Dear Candidate,

Please find the job description below,

Senior Machine Learning Engineer (NLP)

An experienced, production-focused Machine Learning Engineer specializing in Natural Language Processing (NLP) and high-performance backend systems for the academic publishing domain.

Responsible for the full ML lifecycle — from data labeling and model training to deploying concurrent, production-grade inference APIs that process complex textual data at scale.

Areas of Knowledge & Expertise

1. Advanced Natural Language Processing & Information Extraction

Building, extending, and training custom spaCy (v3.6+) pipelines and components (NER, SpanCat, SpanFinder, custom tokenizers). Experience with SOTA frameworks such as Thinc, Flair, and zero-shot architectures like GLiNER. Knowledge of embedding generation, vector spaces, and up-to-date embedding models (e.g., google/embeddinggemma-300m). Mastery of fuzzy matching (Levenshtein distance), regex, and structured data formats (XML, PDF, DOCX, LaTeX), with a focus on academic manuscript structures and metadata standards.

2. Deep Learning & Model Optimization

Proficiency in PyTorch 2.x and PyTorch Lightning for reproducible model training across hardware accelerations (CUDA, MPS, CPU). Hands-on experience with the Hugging Face ecosystem for transfer learning and fine-tuning of LLMs and encoder-based transformers. Solid foundation in statistics, numerical computing (NumPy, SciPy, Pandas), and classical ML algorithms (e.g., DBSCAN clustering, Scikit-Learn pipelines).

3. High-Performance ML Operations & Backend Engineering

Advanced Python development using Asyncio and FastAPI to build high-throughput,



low-latency REST APIs. Understanding of the GIL, multi-threading, and memory/thread safety when serving heavy ML models in production web servers.

4. MLOps, Cloud & Data Lifecycle

Managing the data lifecycle with tools like Prodigy and spaCy Projects for active learning and gold-standard datasets. Using MLflow for experiment tracking, model registry, and reproducibility. Deploying and managing applications on GCP (Compute Engine, GKE, Vertex AI, Gemini API). Writing robust test suites (pytest, pytest-asyncio) and managing CI/CD via GitHub Actions, Jenkins, Docker, and Kubernetes. Monitoring via ELK and Dynatrace, and handling data streams via Kafka.

Technologies

Core

- Python 3.7+

- FastAPI, Uvicorn, Starlette, Pydantic v2 + pydantic-settings

- pytest, pytest-asyncio (unit + functional tests)

- asyncio, aiohttp

- Project packaging (setuptools + pyproject.toml)

- REST API development

ML

- PyTorch 2.x (CPU/GPU/MPS)

- CUDA 12.*+

- PyTorch Lightning

- Hugging Face Transformers

- Sentence_transformers

Classical ML

- scikit-learn, NumPy, Pandas, SciPy, matplotlib

- JupyterLab — exploration and training notebooks

- Basic knowledge of Linear Algebra

NLP

- Custom NLP pipeline design (spaCy, incl. transformer-encoder pipelines)





- Custom spaCy components: NER, SpanCat, Dependency parser, Sentencizer, SpanFinder, custom tokenizers/matchers

- Thinc, Flair NLP, GLiNER

- Retrieval and embedding models (e.g., google/embeddinggemma-300m)

Data processing

- spaCy Projects

- Prodigy

Text processing

- Fuzzy search

- Regex

- XML processing

LLM

- LLM-assisted data annotation with quality guards

- LLM inference servers: vLLM, Hugging Face TGI, llama.cpp

- Async Python (AsyncOpenAIClient) for high-throughput dataset processing

Cloud & integration

- Docker

- GitHub Actions

- GCP: Artifact Registry, GCS, Compute Engine, GKE, Logging, Vertex AI, Gemini API

- Jenkins, K8s, Dynatrace, ELK

- Apache Kafka

- MLflow

- Git

General knowledge

- NER and span classification

- Sequence labeling and document-level classification

- Transfer learning / fine-tuning transformers

- Train/eval/deploy lifecycle

- Data augmentation, information extraction, relation extraction, entity linking, information retrieval

- Fuzzy matching, clustering (DBSCAN)

- Precision/recall tradeoffs in information extraction

- Thread safety serving models in multi-threaded web servers

- XML processing; basic knowledge of doc(x), LaTeX, PDF formats

- Academic publishing domain — manuscript structure, metadata standards

Other tech as a plus

- Java, JavaScript

Kindly share the details below.
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Regards,
Priyanka Bhosale

📌 Urgent Opening For Senior Machine Learning Engineer (NLP)- Pune
🏢 Virtusa
📍 Pune