Job Description:
- We are looking for a highly skilled GenAI Engineer with 5 years of overall experience in designing developing and deploying AI ML solutions with strong hands on expertise in Generative AI LLMs and NLP use cases
- The ideal candidate should have experience building scalable AI applications on AWS or Databricks and should be comfortable working across model development prompt engineering RAG pipelines vector databases and deployment workflows
Key Responsibilities:
- 1 Design and develop Generative AI LLM based applications for enterprise use cases such as chatbots summarization document intelligence search Q A code assistants and workflow automation
- 2 Build and optimize RAG Retrieval Augmented Generation pipelines using embeddings vector databases chunking retrieval strategies and prompt orchestration
- 3 Work with foundation models LLMs from providers such as OpenAI Anthropic Cohere Hugging Face AWS Bedrock or open source models
- 4 Develop robust backend services and APIs to integrate GenAI solutions into enterprise platforms and applications
- 5 Fine tune evaluate and improve model responses through prompt engineering guardrails grounding and performance optimization
- 6 Build scalable AI pipelines on AWS or Databricks for training experimentation deployment and monitoring
- 7 Collaborate with Data Science ML Engineering Application Engineering and Product teams to productionize GenAI use cases
- 8 Ensure best practices in model deployment observability security governance and responsible AI
- 9 Contribute to architecture discussions PoCs reusable frameworks and accelerators for GenAI adoption
Technical Requirements:
- 1 Strong hands on experience in Python
- 2 Good experience with Generative AI Large Language Models LLMs
- 3 Solid understanding of NLP embeddings transformers prompt engineering and LLM application design
- 4 Experience building RAG pipelines
- 5 Hands on experience with orchestration frameworks such as LangChain LlamaIndex Semantic Kernel AutoGen CrewAI LangGraph
- 6 Experience with vector databases such as FAISS Pinecone Chroma Weaviate Milvus Elasticsearch OpenSearch
- 7 Exposure to REST APIs FastAPI Flask for serving AI applications
- 8 Strong understanding of ML lifecycle deployment testing and performance tuning
Additional Responsibilities:
- Cloud Platform Skills
- Candidate should have hands on experience in either AWS or Databricks
- AWS AWS Bedrock SageMaker Lambda ECS EKS S3 API Gateway CloudWatch IAM
- Experience deploying scalable AI ML workloads on AWS
- OR
- Databricks Databricks notebooks MLflow Model Serving Delta Lake Unity Catalog
- Experience building and deploying AI ML GenAI use cases on Databricks platform
- Exposure to MLOps LLMOps concepts
- Knowledge of model monitoring evaluation experimentation and versioning
- Experience with Docker Kubernetes CI CD pipelines
- Familiarity with guardrails AI safety content filtering and governance
- Exposure to multimodal AI agentic workflows or autonomous AI systems
- Understanding of structured unstructured data processing pipelines
Preferred Skills:
Technology->Generative AI->Generative AI for Data Analytics,Technology->data science->Databricks Machine Learning,Technology->Machine Learning->Generative AI->retrieval augmented generation (rag)
