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StatusNeo

AI Engineer

StatusNeo

Location
Onsite (New York, New York)
Employment
Full-time
Level
Senior Level
Posted 3 weeks ago

About the Role

StatusNeo is a global AI-native transformation firm helping enterprises design and govern AI-led systems. The company partners with global enterprises across industries like healthcare and BFSI to build scalable, intelligent systems that drive business value through authentic AI engineering.

Skills

Python Generative AI Large Language Models LangChain LangGraph RAG Prompt Engineering Databricks Snowflake AWS Vector Databases Machine Learning Docker Kubernetes CI/CD Microservices

Full job details

StatusNeo 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 StatusNeo, 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

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.