🚀 AI/ML Engineer | Agentic AI & LLM Systems | M.Tech AI Candidate

I’m an AI/ML Engineer with 2+ years of industry experience and a strong background in LLM-based systems, Agentic AI, and scalable backend engineering. Currently pursuing my M.Tech in Artificial Intelligence, I specialize in building production-grade AI systems that combine reasoning, retrieval, and automation.

I work at the intersection of AI research and real-world deployment, with hands-on experience in:

• LLMs & Transformers – fine-tuning, distillation, prompt engineering
• Agentic AI – multi-agent workflows, reasoning pipelines, tool orchestration
• RAG Systems – vector search, retrieval pipelines, knowledge-grounded generation
• Computer Vision – CNNs, YOLO-based object detection
• ML Engineering – training, evaluation, and deployment pipelines

• LLMs & AI: PyTorch, TensorFlow, Hugging Face, LangChain, OpenAI Agent SDK
• RAG & Vector DBs: Qdrant, embeddings, semantic search
• APIs & Backend: FastAPI, Python
• MLOps: Docker, MLflow, Apache Airflow
• Vision: OpenCV, YOLO
• Databases: PostgreSQL, MongoDB, MinIO

📍 Thiruvananthapuram | Sept 2025 – Present

• Built Agentic AI workflows using LangChain & OpenAI Agent SDK
• Designed RAG pipelines with Qdrant for domain-specific retrieval
• Developed scalable inference APIs using FastAPI & Docker
• Implemented data ingestion & scheduling pipelines with Apache Airflow
• Optimized AI services for low-latency and production reliability

📍 Hybrid | May 2025 – July 2025

• Designed ML pipelines for sensor-based mental health analysis
• Built a YOLO-based object detection system for industrial safety
• Developed a CNN-based doodle recognition system, improving model accuracy through experimentation

📍 Hybrid | July 2022 – Sept 2024

• Built enterprise-grade IAM systems using .NET & Duende IdentityServer
• Implemented OAuth2 / OIDC authentication workflows
• Optimized backend performance with caching & refactoring
• Designed CI/CD pipelines using Azure DevOps
• Collaborated cross-functionally on scalable backend systems

Tech: Python, Qdrant, OpenAI Agent SDK

• Built an Agentic RAG system for intelligent medical assistance

• Designed a 5-layer architecture:

  • Perception
  • Orchestration
  • Reasoning
  • Action
  • Trust & Verification

• Currently developing a HIPAA-compliant system with:

  • Biomedical Knowledge Graphs
  • Explainable reasoning
  • Verifiable outputs

Tech: PyTorch, Transformers

• Built a Transformer model from scratch

• Implemented:

  • Multi-head self-attention
  • Positional encoding
  • Encoder–decoder architecture

• Trained on Catalan → English translation dataset

🏅 Hack Imagine 2025 – Huddle Global (Kerala Startup Mission)

• Built ClipFoundry.ai, an AI-powered video generation agent
• Designed autonomous decision workflows
• Collaborated in a 4-member AI team under 24 hours

🎓 M.Tech in Artificial Intelligence College of Engineering, Trivandrum 2024 – Present | GPA: 8.43

🎓 B.Tech in Computer Science Mar Baselios College of Engineering & Technology 2018 – 2022 | GPA: 7.35

• Ethics in the Era of Generative AI 6th International Conference on Innovative Trends in IT, 2025

• MuteMe: Automatic Audio Playback Control During Emergencies Springer – Advances in Intelligent Systems and Computing, 2022

I’m actively interested in:

• Agentic AI & Multi-Agent Systems
• LLM-based products
• RAG & Knowledge Systems
• AI for Healthcare & Research
• Full-stack AI products

📫 Email: venkiteshsanand1920@gmail.com 🔗 LinkedIn: Venkitesh S Anand