Machine Learning Engineer II - NLP/PyTorch

Position : Machine Learning Engineer

Location : Remote

Experience : 5 to 10 years

Job Descriptions :

Key Responsibilities :

- Design, develop, and maintain a multi-agentic system that powers ML-driven care pathways.

- Build and optimize Retrieval-Augmented Generation (RAG) pipelines to ensure low-latency, high-accuracy, and contextually relevant outputs.

- Develop and implement scalable, safe, and predictable ML solutions that align with goal-seeking pathways.

- Collaborate with data scientists, engineers, and product teams to integrate proprietary ML algorithms and LLMs into production systems.

- Solve complex challenges related to real-time ML applications.

- Monitor and improve the performance of ML models in production, ensuring reliability and robustness.

- Explore and evaluate new tools, frameworks, and ecosystems to enhance team ML capabilities, including potential expansion beyond Azure.

- Implement best practices for ML model lifecycle management, including versioning, retraining, and deployment.

- Ensure compliance with data privacy and security standards in all ML workflows.

Qualifications :

- 5+ years of experience in machine learning engineering or a related field, with a focus on building production-grade ML systems.

- Strong understanding of multi-agent systems and experience designing scalable, distributed architectures.

- Hands on experience with integrating LLMs into production workflows.

- Proficiency in Python and frameworks such as LangGraph, Google ADK, etc.

- Experience with cloud platforms, preferably Azure, and familiarity with cloud-native ML tools and services.

- Proven ability to solve complex problems related to latency, accuracy, and safety in ML systems.

- Strong knowledge of data structures, algorithms, and software engineering best practices.

- Familiarity with MLOps practices, including model versioning, monitoring, and retraining.

- Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.

- A passion for healthcare innovation and a commitment to building solutions that improve patient outcomes.

- Proven ability to navigate and thrive in ambiguous problem spaces, particularly in designing and optimizing ML systems where requirements and data may evolve.

Must Have :

- 5+ years of experience in machine learning engineering or a related field, with a focus on building production-grade ML systems.

- Ability to build and optimize Retrieval-Augmented Generation (RAG) pipelines to ensure low-latency, high-accuracy, and contextually relevant outputs.

- Strong understanding of multi-agent systems and experience designing scalable, distributed architectures.

- Hands-on experience with integrating LLMs into production workflows.

- Proficiency in Python and frameworks such as LangGraph, Google ADK, etc.

- Experience with cloud platforms, preferably Azure, and familiarity with cloud-native ML tools and services.

- Proven ability to solve complex problems related to latency, accuracy, and safety in ML systems.

- Strong knowledge of data structures, algorithms, and software engineering best practices.

- Familiarity with MLOps practices, including model versioning, monitoring, and retraining.

- Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.