- Workplace
- Remote
- Type
- Full-time
- Department
- Engineering
- Education
- PhD
- Source
- RecruiterFlow
Description
-
Design and Deploy LLM Systems: Develop scalable, production-ready LLM applications using frameworks like LangChain/LangGraph. Build robust RAG pipelines and integrate knowledge graphs for biological and clinical data.
-
Full-Stack AI Engineering: Write maintainable, high-performance code and build clean APIs and services for machine learning applications.
-
Data Engineering Collaboration: Work with data engineers to build and optimize data workflows and pipelines for high-quality data ingestion and processing.
-
Product-Focused Prototyping: Collaborate with product and domain teams to rapidly prototype AI solutions, iterate based on feedback, and scale models for production.
-
Model Deployment & MLOps: Use modern MLOps tools to deploy and monitor models in production environments (AWS preferred). Ensure scalability, observability, and resilience.
-
Collaborative Innovation: Partner with engineering, data, and business teams to identify and develop high-value AI/ML applications.
-
Continuous Learning: Stay ahead of the curve on emerging ML frameworks, GenAI capabilities, and healthcare technologies.
-
Education: Bachelor's, Master’s, or Ph.D. in Computer Science, Data Science, Engineering, or a related field.
-
Hands-on AI Experience: Proven ability to build, train, and deploy ML and NLP models, especially those powered by LLMs and transformer architectures.
-
LLM & LangChain Experience: Practical experience working with frameworks like LangChain for applications such as Q&A systems, chatbots, or document automation.
-
Software Engineering: Strong coding skills in Python and experience using Git/GitHub and CI/CD practices.
-
Data Engineering Know-how: Comfort working with ETL pipelines, relational and non-relational databases, and data platforms like Snowflake or Databricks.
-
Big Data & ML Frameworks: Familiarity with Big Data tools (e.g., Apache Spark) and experience orchestrating data workflows using tools like Apache Airflow.
-
Cloud & MLOps: Experience with deploying ML models in cloud environments (AWS, GCP, or Azure) and using containerization/orchestration tools like Docker and Kubernetes.