Senior MLOps Engineer
Edelman is a voice synonymous with trust, reimagining a future where the currency of communication is action. Our culture thrives on three promises: boldness is possibility, empathy is progress, and curiosity is momentum. At Edelman, we understand diversity, equity, inclusion and belonging (DEIB) transform our colleagues, our company, our clients, and our communities. We are in relentless pursuit of an equitable and inspiring workplace that is respectful of all, reflects and represents the world in which we live, and fosters trust, collaboration and belonging.We are currently seeking a Senior MLOps Engineer with 5+ years of relevant experience to lead the design, deployment, and optimization of scalable machine learning pipelines, focusing on Generative AI and large language models (LLMs). You will collaborate across teams to streamline workflows, ensure system reliability, and integrate the latest MLOps tools and practices.Why You'll Love Working with UsWe are at an exciting point in our journey, leveraging Generative AI (GenAI), Large Language Models (LLMs), and advanced Retrieval-Augmented Generation (RAG) techniques to build intelligent, data-driven systems that deliver powerful PR insights. You'll also work on developing agentic workflows that autonomously orchestrate tasks, enabling scalable and dynamic solutions. Our data stack is modern and efficient, designed to process large-scale information, automate analysis pipelines, and integrate seamlessly with AI-driven workflows. This is an excellent opportunity to make a significant impact on projects that push the boundaries of AI-powered insights and automation. If you're passionate about building high-performance data systems, working with cutting-edge AI frameworks, and solving complex challenges in a supportive, forward-thinking environment, you'll thrive here!
Responsibilities:
- Develop and maintain scalable MLOps pipelines for GenAI applications.
- Deploy and optimize GenAI models, including large language models (LLMs) such as GPT and similar architectures, in production environments.
- Develop solutions leveraging traditional AI techniques such as decision trees, clustering, and regression analysis to complement advanced AI workflows
- Implement and manage CI/CD pipelines for ML workflows, including testing, validation, and deployment.
- Optimize cloud infrastructure for cost-efficient training and serving of GenAI and LLM models.
- Define and enforce best practices for model versioning, reproducibility, and governance.
- Monitor and troubleshoot production systems to minimize downtime.
- Utilize Databricks to build and manage data and ML pipelines integrated with GenAI and LLM workflows.
- Evaluate and integrate state-of-the-art MLOps tools and frameworks for LLMs and other GenAI models.
- Stay updated on advancements in GenAI technologies, including LLM fine-tuning and serving, and contribute to strategic initiatives.
Qualifications:
- Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
- 5+ years of experience in MLOps, DevOps, or related roles, focusing on ML and AI.
- Proven expertise in deploying and managing Generative AI models (e.g., GPT, Stable Diffusion, BERT).
- Proficient in Python and ML libraries such as TensorFlow, PyTorch, or Hugging Face.
- Skilled in cloud platforms (AWS, GCP, Azure) and managed AI/ML services.
- Hands-on experience with Docker, Kubernetes, and container orchestration.
- Expertise with Databricks, including ML workflows and data pipeline management.
- Familiarity with tools like MLflow, DVC, Prometheus, and Grafana for versioning and monitoring.
- Experience implementing security and compliance standards for AI systems.
- Strong problem-solving and communication skills, with a collaborative mindset.
- Experience with support and guidance of junior team members
- Fluency in written and spoken English
Preferred Qualifications:
- Experience with large-scale distributed training and fine-tuning of GenAI models.
- Familiarity with prompt engineering and model optimization techniques.
- Contributions to open-source projects in the MLOps or GenAI space.
- Familiarity with PySpark for distributed data processing.
Originally posted on Himalayas
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