Senior GenAI Developer - Prompt Engineering

Role Senior Generative AI developer

Required Technical Skill Set : NLP, Vector Store, Generative AI(Prompt Engineering, Agent Based Architecture), SageMaker, Bedrock, Python, LangChain, Glue

Desired Competencies (Technical/Behavioral Competency) :

Must-Have :

- Experience in AWS services is a must.

- Proven experience in end to end development.

- 3 Tier Architecture based Application Development

- Proven experience in search-related technologies (Elasticsearch, Kendra, Coveo) and any other vector store (Faiss, AWS OpenSearch).

- Experience with back-end technologies (Python, Langchain, Django/Flask/FastAPI).

- Experience with cloud platforms (AWS).

- Knowledge of database technologies (SQL, NoSQL, Elastic/Solr).

- Strong knowledge of AWS services such as S3, DynamoDB, DocumentDB, Lambda, Glue, SageMaker, Bedrock, and Kendra.

- Experience in writing parsers to process unstructured data using AWS Textract.

- Must have experience in prompt engineering and prompt template management.

- Experience in developing agent frameworks.

- Experience with natural language processing (NLP).

Good-to-Have :

- Familiarity with DevOps practices and tools for CI/CD.

- Experience with service architecture.

- Experience in task-specific fine-tuning of LLMs.

- Exposure to generative AI guardrails.

- Knowledge of data governance and security best practices.

Responsibility of / Expectations from the Role :

- Design and develop generative AI platforms and frameworks to accelerate use case development.

- Develop pipelines to parse documents, chunk, vectorize, and persist vector stores.

- Design vector store schemas.

- Develop prompt templates with various prompt engineering techniques to interact with LLMs.

- Experience in prompting with few-shot examples.

- Develop semantic agents and interfaces between the agents.

- Develop microservices and expose them as APIs.

- Collaborate with architects and full-stack developers to deliver generative AI solutions.

- Implement best practices for data management and model deployment.

- Ensure the scalability and performance of generative AI solutions.