Senior Data Scientist

As a Senior Data Scientist, you will lead the development of scalable GenAI-powered systems, designing intelligent workflows that leverage large language models (LLMs), vector-based retrieval, and multi-agent orchestration frameworks. Youll drive solution architecture, mentor junior engineers, and deliver production-ready applications that integrate deeply with business processes and platforms.

Key Responsibilities:

Lead the design and deployment of GenAI systems leveraging LLMs, retrieval pipelines, and orchestration frameworks for multi-step task execution
Architect and optimize prompt workflows, including chaining, templating, and context control, for high-accuracy and cost-efficient solutionsBuild and maintain embedding-based retrieval systems using vector databases and context-aware generation techniques (e.g., retrieval-augmented generation)Collaborate with product owners and engineering leads to align solution architecture with business objectivesGuide and mentor junior engineers on best practices in prompt design, token optimization, security controls, and observability patternsDefine standards for code modularity, response consistency, prompt safety, and testing across LLM-powered applicationsMaintain strong CI/CD practices using version-controlled workflows and cloud-native deployment pipelinesEvaluate emerging GenAI tooling and provide technical recommendations for experimentation and adoption

Qualifications

4+ years of experience in AI/ML solution delivery, with a strong focus on GenAI or LLM-integrated systems
Expertise in Python (v3.11+) with deep familiarity in LLM APIs, embedding generation, vector-based search, and modular pipeline designProven experience in building and deploying prompt-driven applications at scaleSolid understanding of agent orchestration patterns, multi-agent task flows, and context layering techniquesHands-on experience in cloud-native delivery (preferably Azure), including containerization, CI/CD, and monitoring

Preferred Qualifications

Exposure to model context protocols (e.g., MCP) and agent-to-agent (A2A) coordination concepts
Experience with LLM observability tools (latency tracking, relevance scoring, cost management)Contributor to internal or open-source projects that showcase applied GenAI, workflow orchestration, or prompt librariesUnderstanding of responsible AI guidelines, token-level safety, and enterprise security standards in GenAI applications