AI Engineer with 5+ years in Generative AI, MLOps, and Deep Learning
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AI Engineer with 5.2 years of experience in developing and deploying intelligent systems. Expertise in Generative AI, LLMs, MLOps, Deep Learning, and backend infrastructure. Proficient in Python and SQL, with a proven track record in architecting high-concurrency systems and building real-time data pipelines. Strong background in creating robust, traceable, and scalable AI solutions for complex operational challenges.
Muthoot Institute of Technology and Science
B.Tech · Computer Science (Artificial Intelligence)
November 1, 2020 – May 1, 2024
TATA ELXSI LTD
AI Engineer
December 1, 2024 – Present
Thiruvananthapuram, Kerala, India
TATA ELXSI LTD
AI Engineer
November 1, 2020 – May 1, 2024
Thiruvananthapuram, Kerala, India
Framework for Sparse Generative Pre-Trained Transformer
June 24, 2026 – Present
Engineered a lightweight, versatile GPT framework from scratch using PyTorch implementing sparse attention mechanisms to reduce computational overhead. Achieved a 25% acceleration in training speed compared to standard GPT-2 architecture. Research accepted at ICMLBDA 2024 for SCOPUS-indexed SPRINGER Proceedings in Mathematics and Statistics.
WorkPulse
June 24, 2026 – Present
Served an XGBoost model via FastAPI with Celery + Redis async dispatch, maintaining sub-150ms latency end-to-end. Containerized the full stack with Docker and deployed to Azure VM via GitHub Actions CI/CD with Nginx reverse proxy. Enabled zero-downtime rollbacks using MLflow Model Registry with alias-based versioning. Stress-tested to 20 concurrent users with zero failures; monitored live health via Prometheus + Grafana.
View ProjectResearch accepted at ICMLBDA 2024 for SCOPUS-indexed SPRINGER Proceedings in Mathematics and Statistics.
SPRINGER Proceedings
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's experience with both academic research and practical industry projects (TATA ELXSI LTD, WorkPulse) demonstrates a versatile approach to problem-solving. The breadth of technologies used (PyTorch, TensorFlow, YOLO, FastAPI, Docker, Azure) indicates adaptability and a willingness to learn and apply diverse tools. The focus on real-time systems and robust deployments aligns well with a fast-paced, results-oriented environment.
Soft Skills & Operational Fit
The candidate's project descriptions and experience highlight strong problem-solving skills, an ability to work with complex technical challenges, and a focus on performance and reliability. The detailed descriptions suggest good communication of technical concepts. The academic publication indicates a capacity for independent research and contribution to the field.