AI/ML Engineer
Building Production-Ready AI Agents and ML Solutions — from LangGraph multi-agent pipelines and RAG systems to CNN-based computer vision. B.Tech AIML student turning research into real products.
I'm Ashish Soni, a B.Tech AI/ML student from Delhi, India (GGSIPU, CGPA 9.19) building production-grade agentic systems, RAG pipelines, and computer vision applications.
My work spans Agentic AI (LangGraph multi-agent pipelines, MedGuard research), LLM Engineering (RAG, LangChain, ChromaDB, Groq API), and Computer Vision (CNN image captioning, deepfake detection, YOLOv8-Pose).
I'm conducting independent research on multi-agent LLM reasoning for medical false alarm reduction, targeting IEEE Access — a 5-agent LangGraph pipeline using YOLOv8-Pose and Gemini 2.0 Flash.
Seeking AI/ML internship roles at product companies, research labs, and AI-first startups.
From first Python scripts to production multi-agent systems — the milestones that shaped the engineer.
Enrolled in B.Tech AIML at GGSIPU. Dove into Python, statistics, linear models, decision trees, and ensemble methods. First real datasets, first Kaggle submissions.
Built CNN pipelines (MobileNetV3, Xception, EfficientNetB4), trained LSTM/RNN sequence models, and shipped the Deepfake Face Detector and Image Captioning Model (Xception + LSTM, Flickr8k).
Mastered transformer architecture, fine-tuning, and embedding-based retrieval. Built PDF Chat Assistant — full RAG pipeline: HuggingFace embeddings → ChromaDB → Groq LLaMA 3. Deployed on HuggingFace Spaces.
Designing autonomous multi-agent systems: AI Resume Screener (4-agent LangGraph), AI Research Agent (Search → Scrape → Write → Critic), and MedGuard (5-agent pipeline targeting IEEE Access). Production deployments live.
Agentic AI & LLM Engineering on the left · Computer Vision & Audio ML on the right.
Multi-agent LangGraph pipeline screening resumes using LLM reasoning — extracts structured candidate info and produces tech proficiency scores with detailed recruiter reports.
4-node stateful LangGraph pipeline — Search, Scrape, Write, Critic — with conditional edge routing for autonomous web research and structured report synthesis with auto-rewrite loop.
Full RAG pipeline: recursive text splitting → HuggingFace sentence-transformer embeddings → ChromaDB vector storage → top-k retrieval → LLaMA 3 via ConversationalRetrievalChain. Deployed on HF Spaces.
5-agent LangGraph architecture for real-time emergency detection. YOLOv8n-Pose extracts pose keypoints; a 10-frame rolling buffer feeds Gemini 2.0 Flash for chain-of-thought reasoning over trajectories.
CNN binary classifier detecting real vs AI-generated faces. Multi-model comparison: MobileNetV3Large, Xception, EfficientNetB4 with transfer learning, fine-tuning, and automatic best-model selection.
Music genre recognition using MFCC and Mel spectrogram feature extraction. Classifies rock, jazz, classical, pop, and more — capturing frequency, rhythm, and tonal structure far better than raw waveforms.
End-to-end image-to-text system trained on Flickr8k (8K images, 40K captions). Generates natural language descriptions of photographs using Xception CNN encoder and LSTM decoder with beam search.
Manuscript in preparation — targeting IEEE Access publication.
Designing a 5-agent LangGraph pipeline (Perception → Context Memory → LLM Reasoning → Decision → Action) to suppress false positives in camera-only out-of-hospital emergency detection, replacing brittle rule-based threshold logic. Gemini 2.0 Flash performs chain-of-thought reasoning over pose trajectories and scene context. Asymmetric thresholds (Tescalate = 0.60, Tsuppress = 0.85) minimise false negatives in safety-critical escalation decisions. Empirically comparing traditional rule-based AI against the 5-agent LangGraph system.
From raw data preprocessing to production multi-agent pipelines.
I'm actively looking for AI/ML internship opportunities to work on real production systems alongside experienced engineers. If you're building something meaningful with AI — a product, research, or startup — I'd love to contribute.
Available for remote and on-site roles across India. I reply within 24 hours.
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