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AI Engineer with less than a year in LLM Integration & RAG Architectures
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Software Engineer with hands-on experience designing object-oriented, FAST API-based systems using Python. Skilled in relational database design (PostgreSQL, SQL), LLM integration, and RAG architectures. Strong foundation in software development, backend engineering, API integration, debugging, and problem-solving, with proven ability to troubleshoot application-level issues and build scalable, maintainable solutions following software design principles.
National Institute of Technology Karnataka
Bachelor of Technology · Electronics and Communication Engineering
November 1, 2022 – May 1, 2026
Pharma ACE
Trainee Engineer – AI
January 1, 2026 – Present
Pune, Maharashtra, India
Career Intelligence: AI-Powered Job Matching Platform
January 1, 2026 – June 1, 2026
• Developed an AI platform integrating 8 Applicant Tracking and Talent Acquisition systems, including Workday, Greenhouse, Lever, Ashby, SmartRecruiters, BambooHR, iCIMS, and Adzuna, using a configurable adapter pattern. • Built an LLM-powered resume parser using NVIDIA LLAMA 3.1 8B Instruct to extract structured candidate profiles from PDF/DOCX resumes. • Designed a hybrid matching engine combining semantic similarity and keyword scoring to rank job-candidate fit across technical and non-technical roles. • Designed and tested a plugin-based connector registry following the Open/Closed Principle, enabling new system integrations without modifying core logic. • Generated ranked Excel reports with experience and location filters using openpyxl to support reporting and workflow review.
ContextCore: Distributed RAG Knowledge Platform
January 1, 2026 – June 1, 2026
• Built a multi-tenant RAG platform that ingests documents through Kafka pipelines and answers questions using semantic search and LLAMA 3.1 8B via NVIDIA NIM API. • Designed an end-to-end NLP pipeline with PDF/DOCX parsing, chunking, sentence-transformer embeddings, and Qdrant-based vector retrieval. • Integrated NVIDIA NIM LLaMA 3.1 8B Instruct for grounded answer generation with source citations, reducing hallucinations through prompt engineering and testing. • Implemented Prometheus and Grafana monitoring to support production issue resolution by tracking ingestion lag, query latency, and cache hit rates. • Containerized services with Docker Compose and built a GitHub Actions CI/CD pipeline for automated linting, testing, and build validation.
Cultural Fit Analysis
The candidate's academic projects and internship demonstrate a strong interest and practical application in AI engineering, aligning well with an AI Engineer role. The diversity of projects, from job matching to RAG knowledge platforms, shows adaptability and a broad understanding of AI applications. The use of various technologies and concepts like multi-tenancy and CI/CD indicates a proactive learning attitude and a desire to build robust systems.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a structured approach to problem-solving and a focus on building maintainable solutions. Collaboration with senior engineers and contributions to CI/CD pipelines suggest an operational fit for team environments. The detailed project descriptions also imply good communication skills in conveying technical work.