Data Scientist (R-18965)
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- open for 194 days (90+ without a fill is a strong ghost signal)
- 141 open roles at this company in 30 days (mass-hiring blitz)
- no salary disclosed (correlates with ghost postings)
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About the role
Key Responsibilities:
Agent Development & Architecture
Build agentic workflows using LangChain/LangGraph and similar frameworks.
Develop autonomous agents for data validation, reporting, document processing, and domain workflows.
Deploy scalable, resilient agent pipelines with monitoring and evaluation.
GenAI Application Engineering
Develop GenAI applications using models like GPT, Gemini, and LLaMA.
Implement RAG, vector search, prompt orchestration, and model evaluation.
Partner with data scientists to productionize POCs.
Data & Platform Engineering
Build distributed data pipelines (Python, PySpark).
Develop APIs, SDKs, and integration layers for AI-powered applications.
Optimize systems for performance and scalability across cloud/hybrid environments.
MLOps / LLMOps
Contribute to CI/CD workflows for AI models—deployment, testing, monitoring.
Implement governance, guardrails, and reusable GenAI frameworks.
Collaboration & Stakeholder Engagement
Work with analytics, product, and engineering teams to define and deliver AI solutions.
Participate in architecture reviews and iterative development cycles.
Support knowledge sharing and internal GenAI capability building.
Key Skills & Requirements:
8–12 years of experience in AI/ML engineering, data science, or software engineering, with at least 4 years focused on GenAI.
Strong programming expertise in Python, distributed computing using PySpark, and API development.
Hands on experience with LLM frameworks (LangChain, LangGraph, Transformers, OpenAI/Vertex/Bedrock SDKs).
Experience developing AI agents, retrieval pipelines, tool calling structures, or autonomous task orchestration.
Solid understanding of GenAI concepts: prompting, embeddings, RAG, evaluation metrics, hallucination identification, model selection, fine tuning, context engineering.
Experience with cloud platforms (Azure/AWS/GCP), containerization (Docker), and CI/CD pipelines for ML/AI.
Strong problem solving, system design thinking, and ability to translate business needs into scalable AI solutions.
Excellent verbal, written communication and presentation skills.
Good to Have
Experience in workflow automation and building reusable AI components.
Background in analytics, statistical models, or enterprise data products.
Experience with MLOps / LLMOps tooling
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