Job Description

Industry/Sector

Not Applicable

Specialism

Oracle

Management Level

Senior Associate

Job Description & Summary

Job Title: Senior Associate – Generative AI Developer

Experience:

5+ years of total IT experience, including 2–3 years in AI/ML or Generative AI development. Must have hands-on experience building, testing, and deploying LLM-based or agentic AI applications in production or pilot settings.

Role Overview:

The Senior Associate – Generative AI Developer will design, develop, and rigorously test end-to-end Generative AI and agentic AI applications. This role requires hands-on expertise with LLM APIs, RAG pipelines, prompt engineering, and multi-agent frameworks, combined with a disciplined approach to testing, performance monitoring, and optimization.

Key Responsibilities:

• LLM & GenAI Development:
• Design, develop, and maintain LLM-powered applications using APIs such as OpenAI, Azure OpenAI, Anthropic, or Cohere. Implement core GenAI workflows including summarization, classification, Q&A, and content generation.
• Agentic AI Implementation:
• Build and orchestrate multi-agent systems using frameworks like CrewAI, LangGraph, OpenAI Agents SDK, or Azure AI Agent Framework. Implement inter-agent communication, task routing, and tool orchestration for complex workflows.
• RAG & Embedding Pipelines:
• Implement retrieval-augmented generation pipelines involving embedding creation, chunking, and vector indexing using FAISS, Pinecone, Chroma, or Milvus. Ensure efficient retrieval for context-aware outputs.
• Prompt Engineering & Techniques:
• Apply advanced prompting strategies such as Few-Shot Learning, Chain-of-Thought (CoT), ReAct, and CART to improve reliability and interpretability of model responses.
• Testing & Validation:
• Establish automated and manual testing pipelines using TruLens, LangSmith, PromptLayer, or DeepEval to evaluate model quality, accuracy, safety, and factual grounding.
• Define and track metrics such as coherence, hallucination rate, and response diversity.
• Performance Monitoring & Optimization:
• Implement cost-effective token management and model observability using logging and tracing frameworks. Continuously optimize prompts, retrieval logic, and memory mechanisms to improve efficiency.
• Data Analysis & Preparation:
• Conduct EDA and pre-processing of textual datasets to ensure quality input for training, evaluation, and fine-tuning tasks.
• Documentation & Collaboration:
• Create detailed design documents, maintain experiment logs, and collaborate with architects, data scientists, UI/UX engineers, and product managers to ensure solution alignment with business goals.
• Continuous Learning:
• Stay up to date with new model releases (OpenAI GPT, Claude, Gemini, Mistral, LLaMA, etc.) and frameworks in the GenAI and agentic AI ecosystem.

Key Skills & Competencies:

• Strong proficiency in Python, LangChain, LlamaIndex, CrewAI, and LangGraph
• Deep understanding of LLM architectures, tokenization, embedding models, and vector databases
• Experience with prompt testing and evaluation frameworks (TruLens, LangSmith, PromptLayer, DeepEval)
• Familiarity with bias mitigation, safety testing, and Responsible AI principles
• Knowledge of MLOps/LLMOps practices for deploying and monitoring AI models
• Strong analytical mindset, documentation discipline, and collaborative approach
• Exposure to at least one major cloud AI platform (Azure, AWS, GCP, or OCI)

Preferred Background:

• Prior experience in building chatbots, intelligent assistants, summarizers, or document automation solutions using LLMs
• Familiarity with open-weight model experimentation (LLaMA, Mistral, Falcon, etc.)
• Exposure to multi-modal AI (text, image, code) and integration with enterprise data sources
• Experience supporting GenAI projects through testing, benchmarking, and performance optimization

Travel Requirements

Not Specified

Job Posting End Date