AI/ML Engineer (Azure OpenAI)
- Acronotics
- 3 - 7 years
- Bengaluru
- 4 months ago
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Job Description
About the Role
We are looking for a skilled AI/ML Engineer to help design and implement GenAI-based systems that interface with real-time enterprise data. You will be responsible for developing, fine-tuning, orchestrating, and integrating LLM-powered capabilities such as retrieval-augmented generation (RAG), function/tool calling, and data-grounded Q&A, within the Azure OpenAI ecosystem.
The ideal candidate brings hands-on experience with LLM orchestration frameworks, prompt engineering, embedding models, and integrating AI systems into production-grade Azure-based platforms.
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
Build, train, and fine-tune time series forecasting models (e.g., XGBoost, Prophet, ARIMA, or LSTM) for financial KPIs where GenAI requires predictive context
Combine structured model outputs with LLM reasoning (e.g., forecasts + narrative insights)
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
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Machine Learning EngineerEmployement Type: Full timeContact Details:
Company: AcronoticsLocation(s): Bengaluru
Keyskills: Prompt Engineering AI/ML Engineer Azure AI RAG Machine Learning
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