AJJ

Alluri Jeswanth Joel

AI Engineer

https://www.opentalent.in/alluri-jeswanth-joel

AI Engineer with 1+ years in Machine Learning & Full-stack Development

Key Strengths

Strong foundational knowledge in AI/ML concepts, including RAG, vector similarity, and embeddings.
Demonstrated ability to design and implement complex AI systems with performance considerations (e.g., latency benchmarking, async pipelines).
Proficiency in a wide range of relevant technologies for AI/ML, MLOps, and backend development (Python, FastAPI, MLflow, Docker, various vector databases).
Experience with robust system design patterns like multi-tenant architectures, concurrency controls, and audit trails.
Good understanding of deployment strategies and CI/CD pipelines.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's academic projects demonstrate a strong interest and practical application in AI/ML, which aligns well with an AI Engineer role. The diversity of projects (AI hiring platform, RAG, task management) shows a broad technical curiosity and ability to adapt to different problem domains. The use of various modern technologies and cloud platforms suggests a proactive learning attitude. However, as an academic candidate, real-world team collaboration and corporate cultural fit are yet to be fully demonstrated.

Soft Skills & Operational Fit

The candidate's project descriptions indicate a results-oriented approach with a focus on performance metrics (latency benchmarking). The academic projects suggest an ability to work on complex, multi-faceted problems. The leadership role as an organizer for a college event indicates potential for teamwork and coordination, though direct operational fit for a senior engineering role is not explicitly detailed.

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Assessing your cultural and operational fit

About

Highly motivated Computer Science and Engineering student with 1.75 years of experience in AI/ML engineering, full-stack development, and high-performance API design. Proficient in Python, Java, JavaScript, and modern frameworks, with a strong background in building and deploying scalable, data-intensive applications. Demonstrated ability to lead projects, optimize performance, and implement robust security measures, as evidenced by contributions to projects like Neural OPS, Simplify RAG Platform, and TaskFlow API.

Top Skills

PythonJavaScriptJavaDocker

Projects

Neural OPS – Enterprise AI Hiring & Candidate Intelligence Platform

May 1, 2026 – Present

Designed a hybrid multi-tenant ranking architecture combining Qdrant vector similarity, skill-match scoring, and experience signals; orchestrated async ingestion and embedding via Celery workers with experiments tracked in MLflow. Benchmarked candidate-matching workloads across 241 telemetry runs under concurrent stress testing, achieving a median matching latency of 28.31 ms. Implemented Redis-backed concurrency controls and LLM budget governance; instrumented telemetry pipelines for latency benchmarking under concurrent workloads.

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Simplify - Citation-Enabled RAG Platform

April 1, 2026 – Present

Built an async document ingestion pipeline covering file validation, text extraction, overlap chunking, Gemini embedding generation, and Pinecone vector upserts; benchmarked 542 ingestion runs with median latency 24.20 ms and p95 below 353 ms. Integrated citation-backed response generation with Gemini streaming; secured with JWT auth (refresh-token rotation, access-token denylisting) and email OTP with rate limiting. Deployed frontend on Vercel and FastAPI backend on Render; persisted metadata in MongoDB Atlas with Supabase Storage for raw file management.

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TaskFlow – High-Performance Task Management API

January 1, 2025 – Present

Achieved p95 request latency below 50 ms and built JWT-secured REST APIs with RBAC (ADMIN/USER) and JPA Specification-based dynamic filtering across status, priority, assignee, and keyword fields. Implemented an immutable TaskHistory audit trail logging every field-level mutation with actor and timestamp; instrumented benchmark telemetry across auth and request pipelines. Containerized with multi-stage Docker builds; GitHub Actions CI/CD runs JUnit 5 + Mockito tests and publishes Swagger UI on every push.

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Key Strengths

Strong foundational knowledge in AI/ML concepts, including RAG, vector similarity, and embeddings.
Demonstrated ability to design and implement complex AI systems with performance considerations (e.g., latency benchmarking, async pipelines).
Proficiency in a wide range of relevant technologies for AI/ML, MLOps, and backend development (Python, FastAPI, MLflow, Docker, various vector databases).
Experience with robust system design patterns like multi-tenant architectures, concurrency controls, and audit trails.
Good understanding of deployment strategies and CI/CD pipelines.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Alluri Jeswanth Joel

Key Strengths

Cultural & Operational Fit

About

Top Skills

Skills

Education

Projects

Certifications

Key Strengths

Cultural & Operational Fit