Artificial Intelligence Engineer

Nice Software Solutions; Pune District, Gurugram District; Hybrid; INR 350,000 - 650,000; Full time

Job summary

NICE SOFTWARE SOLUTIONS in Pune is seeking an experienced LLM Scientist to research, design, and prototype advanced Generative AI systems. This role focuses on Agentic AI, RAG pipelines, and rapid Python-based prototyping for GenAI techniques ahead of production handoff.

The candidate will experiment with LoRA/QLoRA fine-tuning, develop evaluation frameworks for groundedness and relevance, and collaborate with ML engineers to scale prototypes into production.

Qualifications

• Strong understanding of statistics and probability.
• Experience with supervised, unsupervised, and semi-supervised learning.
• Knowledge of bias-variance tradeoff and regularization.
• Experience with model evaluation metrics (Precision, Recall, F1, ROC-AUC).
• Practical experience with classical ML algorithms (regression, classification, clustering, ensemble methods).
• Understanding of optimization fundamentals (gradient descent, learning-rate tuning, convergence).

Responsibilities

• Agentic AI and GenAI prototyping and evaluation.
• Fine-tuning LLMs using LoRA/QLoRA and experiments.
• Design and optimize RAG architectures and retrieval strategies.
• Build evaluation and benchmarking frameworks for GenAI systems.
• Collaborate with engineers to hand off prototypes for production.
• Apply statistical rigor to experimental results.

Skills

Python

LLMs

Transformer architectures

LangChain

LangGraph

MCP

Vector databases

Education

B.Tech / M.Tech in CS / IT or related field

Tools

LangChain

LangGraph

MCP

FAISS

ChromaDB

Pinecone

Job description

NICE SOFTWARE SOLUTIONS

We are one of the top business intelligence solutions company, experts in producing quicker & impactful self-driven BI applications which can enhance your business growth. .

Location: Pune

Role Overview

We are looking for an experienced LLM Scientist to research, design, and prototype advanced Generative AI systems. This role is experimentation- and coding-heavy, with a strong focus on Agentic AI design, RAG architecture, LLM fine-tuning, and rapid Python-based prototyping of new GenAI techniques ahead of production handoff.

Key Responsibilities

1. Agentic AI & GenAI Prototyping

• Design and prototype Agentic AI / Multi-Agent systems using LangGraph, LangChain, and MCP.
• Research and experiment with emerging LLM and Agentic AI techniques.
• Rapidly prototype and validate new GenAI approaches using Python.

2. LLM Fine-Tuning & Experimentation

• Fine-tune LLMs using LoRA/QLoRA.
• Conduct experiments comparing models, prompting strategies, and architectures.
• Run controlled experiments, including A/B tests and ablation studies.
• Apply statistical rigor when designing experiments and interpreting results.

3. RAG Architecture

• Architect and iterate on RAG pipelines.
• Experiment with chunking strategies, embeddings, hybrid search, and reranking.
• Evaluate retrieval quality and optimize retrieval strategies.

4. Evaluation & Benchmarking

• Build evaluation and benchmarking frameworks for GenAI systems.
• Evaluate retrieval quality, groundedness, hallucination, and relevance.
• Implement observability and tracing using tools such as LangFuse.
• Develop robust evaluation methodologies and avoid metric gaming.

5. Machine Learning & Statistical Analysis

• Apply statistical concepts including hypothesis testing, confidence intervals, significance testing, and sample-size considerations.
• Design and evaluate supervised, unsupervised, and semi-supervised learning approaches.
• Analyze bias-variance tradeoff, regularization, and overfitting/underfitting.
• Apply classical ML algorithms and evaluation metrics to support GenAI system development.

6. Collaboration & Research

• Track emerging LLM and Agentic AI research and assess applicability to business problems.
• Partner with LLM Engineers to hand off validated prototypes for production scaling.
• Research and evaluate new approaches to improve GenAI system performance.

Required Skills

ML & Statistical Foundations

• Strong understanding of statistics and probability, including distributions, hypothesis testing, confidence intervals, and significance testing.
• Strong understanding of supervised, unsupervised, and semi-supervised learning paradigms.
• Knowledge of bias-variance tradeoff, regularization, and overfitting/underfitting diagnosis.
• Experience with model evaluation metrics such as Precision, Recall, F1, and ROC-AUC.
• Practical experience with classical ML algorithms including regression, classification, clustering, and ensemble methods.
• Understanding of optimization fundamentals such as gradient descent, learning-rate tuning, and convergence behavior.

GenAI & Engineering Skills

• Strong Python coding skills with experience building experimental pipelines and tooling from scratch.
• Deep understanding of LLMs, Transformer architecture, and Generative AI fundamentals.
• Hands-on experience with Agentic AI, RAG, and LLM fine-tuning.
• Proficiency in LangChain, LangGraph, and MCP (Model Context Protocol).
• Experience with vector databases such as FAISS, ChromaDB, and Pinecone.
• Strong understanding of GenAI evaluation methodologies, including groundedness, hallucination detection, and relevance scoring.

Good to Have

• Knowledge of NLP techniques such as BERT, embeddings, and sentiment analysis.
• Experience with classical ML techniques such as XGBoost and anomaly detection.
• Research background or publications in NLP/LLMs.
• Experience working in regulated enterprise domains.

B.Tech/M.Tech in Computer Science, IT, or a related field