Description

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

Key Skills

Education

Bachelor's or Master's degrees