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.