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AI Engineer

StatusNeo Inc.United States🇺🇸United StatesPosted 30 Jul 2026

Why This Role Stands Out

As an AI Engineer at StatusNeo, you'll be at the forefront of innovation, designing and deploying cutting-edge Generative AI and LLM solutions within a globally recognized digital transformation leader. This remote role offers immense growth potential for skilled engineers passionate about machine learning and cloud-native technologies, allowing you to build intelligent systems that drive significant business impact. If you're eager to shape the future of AI and contribute to impactful projects, we encourage you to apply.

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
United States
Posted
3 weeks ago
DockerMicroservicesAWSMLOpsMachine LearningSnowflakeAgileAzureDatabricksGenerative AIGitHIPAAKubernetesLLMPythonREST

Job Description

Title: AI Engineer

Location: Remote
Employment Type: Full-Time

About StatusNeo

StatusNeo is a global digital transformation and product engineering company that helps organizations build innovative, scalable, and AI-driven solutions. We partner with startups, enterprises, and technology leaders to accelerate business outcomes through modern engineering, cloud, data, and artificial intelligence capabilities.

Role Overview

StatusNeo is seeking a passionate and innovative AI Engineer to design, develop, and deploy cutting-edge AI and Generative AI solutions. The ideal candidate will have strong expertise in machine learning, large language models (LLMs), AI frameworks, and cloud-native technologies. You will work closely with product managers, data scientists, architects, and engineering teams to build intelligent systems that drive business value.

Responsibilities

  • Design, develop, and deploy production-ready Generative AI and LLM-powered applications.
  • Build scalable Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge sources.
  • Develop AI agents and workflow automation using LangChain, LangGraph, or similar orchestration frameworks.
  • Integrate LLMs (OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, etc.) into enterprise applications.
  • Build and optimize data pipelines using Databricks, Snowflake, and AWS services.
  • Design semantic search solutions using vector databases such as Pinecone, Weaviate, Chroma, or FAISS.
  • Develop REST APIs and backend services to expose AI capabilities.
  • Collaborate with data engineering teams to prepare, transform, and govern structured and unstructured healthcare data.
  • Ensure AI solutions meet security, compliance, and privacy requirements, including HIPAA where applicable.
  • Optimize model performance, latency, accuracy, and cost.
  • Participate in architecture reviews, code reviews, and technical design sessions.
  • Stay current with emerging AI technologies and recommend best practices for enterprise adoption.

Required Qualifications

  • Bachelor''s or Master''s degree in Computer Science, Engineering, AI, Data Science, or a related field.
  • 5+ years of software engineering experience.
  • 3+ years of experience building AI/ML or Generative AI applications.
  • Strong programming skills in Python.
  • Experience building production applications using:
    • LangChain
    • LangGraph (preferred)
    • RAG architectures
    • Prompt Engineering
    • AI Agents
  • Hands-on experience with:
    • Databricks
    • Snowflake
    • AWS (Bedrock, S3, Lambda, ECS/EKS, SageMaker, IAM)
  • Experience with vector databases such as Pinecone, Weaviate, FAISS, Chroma, or Milvus.
  • Experience integrating enterprise data sources and APIs.
  • Knowledge of MLOps and model deployment best practices.
  • Experience with Docker, Kubernetes, and CI/CD pipelines.
  • Strong understanding of REST APIs and microservices architecture.
  • Familiarity with Git and Agile development methodologies.

Preferred Qualifications

  • Previous experience in Healthcare, Life Sciences, HealthTech, or Health Insurance.
  • Experience working with HIPAA-compliant systems and PHI.
  • Knowledge of FHIR, HL7, Epic, Cerner, or other healthcare interoperability standards.
  • Experience building AI-powered clinical assistants, patient engagement platforms, claims automation, or healthcare knowledge systems.
  • Experience evaluating LLM performance using frameworks such as Ragas, DeepEval, or LangSmith.
  • Familiarity with fine-tuning, embeddings, and model evaluation techniques.
  • Exposure to multi-agent AI systems and agentic workflows.

 

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