AI/ML Engineer

About the Company

Acronotics is a global consulting, services and a product company specialising in Hyper Automation (IPA & Low Code Process Automation), Data Science & Artificial Intelligence (AI). We design, develop and implement cognitive automation solutions by applying a combination of RPA & AI technologies such as ML, NLP, NLG for clients across industries and around the globe.

The company is headquartered in the UK and has subsidiaries in the US and India. The company's offices are in UK (London), India (Bangalore and Pune) and USA (New Jersey).

Job Title:

AI/ML Engineer

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

4–6+ 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