Project overview
About the Role This position is for a Data Scientist with a focus on developing and deploying AI-powered solutions. The role is remote and is a 1-year contract requiring 4-7 years of experience. Key skills include Agentic AI, LLM, and MLOps. Key Responsibilities 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. Qualifications/Requirements 4-7 years of experience in data science or related fields. Proficiency in Agentic AI, LLM, and MLOps. Experience with Microsoft Azure’s AI stack and cloud-native AI solutions. Strong collaboration and mentorship skills. Expectations & Impact 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.