AKR

AMAN KUMAR RAJAK

AI Engineer

https://www.opentalent.in/aman-kumar-rajak

AI Engineer with less than a year in Generative AI & Machine Learning.

Key Strengths

Strong foundational knowledge in AI/ML and Generative AI, demonstrated through project work.
Proficiency in developing end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, and deployment.
Hands-on experience with advanced RAG architectures, hybrid search engines, and multi-agent systems using LangGraph.
Competence in MLOps tools like MLflow, DagsHub, Docker, and FastAPI for productionizing ML models.
Ability to design and implement evaluation dashboards for AI systems, indicating a focus on performance monitoring and improvement.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects showcase a strong interest and practical application in cutting-edge AI/ML technologies, aligning well with an AI Engineer role. The diversity of projects, from network security to enterprise RAG platforms, indicates a broad technical curiosity and ability to adapt to different problem domains. The academic background combined with self-driven certifications suggests a proactive learning attitude. However, the lack of professional experience might require mentorship in a fast-paced corporate environment.

Soft Skills & Operational Fit

The candidate demonstrates strong problem-solving skills through complex project implementations. The detailed descriptions of project architectures and technical choices suggest an analytical mindset and a structured approach to engineering. The use of MLOps tools indicates an understanding of operationalizing ML systems. However, without direct experience or behavioral assessment, specific soft skills like teamwork, leadership, or adaptability cannot be fully evaluated.

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About

Aspiring Data Scientist and ML/AI Engineer with solid academic training and hands-on experience engineering end-to-end intelligent systems. Proven capability in developing production-ready Generative AI solutions, including Multi-Agent Retrieval-Augmented Generation (RAG) platforms using LangGraph, and modular machine learning pipelines. Competent in designing scalable ML pipelines, hybrid search engines, and containerized deployments using Docker. Seeking to apply software engineering best practices and advanced machine learning techniques in a dynamic engineering team.

Top Skills

PythonGithubNlpAnaconda

Education

Silicon University

B.Tech · Computer Science and Technology

August 1, 2022 – June 30, 2026

Dhanbad Public School

Senior Secondary · Class XII

June 1, 2020 – May 31, 2022

Dhanbad Public School

Secondary · Class X

June 1, 2018 – May 31, 2020

Projects

Enterprise Multi-Agent RAG Platform

June 1, 2026 – Present

Engineered an enterprise-grade, local Multi-Agent RAG platform using LangGraph to coordinate retriever, reranker, generator, and citation agents. Implemented a high-performance Hybrid Search engine combining BM25 keyword matching and ChromaDB vector search (Nomic-Embed-Text via Ollama), fused with custom weighted scoring. Integrated a Cross-Encoder Reranker (sentence-transformers/ms-marco-MiniLM-L-6-v2) to re-score top documents, reducing LLM hallucination and enhancing contextual precision. Built an automated citation mapping agent to dynamically inject inline source anchors, linking generated answers directly back to specific document segments. Developed a live Evaluation Dashboard using FastAPI, SQLAlchemy, and SQLite to log queries, capture user thumbs-up/down feedback, and track performance metrics (latency, citation density). Containerized the platform using Docker and Docker Compose for seamless local deployment.

Network Security Phishing Detection

April 1, 2025 – April 1, 2026

Built a modular, production-ready machine learning pipeline for phishing website detection, achieving a classification accuracy of 95%. Developed robust data ingestion, validation, and preprocessing components, storing raw network data and validation schemas in MongoDB. Engineered advanced feature extraction pipelines using Scikit-Learn and NLP techniques to preprocess URLs and metadata. Leveraged MLflow and DagsHub to track experiments, manage model runs, and register optimized classifiers (XGBoost, Random Forest). Created RESTful APIs using FastAPI to serve model predictions asynchronously, and designed an interactive HTML web interface for batch CSV predictions.

Certifications

Training in Data Science, Machine Learning, and Deep Learning using Python

Syllogistek pvt ltd

June 1, 2026 – Present

Core Java Developer Certification

Silan Software

June 1, 2026 – Present

Python Programming Certification

NPTEL

June 1, 2026 – Present

Complete Generative AI course with huggingface and langchain

Udemy

June 1, 2026 – Present

Key Strengths

Strong foundational knowledge in AI/ML and Generative AI, demonstrated through project work.
Proficiency in developing end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, and deployment.
Hands-on experience with advanced RAG architectures, hybrid search engines, and multi-agent systems using LangGraph.
Competence in MLOps tools like MLflow, DagsHub, Docker, and FastAPI for productionizing ML models.
Ability to design and implement evaluation dashboards for AI systems, indicating a focus on performance monitoring and improvement.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit