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AI Engineer

Job in New York, New York County, New York, 10261, USA
Listing for: StatusNeo Inc.
Full Time position
Listed on 2026-09-13
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 150000 - 230000 USD Yearly USD 150000.00 230000.00 YEAR
Job Description & How to Apply Below
  • CoE/Practice Architecture, Delivery & Leadership
  • Work Experience 8+ years
  • City New York
  • State/Province New York
  • Country United States
Job Description

Status Neo is a global AI-native transformation firm helping enterprises design, engineer, and govern AI-led systems with trust at the core.

We work with global enterprises across BFSI, retail, healthcare, airlines, and platform-driven industries to transform how software is built, operated, and scaled in an AI-first world.

Our work is anchored in Authentic AI — an approach that treats AI not as a feature or experiment, but as a continuously evolving system that must be engineered with intent, accountability, and governance.

At Status Neo, we don’t just talk about AI transformation.

We build it — across engineering platforms, AI-native SDLC, agentic systems, and enterprise operating models.

Role Overview

Status Neo is seeking a passionate and innovative AI Engineer to design, develop, and deploy cutting-edgeAI and Generative AI solutions. The ideal candidate will have strong expertise in machine learning, large language models (LLMs), AI frameworks, andcloud-native technologies. You will work closely with product managers, data scientists, architects, and engineering teams to build intelligent systems thatdrive 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 Lang Chain, Lang Graph, or similar orchestration frameworks.
  • 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 backendservices 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:
    Lang Chain
  • Lang Graph (preferred)
  • Prompt Engineering
  • AI Agents
  • Hands-on experience with:

    Databricks
  • Snowflake
  • AWS (Bedrock, S3, Lambda, ECS/EKS,Sage Maker, 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 APIsand microservices architecture.
  • Familiarity with Git and Agile development methodologies.
Preferred Qualifications
  • Previous experience in Healthcare,Life Sciences, Health Tech, or Health Insurance.
  • Experience working withHIPAA-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 LLMperformance using frameworks such as Ragas, Deep Eval, or Lang Smith.
  • Familiarity with fine-tuning,embeddings, and model evaluation techniques.
  • Exposure to multi-agent AI system sand agentic workflows.
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