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