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Sr Data Scientist - Gen AI ML - Citi - Irving at Photon Career Site
, , United States -
Full Time

Start Date

Immediate

Expiry Date

16 Jul, 26

Salary

188000.0

Posted On

17 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Python, Generative AI, LLMs, Prompt engineering, Fine-tuning, RAG, LangGraph, LangChain, FastAPI, AWS, Azure, GCP, MLOps, ETL, Data pipelines, Responsible AI

Industry

IT Services and IT Consulting

Description
Core Skill Set: * Python (advanced, production-grade coding) * Generative AI (LLMs, prompt engineering, fine-tuning, RAG) * Agent development using frameworks such as ADK, LangGraph, LangChain Additional Relevant Skills: * Experience building end-to-end AI applications (design → deploy → scale) * Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., Pinecone, FAISS) * API development and integration (FastAPI, REST, microservices) * Cloud platforms: AWS / Azure / GCP (especially AI/ML services) * Model evaluation, guardrails, and Responsible AI practices * Experimentation frameworks, prompt/version management, and observability * Working knowledge of data pipelines and engineering (ETL, streaming) * Familiarity with MLOps / LLMOps (CI/CD for models, monitoring, retraining) * Strong problem-solving with ability to translate business use cases into AI solutions   Compensation, Benefits and Duration Minimum Compensation: USD 53,000 Maximum Compensation: USD 188,000 Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role. Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees. This position is not available for independent contractors No applications will be considered if received more than 120 days after the date of this post
Responsibilities
The role involves building end-to-end AI applications, including design, deployment, and scaling of generative AI solutions. Responsibilities also include developing RAG pipelines, managing model guardrails, and implementing MLOps practices.