Machine Learning Engineer

Blackbuck Insights, LLC; Bengaluru; On-site; INR 350,000 - 550,000; Full time

Job summary

Blackbuck Insights, LLC is hiring a Senior Machine Learning Software Engineer to lead the development of scalable ML infrastructure, tooling, and platforms across training, testing, deployment, and monitoring. The role bridges research and production with a focus on reliability and scalability, while mentoring junior engineers and promoting best practices in MLOps.

You will drive design of end-to-end ML pipelines, collaborate with data science and product teams, and set high standards for code

Qualifications

• 5–6 years of industry experience in ML engineering, backend engineering, or infrastructure roles supporting machine learning.
• Proficient in Python and one or more systems-level languages (Go, Java, C++).
• Experience building and maintaining ML infrastructure (model registries, training orchestration, distributed data pipelines).
• Familiarity with containerization and deployment technologies (Docker, Kubernetes, AWS SageMaker, Vertex AI, etc.).
• Hands-on experience with modern MLOps frameworks (MLflow, Meta-flow, TFX, Kubeflow, etc.).
• Demonstrated mentorship experience, with direct support for the growth and promotion of junior engineers.

Responsibilities

• Architect, build, and maintain reusable components and tools to support model training, evaluation, and deployment at scale.
• Optimize model serving frameworks, feature stores, data pipelines, and CI/CD systems for ML workflows.
• Ensure reliability, observability, and performance across ML systems in production.
• Lead cross-functional engineering initiatives involving platform stability, experimentation infrastructure, or real-time inference systems.
• Review code, propose architectural improvements, and uphold software engineering best practices within the ML engineering team.
• Drive design and implementation of MLOps pipelines, automation, and model governance workflows.
• Work closely with ML researchers to produce experimental models, ensuring compatibility with existing infrastructure.
• Coordinate with data engineering to integrate pipelines, data validations, and model input/output schemas.
• Contribute to product engineering discussions when ML systems require edge optimization, user facing API integrations, or UI-linked inference.
• Mentor ML Software Engineers I and II, with a proven track record of advancing at least one MLSE I to MLSE II.
• Contribute to internal documentation, architecture reviews, and engineering learning resources.
• Set high standards for code quality, reproducibility, and maintainability across the ML engineering discipline.

Skills

Python

Go/Java/C++

ML infrastructure

MLOps frameworks

Mentorship

Tools

Docker

Kubernetes

AWS SageMaker

Vertex AI

MLflow

TFX

Kubeflow

Job description

Job Description

BBI is a global data engineering consulting firm that empowers clients to effectively scale and modernize. We combine engineering fundamentals and innovative tools to execute business-critical, end-to-end projects on-time and on-budget. We offer expert services across Data Integration, Data Modernization, Data Migration, Data Architecture, Platform Support, and Application Services. Our goal is to provide business value in the most effective way for our clients so clients can focus on growth.

Job Description – Sr. Machine Learning Software Engineer

Position Summary

The Senior Machine Learning Software Engineer is a senior-level technical contributor responsible for leading the development of software infrastructure, tools, and platforms that enable scalable and maintainable machine learning operations. This role plays a critical part in bridging the gap between research and production by architecture reliable systems for training, testing, deployment, and monitoring of machine learning models. The Senior Machine Learning Software Engineer ensures AI capabilities are production-grade, reliable, and scalable—unlocking innovation across all AI-driven products. In addition to making significant technical contributions, the Senior MLSE provides mentorship to junior engineers and fosters best practices in software quality, MLOps, and automation across the machine learning lifecycle.

Responsibilities:

Infrastructure Design & Development

• Architect, build, and maintain reusable components and tools to support model training, evaluation, and deployment at scale.


• Optimize model serving frameworks, feature stores, data pipelines, and CI/CD systems for ML workflows.


• Ensure reliability, observability, and performance across ML systems in production.

Technical Leadership & Execution

• Lead cross-functional engineering initiatives involving platform stability, experimentation infrastructure, or real-time inference systems.


• Review code, propose architectural improvements, and uphold software engineering best practices within the ML engineering team.


• Drive design and implementation of MLOps pipelines, automation, and model governance workflows.

Collaboration with Research & Product Engineering

• Work closely with ML researchers to produce experimental models, ensuring compatibility with existing infrastructure.


• Coordinate with data engineering to integrate pipelines, data validations, and model input/output schemas.


• Contribute to product engineering discussions when ML systems require edge optimization, user facing API integrations, or UI-linked inference.


• Mentor ML Software Engineers I and II, with a proven track record of advancing at least one MLSE I to MLSE II.


• Contribute to internal documentation, architecture reviews, and engineering learning resources.


• Set high standards for code quality, reproducibility, and maintainability across the ML engineering discipline.

Requirements

• 5–6 years of industry experience in ML engineering, backend engineering, or infrastructure roles supporting machine learning


• Proficient in Python and one or more systems-level languages (e.g., Go, Java, C++)


• Experience building and maintaining ML infrastructure (e.g., model registries, training orchestration, distributed data pipelines)


• Familiarity with containerization and deployment technologies (Docker, Kubernetes, AWS SageMaker, Vertex AI, etc.)


• Hands-on experience with modern MLOps frameworks (e.g., MLflow, Meta-flow, TFX, Kuberflow, etc.)


• Demonstrated mentorship experience, with direct support for the growth and promotion of junior engineer