As a 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.

Key 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.

Technical Requirements :

- LLM & ML : Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, 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 Specialty, Azure Solutions Architect Expert

What You'll Do :

- 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.

- Present key insights and drive decision-making.

What You Need to Succeed :

- Masters degree or equivalent experience in Machine Learning.

- 3+ years of industry experience in ML, software engineering, and data engineering.

- Proficiency in Python, PyTorch, TensorFlow, and Scikit-learn.

- Strong programming skills in Python and JavaScript.

- Hands-on experience with ML Ops practices.

- Ability to work with research and product teams.