AI ML Architect

About the Role

Key Responsibilities

• Apply expertise in machine learning to real-world datasets, especially customer and transactional data.
• Develop and optimize machine learning models using tools such as Python, scikit-learn, XGBoost, LightGBM, PyTorch, and TensorFlow.
• Utilize LLM APIs (e.g., OpenAI, Hugging Face Transformers) for enterprise augmentation use cases.
• Implement embedding models (e.g., BERT, Sentence Transformers) and vector search tools like FAISS, Pinecone, and Weaviate.
• Design and manage data pipelines using SQL, MongoDB, Elastic/SOLR, and data wrangling tools such as pandas, Dask, or Spark.
• Familiarity with MLOps and deployment workflows including MLflow, Airflow, Docker, and Git-based processes.

Requirements

• 6 to 8 years of experience in applied machine learning focused on real-world datasets.
• Proficiency in Python and ML frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch, and TensorFlow.
• Working knowledge of LLM APIs like OpenAI and Hugging Face Transformers.
• Experience with embedding models and vector search tools such as FAISS, Pinecone, and Weaviate.
• Knowledge of data pipelines and data wrangling using SQL, MongoDB, Elastic/SOLR, pandas, Dask, or Spark.
• Familiarity with MLOps tools and deployment workflows including MLflow, Airflow, Docker, and Git.

Nice to Have

• Experience with prompt engineering and few-shot learning strategies for enterprise use cases.

Benefits & Perks

• Salary range of $110,000 to $200,000 per year.