Agentic AI Engineer

Company Description

Acronotics Limited specializes in modern-age automation technologies such as Robotic Process Automation (RPA) and Artificial Intelligence (AI). We apply human intelligence to build cutting-edge robotic automation and AI solutions for our clients, transforming the way businesses operate. Our mission is to help clients design, develop, implement, and run truly transformative generative AI-based solutions.

Role Description

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

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

3–6+ 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

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

Location

Bangalore