Bangalore · On-site

Software Engineer (ML Engineering)

Full-timePosted 30 Oct

Salary: ₹21.4–24.7 LPAEstimated from AmbitionBox data for this role at Visa Inc.. The employer has not stated pay.
Experience: 1–2 years
Eligible batches: 2024 · 2025
Location: BangaloreOn-site

About the role

Visa is looking for a Software Engineer focused on machine‑learning engineering to join its Bengaluru office. The role involves building, training, and deploying ML models, creating data pipelines, and integrating intelligent services into Visa’s payment platforms.

Responsibilities

Design, write, and review production‑grade code for data, model training, and inference layers.
Develop and maintain scalable feature‑engineering pipelines and large‑scale data‑processing jobs using cloud services.
Train, tune, and evaluate ML models while tracking experiments and documenting results.
Package and serve models in production with containerization, orchestration, and model‑serving frameworks.
Implement robust testing (unit, integration, end‑to‑end) and CI/CD pipelines for code and model releases.
Monitor deployed models, investigate drift or incidents, and apply fixes using observability tools.

Free interview prep

Prepare for the interview

All lists

Picked for this role's title and skills: the questions that come up most, each with a short answer to say out loud.

Machine learning interview questions40 questions · ≈ 18 min to revise

Asked most often · 19 of 40

Python interview questions40 questions · Data structures, Functions, OOP

Eligibility

Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics or a related quantitative discipline.
1‑2 years of professional experience in software or ML engineering.
Demonstrated ability to write, debug, and review production code.
Experience with secure coding practices and regulatory compliance.

Skills

Proficiency in Python and Go.
Hands‑on experience with ML frameworks such as scikit‑learn, PyTorch or TensorFlow.
Knowledge of data‑pipeline tools (Airflow, Dagster, Kubeflow) and cloud platforms.
Familiarity with containerization (Docker) and orchestration (Kubernetes).
Experience building, testing, and deploying models using serving stacks like Triton or TorchServe.
Regular use of LLM‑based or agentic tools for development and evaluation.