Junior Machine Learning Engineer

Junior Machine Learning Engineer, Gandhi Nagar — Vijayawada A company in Gandhi Nagar is hiring a Junior Machine Learning Engineer for a full-time, on-site role. This is an entry-level position for someone with roughly a year of hands-on experience, not a first-time internship. The team wants someone who has actually trained and deployed a model before, even a small one, rather than someone who has only completed coursework. The work here sits close to the data itself. A typical week involves pulling datasets together, cleaning out the parts that don't hold up, training a model against them, and then figuring out why it performs worse in production than it did in testing. That last part comes up more often than most junior engineers expect, and getting comfortable with it early tends to matter more than any single algorithm learned in school. Day-to-day responsibilities include: Building and training predictive models based on business requirements Digging through large datasets to spot trends that inform product or business decisions Deploying trained models into production systems Monitoring model performance over time and flagging when accuracy starts to drift Model drift is one of those problems nobody explains well in a classroom. A recommendation model that performed fine at launch can quietly get worse over a few months as user behavior shifts underneath it, and nothing about the code itself has to change for that to happen. Part of this role is checking for that kind of slow decay rather than assuming a working model stays working. Python is required, along with practical experience in either TensorFlow or PyTorch, a solid grip on core machine learning algorithms, and enough SQL to pull and shape data without help. Candidates who've built something end-to-end, even a personal project that went from raw data to a working deployed model, tend to interview better here than someone with a longer list of completed courses but no shipped work. A few extra skills would help but aren't required: exposure to a cloud ML platform such as AWS SageMaker or Google Vertex AI, some familiarity with version control workflows for model code, and basic comfort with Docker for packaging a model for deployment. A four-year degree is the baseline education requirement here. The company doesn't lock this down to one specific major, but something numerically heavy, whether that's a computing background, a math- or stats-heavy program, or an engineering degree with strong analytical coursework, tends to line up best with what the job actually demands. Hiring managers here have made exceptions for candidates from other backgrounds who could clearly demonstrate the technical skills through past projects. On the experience side, the company is asking for 12 months in a similar role or equivalent applied project work, which for most applicants means at least one prior job, internship, or serious independent project that involved putting a model into actual use. Pay for this role goes up to ₹60,500 a month, on-site, full-time. Naukri Mitra has listed a growing number of machine learning roles in Vijayawada over the past year as more companies build data teams outside the bigger tech hubs, and this one sits toward the entry end of that range. Benefits include: Health insurance Paid time off Provident fund contributions Relocation assistance and accommodation support for candidates moving to Vijayawada Gandhi Nagar is a fairly central part of the city, and the office sits within a short auto ride of most nearby residential areas, which keeps the daily commute manageable for people who relocate specifically for this job. The data team is small right now, just three people including this hire, so a junior engineer here ends up working directly with a senior data scientist rather than sitting several layers removed from the more experienced staff. Interviews for this role generally include a take-home exercise with a small, realistic dataset, followed by a conversation walking through the choices made while building the solution. The company cares more about how someone reasoned through handling messy or incomplete data than whether the final model hit a specific accuracy number, since that reasoning tends to predict how someone will handle real production problems later. New hires typically spend their first few weeks getting familiar with the existing codebase and current production models before taking on anything independently. That ramp-up period is treated as normal onboarding rather than a probation period, and most people are contributing to live projects within a month or so. Applications are reviewed on a rolling basis rather than against a fixed closing date. Candidates outside Vijayawada who are open to relocating should mention that directly in their application, since the relocation support is discussed early in the process rather than left until an offer is made. The team's current stack leans on Python for almost everything, with model training happening either locally or on a shared GPU machine depending on dataset size. Notebooks are used for exploration, but anything going into production gets rewritten as proper scripts with version control, since a notebook that only runs on one person's laptop tends to become a problem the moment someone else needs to touch it. Getting comfortable with that transition, from messy exploratory code to something a teammate can actually run, is one of the harder adjustments for people coming straight from academic projects. Data quality issues show up more often than model architecture problems in practice. A batch of customer records with mismatched date formats or a sensor feed that occasionally reports null values can quietly break a training run in ways that are harder to diagnose than a straightforward coding bug. Learning to spot these issues before they corrupt a model's output is treated as a core skill here, not a side task handled by someone else. Career growth from this position usually leads toward a mid-level machine learning engineer role within the same team, with more ownership over model architecture decisions and less day-to-day supervision. A couple of people who started in junior roles similar to this one have moved into that next tier within roughly eighteen months, which gives a rough sense of pace without promising a fixed timeline for anyone new coming in.