Data Scientist

Role : Data Scientist Location : Remote Experience : 4-7 Years Type : 1 Year Contract Skills : Agentic AI, LLM, MLOps Job Descriptions: Autonomous & Multi-Agent Systems Design, build, and deploy AI-powered autonomous agents capable of executing complex tasks and reasoning independently. Develop multi-agent orchestration frameworks using LangGraph, AutoGen, or CrewAI for enterprise-grade use cases. LLMs & Generative AI Fine-tune and optimize large language models for contextual understanding, summarization, classification, and dialogue systems. Implement retrieval-augmented generation (RAG) pipelines using vector databases and embedding strategies for grounding LLMs in enterprise data. Predictive Modeling & ML Build and deploy traditional ML models (classification, regression, time-series forecasting) to support analytical decision-making. Collaborate on data science projects with a focus on explainability, accuracy, and performance. MLOps & Deployment Own the end-to-end lifecycle of AI models, from experimentation to scalable deployment in production using MLOps best practices. Work closely with engineering to ensure CI/CD for ML, automated testing, monitoring, and retraining of models in Azure ML or Databricks. Azure AI & Cloud AI Architecture Leverage Microsoft Azure's AI stack – OpenAI, Cognitive Services, Azure ML Studio, Synapse, and containerized deployments – to build cloud-native AI solutions. Design robust and secure architectures that scale across clients and verticals. Collaboration & Mentorship Partner with solution architects, product teams, and front-end engineers to translate business requirements into technical solutions. Mentor junior data scientists and contribute to knowledge-sharing across the AI and product teams. Expectations & Impact As a Senior Data Scientist, you are expected to: Take technical ownership of AI components across projects and products. Drive experimentation and rapid prototyping to validate ideas. Push the limits of innovation by exploring new AI paradigms, especially in autonomous systems and self-improving agents. Ensure reliable and secure deployment pipelines, reinforcing AI quality and trust at scale. Balance hands-on delivery with strategic thinking to align with client's roadmap and mission