Java FSD Gen AI

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

Cloud Platform Engineering

Design and deploy AI solutions on cloud platforms such as AWS Azure or GCP

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

Core Technical Skills

Strong proficiency in Python and working knowledge of SQL

Solid foundation in AIML concepts with handson experience deploying models

Proven experience with LLMs AI agents and agent frameworks

Handson expertise with RAG architectures and vector databases

Experience implementing CICD pipelines for AI or ML systems

Strong understanding of data pipelines and distributed data processing

Experience working on at least one major cloud platform AWS Azure or GCP

Preferred Good to Have

Experience finetuning LLMs LoRA PEFT RLHF concepts

Familiarity with evaluation frameworks for GenAI hallucination testing grounding latency benchmarks

Exposure to governance security and compliance considerations for enterprise AI

Background in domains such as BFSI healthcare or regulated industries

Education

Bachelors or Masters degree in Computer Science Engineering Data Science or a related field or equivalent practical experience

What Success Looks Like

Scalable reliable GenAI solutions deployed to production

Wellarchitected AI agents delivering measurable business value

Highquality explainable and maintainable AI systems

Strong collaboration across engineering data and business teams

Skills

Mandatory Skills : Java GenAI - LLMOps LLMOps