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