Senior AI ML Engineer - GenAI LLM Data Science and MLOps
Overview
We're seeking a Senior AI/ML Engineer with 6-7 years of experience to lead the design and deployment of scalable AI solutions. This on-site role focuses on Generative AI, Large Language Models (LLMs), Data Science, and MLOps — building production-ready systems that power real-world products. You'll collaborate with cross-functional teams, mentor junior engineers, and shape our AI strategy.
What You'll Do5
- 1Architect and implement end-to-end AI/ML pipelines that scale across production environments.
- 2Drive the development of GenAI applications, including chatbots, intelligent automation, and RAG-powered retrieval systems.
- 3Own the full model lifecycle: from training and fine-tuning to deployment, monitoring, and continuous optimization.
- 4Build and maintain MLOps workflows — managing CI/CD, model versioning, and automated monitoring for reliability.
- 5Integrate AI models with enterprise APIs and microservices, ensuring seamless performance and cost efficiency.
Requirements4
- 1Deep hands-on expertise with Python, PyTorch, TensorFlow, or similar deep learning frameworks.
- 2Proven experience with LLMs (e.g., OpenAI, Anthropic, Llama) and prompt engineering, fine-tuning, and evaluation.
- 3Solid understanding of NLP, embeddings, vector search, and production-grade RAG pipelines using tools like Pinecone or Weaviate.
- 4Strong MLOps skills — comfortable with Docker, Kubernetes, CI/CD, and model monitoring in cloud environments like AWS or Azure.
Who Should Apply
This role is ideal for a seasoned AI/ML engineer who thrives on building and shipping production systems. You're equally comfortable experimenting with the latest GenAI models and setting up robust MLOps infrastructure. You enjoy mentoring teammates, leading technical decisions, and staying ahead of the curve in AI research and best practices.
Salary Insight
Compensation is competitive and commensurate with experience, though a specific range is not listed.
Required Skills
Application Tip
Tailor your application to showcase a concrete project where you designed a RAG pipeline or deployed an LLM into production — include details on the tech stack, performance metrics, and how you handled model monitoring.
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