Java Full Stack

Java FSD- GenAI

Dallas - Texas Plano TX or Charlotte NC or Seattle WA or Jersey City NJ

Full-Time Position

Job description

Role Summary

We are seeking a highly skilled GenAI Engineer to design build and operationalize nextgeneration AI solutions leveraging Large Language Models LLMs AI agents RetrievalAugmented Generation RAG architectures and scalable cloud platforms This role requires strong handson expertise across AI concepts model integration data pipelines and MLOpsCICD with the ability to translate business problems into productiongrade AI systems
Key Responsibilities

GenAI LLM Engineering

Design develop and deploy LLMpowered applications using leading foundation models OpenAI Azure OpenAI Anthropic opensource LLMs

Build LLMbased AI agents capable of multistep reasoning tool use orchestration and autonomous workflows

Implement and optimize agent frameworks LangChain LlamaIndex Semantic Kernel AutoGen CrewAI etc

Engineer robust prompting strategies memory mechanisms and toolaugmented reasoning

RAG Knowledge Systems

Design and implement RetrievalAugmented Generation RAG architectures

Build embedding pipelines using vector databases FAISS Pinecone Weaviate Azure AI Search Chroma

Optimize document ingestion chunking strategies metadata management and reranking

Ensure accuracy relevance and performance of AIgenerated responses

Machine Learning Model Integration

Apply practical ML concepts including classification clustering ranking and similarity search where applicable

Integrate traditional ML models with LLMbased systems for hybrid AI solutions

Evaluate finetune and test models using appropriate performance metrics

Data Engineering Pipelines

Develop and maintain data pipelines for structured and unstructured data using Python and SQL

Work with large datasets APIs and streamingbatch processing frameworks

Ensure data quality lineage observability and governance within AI workflows

MLOps CICD Productionization

Build CICD pipelines for AI and ML workloads including model versioning and automated testing

Deploy AI services in containerized environments Docker Kubernetes

Implement monitoring for model performance drift latency and cost

Ensure security access control and compliance for AI systems

Design and deploy AI solutions on cloud platforms such as AWS Azure or Google Cloud Platform

Leverage managed AIML services serverless components and scalable infrastructure

Optimize cost performance and reliability of AI workloads

Collaboration Stakeholder Engagement

Partner with product platform and business teams to translate requirements into AI solutions

Document architectures design decisions and operational runbooks

Provide guidance on GenAI best practices risks and responsible AI usage

Required Skills Experience