Machine Learning Engineer

What You Need To Succeed

• Master’s 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.
• Excellent problem-solving skills and a track record of innovation.
• Passion for learning and applying the latest technological advancements.
  

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.