AI Engineer

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.