GEN AI

Job Description

Job Title: Gen AI Integration Developer Key Responsibilities As a Gen AI Integration Developer, you will: Gen AI Orchestration & Integration: Implement and integrate Generative AI solutions. This includes extensive experience in data analytics or a senior developer role with a modern technology stack. LLM Integration: Gain hands-on exposure to integrating at least one popular Large Language Model (LLM) such as OpenAI GPT, PaLM 2, Dolly, Claude 2, or Cohere, using API endpoints. Prompt Engineering & Agents: Apply a thorough understanding of prompt engineering principles and implement LLM agents like LangChain. Vector Databases: Work with vector databases such as Pinecone, Chroma, or FAISS. API Development: Build API-based scalable solutions and possess strong debugging and troubleshooting skills for software and design issues. Data Engineering: Perform basic data engineering tasks, including loading structured and unstructured data from source systems to target data stores. Build and maintain data pipelines and infrastructure to support AI Solutions. Experimentation & Analysis: Quickly conduct experiments and analyze the features and capabilities of newer versions of LLM models as they become available. Collaboration: Work closely with Gen AI leads and other team members to address requirements from the product backlog. Mandatory Skills & Experience Technical Proficiency: Programming Languages: Excellent programming skills and proficiency in at least one major programming/scripting language used in Gen AI orchestration, such as Python, PySpark, or Java . LLM Integration: Hands-on exposure to integrating at least one of the popular LLMs (OpenAI GPT, PaLM 2, Dolly, Claude 2, Cohere, etc.) using API endpoints. Prompt Engineering: Thorough understanding of prompt engineering . LLM Agents: Implementation exposure to LLM agents like LangChain . Vector Databases: Experience with vector databases such as Pinecone, Chroma, or FAISS. API Development: Ability to build API-based scalable solutions . Debugging & Troubleshooting: Strong ability to debug and troubleshoot software or design issues. Basic Data Engineering: Skills to load structured & unstructured data from source systems to target data stores. Experience & Qualifications: Extensive implementation experience in the data analytics space or a senior developer role in one of the modern technology stacks. Ability to quickly conduct experiments and analyze new LLM features. Experience in building and maintaining data pipelines and infrastructure for AI solutions. Essential Professional Skills Desirable: Hands-on exposure to using cloud services (Azure/GCP/AWS) for storage, serverless-logic, search, transcription, and chat. Extensive experience with data engineering and ETL tools is a big plus. Master's/Bachelor's degree in Computer Science, Statistics, or Mathematics.