Description

You will build and enhance AI/ML and GenAI-powered solutions using Python, LLMs, RAG, and agentic frameworks.

This role is hybrid.

Responsibilities

• Develop models for NLP, classification, clustering, anomaly detection, and predictive analytics.
• Support the full model lifecycle: experimentation, evaluation, versioning, deployment, monitoring, and documentation.
• Build cloud-native ML pipelines and AI application deployments 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 cloud platforms (AWS, Azure, GCP), Docker, Kubernetes, and vector databases.
• MLOps/data tools: MLflow, Kubeflow, Argo Workflows, Kafka, Spark, or NiFi.
• Bachelor's degree and 5+ years of experience.

Preferred Skills

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

Key Skills

Education

Bachelor's degree