Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global professional services organization is investing heavily in the future of healthcare through an AI-first initiative focused on transforming decision-making across the healthcare ecosystem. Backed by a significant long-term investment, the organization is developing advanced AI-powered systems that enhance clinical reasoning, streamline operational workflows, improve patient care, and support healthcare providers, payers, and life sciences organizations through intelligent automation and responsible AI innovation.
Key Responsibilities
- Design and develop production-ready agentic AI systems capable of multi-step reasoning, planning, tool usage, and workflow orchestration.
- Build stateful AI workflows using frameworks such as LangGraph, LangChain, or equivalent orchestration technologies.
- Develop reliable long-horizon AI agents capable of recovering from failures and adapting to complex decision-making scenarios.
- Engineer reasoning systems that generate policy-grounded, auditable, and explainable outputs for regulated healthcare processes.
- Design and optimize Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embeddings, retrieval, reranking, and contextual grounding.
- Build memory management and context engineering capabilities for conversational AI systems and persistent agent memory.
- Develop observability, tracing, monitoring, and evaluation frameworks for AI agents, prompts, retrieval quality, and production performance.
- Implement AI safety mechanisms, guardrails, human-in-the-loop workflows, and compliance controls to minimize hallucinations and operational risks.
- Integrate AI agents with enterprise applications, APIs, databases, cloud platforms, and external tools.
- Develop production-quality software following best practices in testing, CI/CD, logging, documentation, and version control.
- Collaborate with AI researchers and model engineering teams to improve reasoning performance, tool usage, and production reliability.
- Translate complex business and operational processes into scalable AI-powered solutions while staying current with emerging AI technologies and research.
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, Computational Linguistics, or a related discipline.
- Demonstrated experience designing, building, and deploying production-grade agentic AI systems.
- Strong expertise with AI orchestration frameworks such as LangGraph, LangChain, or similar technologies.
- Experience designing and optimizing end-to-end Retrieval-Augmented Generation (RAG) systems.
- Deep understanding of memory management, context engineering, and retrieval-driven AI architectures.
- Strong knowledge of Large Language Models (LLMs), reasoning limitations, hallucination mitigation, and AI evaluation methodologies.
- Experience evaluating, debugging, and improving multi-step AI agent behavior.
- Strong Python programming skills and experience with software engineering best practices, testing, CI/CD, API development, and production deployment.
- Experience working with leading AI model platforms such as OpenAI, Anthropic, Google, or open-source models including Llama and vLLM.
- Ability to travel up to 50% based on project and client requirements.
- Eligibility to work within applicable employment and immigration requirements.
Preferred Qualifications
- Experience building multi-agent systems and collaborative AI architectures.
- Familiarity with vector databases such as Pinecone, Weaviate, or Milvus.
- Experience with model fine-tuning techniques including LoRA or QLoRA.
- Knowledge of Natural Language Processing (NLP) concepts including tokenization, semantic similarity, entity extraction, summarization, and transformer architectures.
- Experience working within regulated industries such as healthcare, financial services, or other compliance-driven environments.
- Familiarity with healthcare standards such as FHIR is advantageous but not required.
- Strong interest in staying current with advancements in AI research, benchmarks, and emerging engineering practices.
Disclaimer
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