AI/ML Engineer

We're Hiring: AI/ML Engineer (GenAI & LLM Focus)

A leading global telecom analytics company is seeking a highly skilled AI/ML Engineer (GenAI/LLM) to design, fine-tune, and operationalize Large Language Models (LLMs) for complex telecom business applications. In this role, you will build domain-specific GenAI solutions, transforming telecom operational processes, customer interactions, and internal decision-making workflows.

Role Overview

• Role: AI/ML Engineer – Engineering
• Industry: Telecommunications & Data Analytics
• Experience: 4+ years in AI/ML (with 2+ years in LLMs or GenAI deployments)
• Education: B.E. / B.Tech, M.E. / M.Tech, or M.Sc. in Computer Science or a related field
• Location: Bangalore.

Key Responsibilities

• Domain-Specific LLMs: Curate domain-relevant datasets to train and fine-tune LLMs (e.g., GPT, Llama, Mistral) tailored to telecom use cases.
• RAG & Agent Workflows: Develop Retrieval-Augmented Generation (RAG) pipelines integrated with vector databases (FAISS, Pinecone). Build multi-agent LLM pipelines using orchestration tools like LangChain and LlamaIndex.
• Prompt Engineering: Design prompt engineering frameworks and optimize context strategies for complex telco-specific queries.
• Cross-Functional Collaboration: Partner with data engineers, product teams, and domain experts to translate telecom business logic into active GenAI workflows.
• Model Evaluation & Quality: Conduct systematic model evaluations to minimize hallucinations, enhance domain-specific accuracy, and track business KPIs.
• Best Practices: Build reusable internal GenAI modules, maintain coding standards, and document best practices.

Technical Qualifications & Skills

• Core Tech Stack: Proficiency in Python, PyTorch, Hugging Face Transformers, and NLP libraries.
• LLM Architecture & Fine-Tuning: Deep understanding of transformer architectures and fine-tuning techniques (LoRA, PEFT, adapters).
• Frameworks & Tools: Hands-on expertise with prompt engineering, RAG architecture, and orchestration frameworks (LangChain, LlamaIndex).
• Bonus Exposure: Experience with multi-modal LLMs (text + tabular/time-series), OpenAI function calling, LangGraph, low-latency inference optimization (quantization, distillation), and telecom datasets (call records, network logs, customer tickets).