Job Description

Title: Gen AI/AI Engineer

Location: Philadelphia, PA or New Jersey

Duration: Long-term Contract

Key Responsibilities:

• Develop and deploy AI/ML pipelines that ingest and analyze unstructured data (e.g., application logs, usage data, PDFs).
• Build and maintain scalable solutions using generative AI models for tasks such as document summarization, anomaly detection, and content extraction.
• Design and implement data processing pipelines feeding into analytical data marts.
• Integrate and optimize vector databases for semantic search and retrieval-augmented generation (RAG) applications.
• Collaborate closely with data engineers and business stakeholders to translate requirements into production-grade solutions.
• Leverage AWS tools (e.g., Bedrock, Textract, Comprehend) to implement AI-powered features and automate unstructured data workflows.
• Contribute to the team's Agile practices including sprint planning, backlog grooming, and demos.

Required Qualifications:

• 3+ years of experience in AI/ML engineering, with proven deployment of models in production.
• Proficiency in Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow, Lang Chain, Hugging Face).
• Hands-on experience with generative AI technologies (e.g., LLMs, diffusion models, RAG systems).
• Solid understanding and application of Vector Databases (e.g., FAISS, Pinecone, Weaviate, Milvus).
• Demonstrated ability to work with application logs, telemetry data, and unstructured documents (e.g., PDFs, text logs).
• Knowledge of document parsing, entity extraction, summarization, and topic modeling techniques.
• Familiarity with AWS AI/ML tools , particularly Textract, Bedrock, Comprehend, and S3 .
• Experience deploying ML models in containerized environments (e.g., Docker, Kubernetes).

Preferred Qualifications:

• Experience in fraud analytics or insurance claims systems .
• Background in data warehousing and data mart design for BI/reporting use cases.
• Understanding of MLOps best practices and CI/CD pipelines for model deployment.
• Exposure to Agile/Scrum development and cross-functional team collaboration.

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