Senior AI & Data Engineer: Generative AI & Data Pipelines

Medidata Solutions, Inc.

New York, Northern (NY, KY)

Hybrid

USD 115,000 - 153,000

Full time

8 hours ago

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Benefits offered by this job

Medical insurance

Dental insurance

Life insurance

Disability insurance

401(k) matching

Family leave

Flexible paid time off

10 paid holidays

Job summary

Medidata Solutions, Inc. is seeking a Senior AI & Data Engineer – Study Experience to design, build, and operate production-grade AI and data solutions enabling intelligent applications and automated workflows.

You will develop AI-enabled services, connect enterprise data to LLM-powered workflows, and build scalable data pipelines using modern cloud technologies. You will work across Generative AI, agentic workflows, AWS, Snowflake, Python, and Java to power AI-driven analytics and enterprise

Qualifications

5+ years of professional experience in software engineering, AI engineering, data engineering, or a related technical discipline.
Strong hands-on experience developing production applications and services using Python.
Practical experience building or integrating Generative AI applications, LLM workflows, agentic systems, or AI-enabled services.
Good understanding of RAG, embeddings, vector search, retrieval, grounding, and LLM orchestration concepts.
Experience with at least one AI orchestration framework or emerging protocol such as LangGraph, LangChain, MCP, or equivalent technologies.
Strong SQL and data engineering fundamentals, including experience building production data pipelines and integrations.
Experience working with AWS and modern data platforms such as Snowflake.
Experience developing or integrating REST APIs and backend services.
Strong understanding of modern software engineering practices including automated testing, CI/CD, version control, observability, and production support.
Ability to independently troubleshoot complex issues across AI services, APIs, data pipelines, and cloud environments.
Strong communication and collaboration skills and the ability to contribute effectively to technical design discussions.

Responsibilities

Design and build production Generative AI applications, AI agents, and LLM-powered workflows for enterprise use cases.
Develop AI services and backend capabilities using Python, APIs, and cloud-native engineering patterns.
Build RAG and retrieval workflows that securely connect enterprise data and applications to AI solutions.
Implement agentic workflows involving tool calling, structured outputs, retrieval, workflow execution, and external system integrations.
Work with AI orchestration frameworks and emerging standards such as LangGraph, LangChain, MCP, or similar technologies.
Implement testing, evaluation, guardrails, monitoring, and observability to improve the quality and reliability of production AI solutions.
Design and maintain scalable data pipelines and ETL/ELT workflows that support AI, analytics, and operational applications.
Build data solutions using AWS, Snowflake, Python, SQL, and modern data engineering technologies.
Develop and integrate APIs and backend services, including Java-based applications where needed.
Apply strong software engineering practices including automated testing, CI/CD, Git, security, documentation, and production support.
Use AI-assisted development tools and coding agents as part of day-to-day design, coding, testing, debugging, and code review.
Collaborate with engineers, architects, product teams, and business stakeholders on technical designs and production solutions.
Mentor engineers through technical guidance, code reviews, and knowledge sharing.