What are the responsibilities and job description for the Generative AI Developer position at MphasiS Corporation USA?
Job Details
We are seeking a Mid-Level Generative AI Developer with strong expertise in Python, AI-focused Python libraries, and AWS services to design, develop, and deploy cutting-edge AI solutions. The ideal candidate will have hands-on experience with AWS Bedrock, AWS Knowledge Base, and a deep understanding of LLM models. They also understand Retrieval-Augmented Generation (RAG) models, vector databases, and AWS Serverless Technologies to build scalable and efficient AI solutions.
Key Responsibilities:
Develop and fine-tune Generative AI solutions using foundation models
Develop and optimize AI solutions using Python and AI-related Python libraries
Leverage AWS AI Services & AWS Serverless technologies to build & deploy intelligent AI-driven solutions.
Ability to implement Retrieval-Augmented Generation (RAG) techniques using vector databases to improve AI responses.
Work closely with cross-functional teams to design and deploy AI-driven applications in a cloud-native environment.
Stay updated on emerging AI trends, best practices, and advancements in LLMs and generative AI models.
Collaborate with data engineers and domain experts to integrate AI solutions into existing insurance platforms.
Ensure compliance with industry regulations and data security standards while handling sensitive insurance data.
Required Skills & Qualifications:
3 years of experience in AI/ML development, with a focus on Generative AI and LLMs.
Strong proficiency in Python and experience with AI/ML-focused libraries (e.g., PyTorch, TensorFlow, LangChain, Transformers).
Hands-on experience with AWS Bedrock and AWS Knowledge Base.
Solid understanding of LLM models (GPT, Claude, Llama, Falcon, etc.) and their applications.
Experience with RAG (Retrieval-Augmented Generation) models and vector databases (e.g., Pinecone, OpenSearch).
Expertise in AWS Serverless Technologies, including Lambda, API Gateway, Step Functions, S3, and DynamoDB.
Familiarity with MLOps practices, CI/CD pipelines for AI models, and cloud-based AI deployment.
Strong problem-solving skills, analytical thinking, and ability to work independently or in a team environment
