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AI Engineer with less than a year in Agentic AI & Machine Learning
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AI/ML Software Engineer with 0.9 years of experience, specializing in autonomous signal generation and trading automation platforms. Proficient in designing and deploying intelligent data backbones, building weighted decision engines, and implementing agentic risk controls. Skilled in leveraging Python, LLMs (LangChain, Google ADK, OpenAI API, Gemini API), and various AI/ML libraries to orchestrate end-to-end workflows and deliver real-time data solutions.
Bharath Institute of Higher Education and Research
Bachelor of Technology · Computer Science & Engineering
August 1, 2021 – June 30, 2025
GenAI Lakes
Software Engineer — AI/ML (Full-Time)
July 1, 2025 – Present
Hyderābād, Telangana, India
LLM Research Automation Pipeline — Agentic RAG System
June 27, 2026 – Present
Built a modular agentic AI pipeline using Google ADK and LangChain where autonomous agents handle multi-step reasoning: document ingestion, semantic chunking, FAISS vector indexing, and RAG-based retrieval over large unstructured corpora demonstrating practical tool calling and planning system design. Integrated ROUGE-metric evaluation and human-judgment baselines to benchmark LLM summarization quality; implemented retrieval caching to reduce repeat-query latency on large document sets. Designed swappable LLM backends (OpenAI/Gemini), configurable chunking strategies, and pluggable data acquisition — extensible agentic architecture enabling reuse across different document domains.
View ProjectGlobal Trade Analytics Dashboard - NeoNest
June 27, 2026 – Present
Built an interactive analytics dashboard over WTO/UN trade datasets covering 12 categories using Python, Pandas, and Plotly/Dash; surfaced multi-year trend analysis and identified statistically underutilised trade corridors. Designed an end-to-end data pipeline handling heterogeneous source formats, normalising country/category taxonomies and producing a responsive visualisation layer — demonstrating data engineering fundamentals.
View ProjectOracle Cloud Infrastructure 2024 Generative AI Certified Professional
Oracle
January 1, 2024 – Present
Principles of Generative AI
Infosys Springboard
January 1, 2024 – Present
SQL and Relational Databases
IBM
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's projects and experience show a strong alignment with an innovative, fast-paced environment, particularly in AI/ML and FinTech. Their proactive pursuit of certifications and involvement in complex, production-grade systems (Stock Analysis Platform, FASTTRADE99) suggest a driven and results-oriented individual. The diversity of projects, from LLM research automation to global trade analytics, indicates adaptability and a broad interest in applying AI/ML across different domains. The emphasis on autonomous systems and end-to-end ownership aligns well with a culture that values initiative and comprehensive problem-solving.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex system design (e.g., autonomous multi-signal scoring engine, agentic trading automation). Their project descriptions suggest an ability to work independently and take ownership of end-to-end solutions. The focus on modular and extensible architectures indicates good design principles and foresight for maintainability and scalability. The experience with real-time systems and risk controls points to an operational mindset focused on reliability and robustness.