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ML Engineer with less than a year in NLP, distributed training, and model deployment.
Aspiring Machine Learning Engineer with a Bachelor’s degree in Computer Science and hands-on experience building automated data pipelines, processing large-scale datasets, and deploying scalable model-serving infrastructure. Proficient in Python and deep learning frameworks including TensorFlow and JAX, with applied knowledge of autoregressive sequence models and Transformer architectures. Passionate about applied NLP, large-scale distributed training, and continual learning on streaming data. Eager to collaborate with senior ML researchers and product teams to train safer, better, and faster models.
Bharath Institute of Higher Education and Research
B.Tech in Computer Science Engineering · Computer Science Engineering
August 1, 2021 – June 30, 2025
Pentagon Space
Engineering Analyst — Software & Application Support (Intern)
March 1, 2025 – September 1, 2025
Bengaluru, Karnataka, India
Automated Flight Telemetry Data Pipeline
June 1, 2026 – Present
Designed and implemented an automated Python data ingestion pipeline leveraging BeautifulSoup and streaming parsers to extract, parse, and filter large-scale aeronautical text data streams and telemetry payloads. Processed unstructured telemetry streams into clean, structured training-ready datasets, applying anomaly detection and validation metrics — analogous to preprocessing massive datasets for large-scale model training. Built database layers in a relational SQL engine to securely manage parsed data packets, achieving optimized querying speeds via schema normalization.
Infrastructure Configuration Auditor & Microservice
June 1, 2026 – Present
Built an asynchronous REST API inference-monitoring network using Python’s FastAPI engine to track model container health and remote pipeline statuses — reflecting model-serving architecture principles used in TF-Serving deployments. Engineered modular, shared front-end interface components using HTML, CSS, and JavaScript to yield interactive status panels for infrastructure compliance monitoring. Controlled code logic within modern IDE setups using Git version control, verifying continuous system maintenance, structured branching, and high test coverage.
Miniature Autoregressive Language Model
June 1, 2026 – Present
Designed and trained a decoder-only Transformer language model from scratch using PyTorch, implementing tokenization, multi-head self-attention, positional embeddings, and causal masking on a custom text corpus. Re-implemented the same model in JAX using jit-compiled forward passes and vmap for batched gradient computation, achieving measurable throughput gains over the vanilla PyTorch baseline; explored XLA-level graph optimizations. Extended the model with online fine-tuning on streaming text batches, experimenting with elastic weight consolidation (EWC) to mitigate catastrophic forgetting on sequential task domains.
NLP Sentence Similarity & Classification Engine
June 1, 2026 – Present
Fine-tuned a pre-trained BERT model (via Hugging Face) on a custom dataset for semantic sentence similarity and multi-class intent classification, achieving strong benchmark performance with efficient transfer learning on limited labeled data. Exported the fine-tuned model as a SavedModel and served it via TensorFlow Serving (TF-Serving), implementing REST inference endpoints with batched request handling and latency benchmarking. Implemented an active learning annotation loop using uncertainty sampling on model softmax outputs to iteratively label the most informative streaming samples, reducing labeling effort by ~40% while maintaining classification accuracy.
Python for Automation & Data Processing
Unknown
June 1, 2026 – Present
Microsoft Azure Fundamentals (AZ-900)
Microsoft
June 1, 2026 – Present
Deep Learning Specialization
Coursera — deeplearning.ai
June 1, 2026 – Present
Natural Language Processing with Transformers
Hugging Face
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from data pipelines to NLP engines and custom language models, indicates a broad interest and adaptability, which aligns well with dynamic ML environments. Their self-development efforts in advanced ML topics like Transformer architecture study, JAX/XLA exploration, and large-scale training strategies show a strong passion for the field and a proactive learning mindset. The focus on applied NLP and distributed training aligns directly with the target ML Engineer role, suggesting a good cultural fit for a team focused on cutting-edge ML development.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical problem-solving skills, as evidenced by contributions to system logic models and root cause analysis during their internship. Their participation in Agile/Scrum sprints indicates an ability to work within structured team environments and contribute to continuous system maintenance. The detailed project descriptions suggest a proactive and self-driven approach to learning and applying complex technical concepts.