Srijan Shukla

AI Researcher

Jodhpur, Rajasthan, India3 yrs 11 mos experience

AI ML PractitionerAI Enabled

Key Highlights

Expert in building scalable AI systems.
Proven track record in Generative AI and predictive analytics.
Strong background in Graph Neural Networks and LLM applications.

Stackforce AI infers this person is a Data Science and AI Engineering expert in the Healthcare and Energy sectors.

Contact

Skills

Core Skills

Generative AiMachine Learning

Other Skills

Python (Programming Language)Graph Neural NetworksLarge Language ModelsAzureAgentic AI DevelopmentRetrieval-Augmented Generation (RAG)FastAPILarge Language Models (LLM)PySparkScikit-LearnGoogle Cloud Platform (GCP)Deep LearningComputer VisionBig DataAttention to Detail

About

AI Engineer | GenAI | Agentic AI | ML Engineer | Data Science | M.Tech @ IIT Jodhpur I’m a results-driven AI Engineer and Data Scientist with 4+ years of experience building and deploying production-grade Machine Learning and Generative AI systems to solve real-world business problems. Currently working on Agentic AI, LLM-powered systems, Graph Neural Networks (GNNs), and predictive analytics, helping enterprises automate decision-making, improve customer experience, and optimize operations at scale. 💡 What I do best: • Build end-to-end AI systems (from data pipelines → ML models → LLM apps → production deployment) • Design GenAI solutions using LLM fine-tuning, RAG pipelines, vector databases, and AI agents • Apply Graph Neural Networks (GraphSAGE) for relationship-based prediction and root cause analysis • Deploy scalable ML systems using FastAPI, Docker, Azure, GCP 🚀 Recent impact: • Built an AI-driven customer experience optimization system using GNNs + LLMs for health insurance • Developed predictive outage & service analytics systems for power distribution operations • Delivered explainable AI models improving operational KPIs and customer resolution workflows 🧠 Tech Stack: Python, SQL, PyTorch, Scikit-learn, LangChain, LangGraph, FastAPI, LLMs, RAG, Vector DBs (ChromaDB), Docker, Azure, GCP, MLflow 🎯 Currently looking for: GenAI Engineer | AI Engineer | ML Engineer | Applied Scientist | Data Engineer (AI/ML Systems) roles where I can build large-scale AI products, agentic workflows, and intelligent automation systems. 📩 Open to opportunities, collaborations, and high-impact AI projects.

Experience

3 yrs 11 mos

Total Experience

1 yr 11 mos

Average Tenure

0 mo

Current Experience

Statusneo

Digital Consultant – Generative AI

Jun 2026 – Present · 0 mo · Gurugram, Haryana, India · On-site

Wipro

Data Scientist

Mar 2022 – Mar 2025 · 3 yrs · Noida, Uttar Pradesh, India · Hybrid

ML Engineer – Agentic AI
Led development of an AI-driven customer experience optimization platform for a health insurance client using Graph Neural Networks (GNNs) + Large Language Models (LLMs) to automate issue detection, intent understanding, and resolution recommendations.
Designed and trained GraphSAGE-based models to predict customer experience bottlenecks across interaction graphs (policyholders, claims, tickets, agents), improving root cause detection and churn risk analysis.
Implemented GNNExplainability (GNNExplainer) to provide interpretable insights for business stakeholders, identifying key workflow stages and agents impacting customer dissatisfaction.
Built domain-adapted LLM pipelines using Phi-3 Mini / OPT with LoRA fine-tuning to generate customer explanations, agent-assist summaries, and automated resolution suggestions.
Deployed scalable AI services on Azure AI Foundry with modular ML pipelines, FastAPI inference endpoints, and CRM integration.
Delivered measurable business impact by improving complaint resolution efficiency, service response prioritization, and customer experience KPIs.

Python (Programming Language)Generative AIMachine Learning

Tata power solar systems limited (rims manpower)

Engineer

Apr 2021 – Mar 2022 · 11 mos · Noida, Uttar Pradesh, India

Project Engineer (Data Science & ML)
Built predictive analytics pipelines for power distribution use cases including demand forecasting, outage impact analysis, and service performance optimization.
Designed end-to-end data preprocessing pipelines (imputation, outlier handling, normalization, categorical encoding) to support production ML workflows.
Developed and evaluated classification models (logistic regression, tree-based models) to predict service disruptions, complaint likelihood, and operational delays, optimizing precision, recall, and F1-score.
Conducted large-scale EDA and KPI analysis, translating model insights into actionable recommendations for operations and engineering teams.
Contributed to data-driven decision-making improving service reliability and operational planning.