Must have experience in software development, data science and ML, with at least 3+ years in AI engineering roles.
Proven experience in end-to-end ML lifecycle: data wrangling, model development, deployment, and monitoring.
Strong programming skills in Python with Solid knowledge of AI/ML, including LLMs and data science libraries like pandas, scikit-learn, TensorFlow/PyTorch, etc.
Experience with LLM Orchestration frameworks like Langchain, LangGraph, vLLM, LMDeploy.
Strong knowledge in NoSQL databases (any experience in Graph database is desirable)
Experience with MLOps tools: MLflow, Airflow, Kubeflow, or similar.
Familiarity with either of cloud platforms (GCP, AWS) for AI Solutioning and ML deployment.
Knowledge of data science techniques including supervised/unsupervised learning, NLP, time series, etc.
Experience with CI/CD pipelines and containerization (Docker, Kubernetes).
Strong understanding of AI governance, model risk management, and regulatory requirements in AI.
