AI Engineer

Location: Bengaluru

Company Description
Acronotics Limited is a specialist product, consulting, and services firm focused on transforming businesses through innovative AI and robotic process automation (RPA) technologies. We design, develop, and implement cutting-edge automation solutions, combining human intelligence and advanced AI to deliver unprecedented efficiency and performance. Our flagship product, Radium AI, automates bot monitoring and support activities, providing frictionless digital worker support powered by artificial intelligence.

At Acronotics, we are building a world-class team of engineers, designers, consultants, and thought leaders to push the boundaries of automated and AI-driven solutions. Learn more about our mission and products by visiting our website:
Radium AI.

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 (Lang Chain/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  Prompt Layer
Maintain few-shot/test prompt sets and continuously refine

Required Skills and Experience
2-6+ 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
Lang Chain ,  Semantic Kernel , or similar orchestration tools
Vector databases  (Azure AI Search, FAISS, Pinecone)
Embedding model APIs (OpenAI, Hugging Face, 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