Machine Learning Engineer II - Data Science

Job Description :

As a Machine Learning Engineer - 2 you will lead the design, implementation, optimization, and deployment of AI-powered features. You will take ownership of the full ML lifecycle from ideation to deployment and monitoring while working closely with cross-functional teams to deliver production-ready ML systems that directly impact customer outcomes.

Responsibilities :

- Design and develop AI product features aligned with customer and business needs.

- Maintain and enhance existing ML systems and infrastructure.

- Build and manage ML pipelines for experimentation, training, deployment, and monitoring.

- Conduct A/B testing and build scalable model inference APIs.

- Deploy LLM-based solutions customized to specific insurance use cases.

- Optimize GPU infrastructure, parallel training, and fine-tune foundation models.

- Implement DevOps/ML Ops best practices using Kubernetes, Docker, and orchestration frameworks.

- Collaborate with engineering, product, and research teams to design and implement scalable ML systems.

- Apply advanced ML techniques in NLP, Generative AI, Retrieval-Augmented Generation (RAG), and transformer models.

- Monitor, optimize, and maintain deployed models to ensure performance, accuracy, and reliability.

- Conduct applied research to keep up with and implement state-of-the-art advancements.

- Build robust data pipelines and model deployment infrastructure.

- Communicate findings and technical recommendations clearly to cross-functional stakeholders.

Requirements :

- Master's degree (or equivalent industry experience) in Machine Learning, Data Science, or related fields.

- 3 - 6 years of hands-on experience in Machine Learning and software/data engineering.

- Deep proficiency in Python and working experience with JavaScript.

- Strong background in NLP, LLMs, and model optimization.

- Prior exposure to ML Ops and model deployment workflows.

- Ability to work in a fast-paced environment with high accountability.

- A passion for innovation, learning, and building impactful solutions.

Technical Requirements Machine Learning & LLMs :

- Hugging Face LLMs, GPT, Gemini, Claude, Mixtral, LLaMA

- ML libraries : PyTorch, TensorFlow, Scikit-learn

LLMOps & Tools :

- MLFlow, Langchain, LangGraph, LangFlow, Langfuse

- LlamaIndex, AWS Bedrock, Azure AI, SageMaker

Cloud & Infrastructure :

- Cloud Platforms : AWS, Azure

- DevOps : Kubernetes, Docker

Databases & Storage :

- MongoDB, PostgreSQL, Pinecone, ChromaDB

Languages :

- Python, SQL, JavaScript

Bonus Certifications :

- AWS Certified Solution Architect - Professional.

- AWS Certified Machine Learning - Specialty.

- Azure Solutions Architect Expert.