ML Engineer II

As an MLE-2, you will design, implement, and optimize AI solutions while ensuring model success. You will lead the ML lifecycle from development to deployment, collaborate with cross-functional teams, and enhance AI capabilities to drive innovation and impact.

Responsibilities:

• Design and implement AI product features.
• Maintain and optimize existing AI systems.
• Train, evaluate, deploy, and monitor ML models.
• Design ML pipelines for experiment, model, and feature management.
• Implement A/B testing and scalable model inferencing APIs.
• Optimize GPU architectures, parallel training, and fine-tune models for improved performance.
• Deploy LLM solutions tailored to specific use cases.
• Ensure DevOps and LLMOps best practices using Kubernetes, Docker, and orchestration frameworks.
• Collaborate with cross-functional teams to design and build scalable ML solutions.
• Implement state-of-the-art ML techniques, including NLP, generative AI, RAG, and transformer architectures.
• Deploy and monitor ML models for high performance and reliability.
• Innovate through research, staying ahead of industry trends.
• Build scalable data pipelines following best practices.

Requirements:

• LLM and ML: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, and Llama.
• LLMOps: MLFlow, Langchain, Langgraph, LangFlow, Langfuse, LlamaIndex, SageMaker, AWS Bedrock, Azure AI.
• Databases: MongoDB, PostgreSQL, Pinecone, ChromDB.
• Cloud: AWS, Azure.
• DevOps: Kubernetes, Docker.
• Languages: Python, SQL, JavaScript.
• Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialist, Azure Solutions Architect Expert.

Skills

LLM, ML Engineer, Machine Learning, RAG