Generative AI Developer - Senior Associate

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 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

• 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