This is a full-time, on-site opening for an NLP Engineer based in the Hebbal Industrial Area in Mysore, Karnataka, with a salary up to ₹1,05,000 per month.
The position sits inside a Data Science & Machine Learning team that spends most of its time turning unstructured text into something a business can actually act on. Support tickets, product reviews, internal documents, call transcripts: whatever the current project needs, someone has to get it into a usable shape before any model comes near it.
What the role covers
An NLP engineer here isn't tuning models in isolation. The job runs from raw text through to a recommendation someone in the business can use, so a fair amount of the week goes into understanding what a dataset actually contains before deciding what's worth building on top of it. This particular opening leans more applied than research-heavy, closer to shipping a working pipeline than to publishing a paper on it.
Most projects start small, tied to a narrow question about a specific dataset, and grow from there once an initial pass shows something worth pursuing further. A model that looked promising on a sample of five hundred records might need to hold up against fifty thousand messier ones before it earns a place in production, and that gap is usually where most of the real engineering happens. Work happens mostly in Python notebooks during the early exploration stage, then moves into scripted, version-controlled code once a pipeline needs to run repeatedly rather than just once for a demo.
Responsibilities
- Pull raw text from internal systems and outside sources, then organize it into a shape a model can actually use
- Build and fine-tune NLP models using libraries such as spaCy and NLTK
- Run statistical and machine learning methods over the data to surface trends and irregularities in the language
- Translate technical results into plain terms for colleagues without a data background
- Turn what the models and analysis show into concrete, actionable calls for the business
The cleaning step tends to take longer than people expect coming into this kind of work. Text data rarely shows up tagged and consistent, and a chunk of most weeks goes into resolving encoding issues, inconsistent formatting, or duplicate records before modeling can start in earnest. Version control and a basic experiment log matter more here than the job title might suggest, mostly because it's easy to lose track of which cleaning step actually fixed a downstream problem.
Qualifications and experience
The education bar here sits at a bachelor's degree, most often in computer science, statistics, or another quantitative discipline. It doesn't need to be a specialized degree in NLP or linguistics, though that background helps during the interview.
Candidates should have around 30 months of experience in a Python-based ML role, with a track record of shipping trained models rather than just prototyping them in a notebook. Some of that time should ideally involve text or language data specifically rather than purely tabular datasets, since the instincts for handling messy language input usually take longer to build than broader ML experience on its own. Prior exposure to a regulated or customer-facing industry isn't required, but it does shorten the ramp-up period, since many early questions on this team are about what a piece of text actually means to the business before they're about which model architecture fits best.
Skills
Required for this role are strong Python skills for data processing and model building, experience with NLP libraries such as spaCy and NLTK, a solid grounding in core machine learning concepts, and sufficient basic linguistic knowledge to reason about tokenization, part-of-speech tagging, and sentence structure.
It also helps to have touched transformer-based models like BERT or spaCy's transformer pipelines, spent time on a text classification or information retrieval project, and picked up enough SQL to pull data without waiting on someone else's schedule. None of these are hard requirements, but candidates who arrive with one or two of them tend to get productive faster in the first few months.
Compensation and benefits
Pay for this role goes up to ₹1,05,000 per month, which is close to what an NLP engineer's salary in Mysore looks like for someone with a couple of years of applied experience. Beyond the base salary, the package includes health insurance, paid time off, and provident fund contributions.
Since the role is on-site rather than remote or hybrid, the employer also covers relocation assistance and provides accommodation support for candidates who need to move to Mysore to take the job. Anyone weighing an NLP engineer job opening in Mysore against a fully remote role elsewhere should factor in that support when making the comparison.
Where you'd be working
The office is located in Hebbal Industrial Area, on the northern edge of Mysore. It's an established stretch for tech and manufacturing employers, with reasonable connectivity to the rest of the city for anyone commuting daily rather than relocating from further away. Naukri Mitra tends to see steady interest in openings around this part of the city, partly because there aren't many comparable roles in that specific pocket at any given time.
Team and pace
Deadlines here cluster near the end of each project phase rather than spreading evenly across the calendar, so some weeks are lighter, and others involve a genuine push to get a model or report ready before a stakeholder review. The team is small enough that whoever takes this role will end up explaining technical decisions directly to non-technical colleagues fairly often, rather than routing everything through a manager first.
There's room to grow into a more senior modeling or team-lead track over time, but that path tends to depend more on the quality of shipped work than on tenure alone. New hires typically shadow an existing project for the first few weeks before owning a dataset outright, which gives enough time to learn the internal data conventions without being thrown at a deadline immediately.
How to apply
Candidates should be ready to walk through a recent NLP project during the interview, ideally one where data cleaning took longer than modeling, since that's a fair reflection of the actual job. Along with a resume, include any code samples or notebooks that show direct experience with the libraries and techniques listed above. Applications are reviewed on a rolling basis, and shortlisted candidates are contacted directly for the next round.
