Key Responsibilities: Build and deploy
end-to-end ML & LLM pipelines Develop
RAG systems and multi-agent AI workflows Manage
model lifecycle
(experiment → registry → production) Containerize and deploy using
Docker & Kubernetes Implement
CI/CD pipelines
for ML/AI systems Monitor
model performance, drift, and reliability
in production Collaborate with data scientists to
productionize models Integrate AI solutions into
scalable enterprise platforms Core Tech Skills: Strong
Python
(production-grade, scalable code) MLOps:
MLflow / Weights & Biases, model registry & lifecycle LLMs & GenAI:
OpenAI / open-source models, embeddings,
RAG
, agentic workflows AI Frameworks:
LangChain / LlamaIndex (or similar orchestration tools) Data Pipelines:
Feature engineering, preprocessing, model evaluation Containers & Orchestration:
Docker, Kubernetes (K8s) CI/CD:
GitLab / Jenkins for ML pipelines Cloud & Compute:
GPU workloads, cloud ML in...