AI/ML Engineer (Azure OpenAI)

About the Role

skilled AI/ML Engineer

LLM orchestration frameworks

Core Responsibilities

LLM System Development

• Design and implement 

  LLM-based pipelines

  , including:
• Prompt engineering
• Few-shot and zero-shot techniques
• Function/tool calling
• Chain-of-thought and structured output generation
• Work with 

  Azure OpenAI

  , 

  GPT-4

  , and embedding models for various use cases
• Build conversational flows, decision trees, and fallback logic for copilots or assistants

Retrieval-Augmented Generation (RAG)

• Develop and optimize 

  RAG pipelines

  :
• Create embedding pipelines (e.g., using text-embedding-ada-002, Cohere, or Sentence Transformers)
• Chunk and index content from structured and unstructured sources (PDFs, Office files, HTML, etc.)
• Store and retrieve embeddings using 

  Azure AI Search

  , 

  FAISS

  , or 

  Weaviate

• Evaluate grounding accuracy and relevance scoring

Machine Learning Models

time series forecasting models

Tool/Function Integration

• Integrate structured data APIs, SQL endpoints, Power BI connectors, and OLAP cube access as 

  tools/functions

  callable by the LLM
• Design input/output schemas for safe and deterministic API usage by the model
• Support plugin-style orchestration (LangChain/Function Calling/Semantic Kernel)

Evaluation & Iteration

• Define custom 

  evaluation frameworks

   using metrics like:
• Hallucination rate
• Grounding precision/recall
• Prompt latency and token efficiency
• Set up experiment tracking using tools like 

  MLflow

  , 

  Weights & Biases

  , or 

  PromptLayer

• Maintain few-shot/test prompt sets and continuously refine

Required Skills and Experience

• 36+ years of experience in AI/ML/NLP engineering
• Deep familiarity with 

  LLM systems

  : prompt tuning, orchestration, and fine-tuning
• Hands-on experience with:

• Azure OpenAI Service

• LangChain

  , 

  Semantic Kernel

  , or similar orchestration tools

• Vector databases

   (Azure AI Search, FAISS, Pinecone)
• Embedding model APIs (OpenAI, HuggingFace, Cohere, etc.)
• Strong understanding of 

  time series modeling and ML forecasting techniques

  in financial domains (e.g., cost, margin, working capital, price volatility)
• Strong proficiency in

  Python

  , with experience in developing modular, testable code for AI/ML pipelines, API integrations, and backend services
• Experience building and deploying 

  backend components

   (e.g. FastAPI, Flask) to serve AI models or integrate with retrieval pipelines
• Familiarity with best practices for 

  production-grade AI applications

  , including logging, monitoring, and containerisation (e.g. Docker)
• Ability to work across the full stack of an AI system – from model development to integration and inference APIs
• Experience in building chatbots or copilots in enterprise settings
• Knowledge of 

  Azure cloud services

  , esp. 

  Functions

  , 

  App Services

  , 

  Blob Storage

  , and 

  Key Vault

• Familiarity with enterprise systems like 

  Power BI

  , 

  SAP

  , or 

  OLAP cubes