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).

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

• 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