Senior Specialist - Data Sciences

Must have Experience of 3-5 years for Associate Level 6-10 yrs for Senior Associate Level Bachelors or masters degree in Computer Science Data Science Engineering or a related field Experience on Agentic AIFramework and Platform Strong programming skills in languages such as Python SQLNoSQL Experience in working with OpenAI Models Experience in TensorFlow PyTorch PySpark Good experience in Azure Exposure to Gen AI Vector DBEmbeddings LLM RAG Grounding with external APIs Good to have Experience with MLOps MLFlow Kubeflow CICD Pipeline etc Experience in FlaskDjango is a bonus Knowledge of math probability and statistics Familiarity with a variety of Machine Learning NLP and deep learning algorithms Responsibilties Collaborate with software engineers business stake holders andor domain experts to translate business requirements into product features tools projects AIML NLPNLU and deep learning solutions Develop implement and deploy AIML solutions Experience in GenAI applications like chatbots summarization code generation content creation Preprocess and analyze large datasets to identify patterns trends and insights Evaluate validate and optimize AIML models to ensure their accuracy efficiency and generalizability Deploy applications and AIML model into cloud environment such as AWSAzureGCP etc model performance will be evaluated such as accuracy latency hallucination rate for LLMs Exposure to evaluation of model performance such as accuracy latency hallucination rate for LLMs Handson experience with prompt engineering for large language models LLMs such as OpenAI GPT Claude or Gemini Experience with containerization technologies like Docker orchestration platforms such as Kubernetes and optimizing machine learning model inference for performance and scalability Experience in maintaining data pipelines and ETLELT workflows using tools like Apache Airflow Azure Data Factory or Databricks

Monitor and maintain the performance of AIML models in production environments identifying opportunities for improvement and updating models as needed

Familiarity with ethical AI practices including bias detection fairness and model transparency

Document AIML model development processes results and lessons learned to facilitate knowledge sharing and continuous improvement