Responsibilities
- Design, develop, and deploy production-grade machine learning models and generative AI applications using state-of-the-art frameworks and methodologies
- Build and optimize Retrieval Augmented Generation (RAG) pipelines for enterprise knowledge systems and intelligent document processing
- Implement Model Context Protocol (MCP) and agent-to-agent (A2A) context engineering solutions for complex AI orchestration
- Develop feedback loops and monitoring systems to continuously improve model performance and ensure reliability
- Architect and maintain MLOps pipelines for model training, versioning, deployment, and monitoring
- Create AI agents using LangChain and LangGraph frameworks for autonomous decision-making and workflow automation
- Collaborate with data engineering teams to build robust data pipelines and feature engineering workflows
- Mentor junior data scientists and contribute to the development of AI best practices and standards
Basic qualifications
- 5+ years of experience in data science, machine learning, or related field
- Strong expertise in generative AI technologies including LLMs, prompt engineering, and context management
- Hands-on experience building RAG systems with vector databases and semantic search
- Proficiency with LangChain, LangGraph, and agent-based architectures
- Experience with Model Context Protocol (MCP) and A2A context engineering patterns
- Deep understanding of traditional machine learning algorithms (regression, classification, clustering, time series)
- Strong MLOps experience including CI/CD pipelines, model versioning, and monitoring frameworks
- Proficiency with Hugging Face ecosystem (Transformers, Datasets, Hub)
- Experience with TensorFlow and/or PyTorch for model development
- Strong Python programming skills with experience in production-grade code
- Experience designing and implementing feedback loops for continuous model improvement
- Knowledge of cloud platforms (AWS, Azure, or GCP) for ML deployment
- Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders
Preferred qualifications
- Experience in regulated industries (financial services, healthcare)
- Knowledge of data governance and model risk management frameworks
- Experience with distributed training and large-scale model deployment
- Familiarity with other frameworks like Anthropic's Claude API, OpenAI API
- Experience with vector databases (Pinecone, Weaviate, Chroma)
- Understanding of prompt engineering and fine-tuning techniques
- Contributions to open-source ML/AI projects
- Languages: Python, SQL
- ML Frameworks: TensorFlow, PyTorch, scikit-learn
- GenAI Tools: LangChain, LangGraph, Hugging Face
- MLOps: Docker, Kubernetes, MLflow, Weights & Biases
- Cloud: AWS/Azure/GCP machine learning services
- Data: Pandas, NumPy, vector databases Version Control: Git, CI/CD pipelines
Tags & focus areas
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