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AI Research Engineer with 1+ years in Machine Learning & GenAI
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Assessing your cultural and operational fit
AI/ML Engineer with experience across classical ML, deep learning, GenAI, and agentic AI in advertising, manufacturing, aerospace, and healthcare. Skilled in end-to-end ML lifecycles, Python, SQL, PySpark, and cloud deployment (AWS SageMaker, Azure, Databricks) using TensorFlow, PyTorch, Hugging Face, with expertise in LLM fine-tuning, RAG, and LangChain, LangGraph, and LangSmith.
Vellore Institute of Technology Andhra Pradesh
B.Tech · Computer Science and Engineering
N/A – June 30, 2026
MiQ Digital
PDS Intern Product Data Science
January 1, 2026 – Present
Mumbai, Maharashtra, India
Tvarit GmbH
Data Scientist Intern - Data Science & Analytics
May 1, 2025 – July 1, 2025
Mumbai, Maharashtra, India
ISRO SHAR SDSC
Data Science Research Intern Research Division
December 1, 2024 – January 1, 2025
Sūlūru, Andhra Pradesh, India
Intraintech-SkillCepha
Data Scientist Intern - ML Engineering
January 1, 2024 – March 1, 2024
India
PCOS Detection (Medical AI)
June 24, 2026 – Present
Built CNN ultrasound classifier (96% accuracy, 0.94 F1) on 3,000+ images; ResNet/EfficientNet transfer learning with LIME/SHAP for clinician-ready explainability.
TSA Agent Evaluation
June 24, 2026 – Present
Production harness for agent routing, tool selection, and parameters; 100% rule-based precision on 335 queries and ~92% Qwen LLM-judge agreement via LangSmith.
Landslide Hazard Mapping
June 24, 2026 – Present
Trained Transformer-UNet on NASA/Beijing satellite imagery (98% accuracy) with geospatial ETL and custom loss for automated risk-tier early warning.
DA2C (Distributional RL)
June 24, 2026 – Present
Distributional A2C improving task completion 35% and policy stability 28% vs. A2C/PPO/DQN; published at IEEE ICITEE 2025 (IEEE Xplore).
AWS Certified Cloud Practitioner
AWS
June 1, 2026 – Present
Microsoft Azure AI Engineer Associate (AI-102)
Microsoft Azure
June 1, 2026 – Present
IEEE ICITEE 2025 publication
IEEE
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio across various domains (medical, aerospace, manufacturing, advertising) and academic background indicates a broad interest and adaptability, which aligns well with a dynamic research environment. Their involvement in community leadership and mentorship roles suggests a collaborative mindset. The blend of academic research (RL, NLP) and practical application (productionizing models, agentic AI) shows a balanced approach that could fit well into an AI Research Engineer role.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project work, particularly in optimizing models and pipelines. Their involvement in academic publications and leadership roles (Google Developer Groups, Stanford Technical Mentor) suggests good communication and collaboration potential. The focus on explainability (LIME/SHAP) and validation (LangSmith) indicates an understanding of operational requirements for production AI systems.