLM-powered application.
- Troubleshooting/Debugging: Scenarios involving issues with deployed ML models or pipelines.
- Behavioral Questions: Assessing your teamwork, communication, and approach to problem-solving and operational challenges in an ML context.

Conclusion

An LLM & ML Ops Engineer role at Gainwell Technologies offers an exciting frontier for those eager to bridge the gap between AI innovation and real-world impact in healthcare. If you are a skilled engineer with expertise in MLOps and a passion for Large Language Models, this is an exceptional opportunity to contribute to transformative solutions. Apply today and help operationalize the future of intelligent systems for better human services!