Description

Key Responsibilities

• Build and enhance AI/ML and GenAI-powered solutions using Python, LLMs, RAG, prompt engineering, and agentic AI frameworks
• Develop models for NLP, classification, clustering, anomaly detection, and predictive analytics
• Support model lifecycle activities: experimentation, evaluation, versioning, deployment, monitoring, and documentation
• Contribute to cloud-native ML pipelines and AI application deployment using MLOps, APIs, Docker, and Kubernetes
• Collaborate cross-functionally with Security, Risk, Engineering, Governance, and Data teams
• Stay current with emerging trends in Machine Learning, Generative AI, Agentic AI, and MLOps

Required Skills

• Strong Python programming with frameworks such as PyTorch, TensorFlow, or scikit-learn
• Hands-on experience with LLMs, NLP, embeddings, RAG, and prompt engineering
• Experience with agentic AI frameworks: LangChain, LangGraph, AutoGen, or CrewAI
• Exposure to APIs, cloud platforms (AWS, Azure, GCP), Docker, Kubernetes, and vector databases
• MLOps/data tools: MLflow, Kubeflow, Argo Workflows, Kafka, Spark, or NiFi

Preferred Skills

• Flask, FastAPI, Hugging Face, Triton Inference Server
• pgVector, Milvus, Prometheus, Elasticsearch, Kibana
• CI/CD: Jenkins, GitLab CI, XL Release

Key Skills

Education

Bachelor's degree