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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