Role description
Role Summary
We are seeking a skilled GenAI Engineer to design build and operationalize nextgeneration AI solutions leveraging Large Language Models LLMs AI agents Retrieval Augmented 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 such as LangChain LlamaIndex Semantic Kernel AutoGen and CrewAI
Engineer robust prompting strategies memory mechanisms and toolaugmented reasoning
RAG and Knowledge Systems
Design and implement Retrieval Augmented Generation RAG architectures
Build embedding pipelines using vector databases such as FAISS Pinecone Weaviate Azure AI Search and 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 and Pipelines
Develop and maintain data pipelines for structured and unstructured data using Python and SQL
Work with large datasets APIs and streaming and batch processing frameworks
Ensure data quality lineage observability and governance within AI workflows
MLOps CICD and 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 and 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 and 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 regulated domains such as BFSI or healthcare
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
