AI / ML Senior Developer

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.