Job Description

Model Development Deployment
Design, develop, and deploy MLDL models classification, regression, forecasting, recommendation, anomaly detection using Python and frameworks like scikit-learn, TensorFlow, PyTorch.
Build and maintain end-to-end ML pipelines: data preprocessing, feature engineering, training, evaluation, and serving.
Generative AI LLM Integration
Develop GenAI applications using LLMs, RAG pipelines, vector databases, and frameworks like LangChain, LlamaIndex, or LangGraph.
Fine-tune open-source LLMs e.g., Llama, Mistral for domain-specific tasks implement human-in-the-loop workflows for quality and bias control.
MLOps Productionization
Containerize models Docker and orchestrate deployments Kubernetes implement CICD for ML workflows.
Set up monitoring for model drift, data quality, latency, and business impact use tools like MLflow for experiment tracking.
Collaboration Communication
Work with cross-functional teams data engineers, domain experts, product owners to translate business requirements into AI solutions.
Document architectures, training procedures, and performance benchmarks present findings to technical and non-technical stakeholders.