GEN AI

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.