The Role
Lead the design and development of scalable machine learning infrastructure, including training, evaluation, deployment, model serving, feature stores, data pipelines, CI/CD, monitoring, and governance. Optimize production inference systems, ensure reliability and performance, collaborate with research and engineering teams, review architecture and code, and mentor junior ML engineers.
Summary Generated by Built In
Experience level: 5 - 8 Years
Qualification: Postgraduate/ Graduate
Location: Chennai/Pune/Bangalore
At Black buck Insights (BBI), we hire great minds who can embrace technology to innovate and build. We are
always on the lookout for individuals who are thrilled by the idea of developing solutions, features, and
services while managing ambiguity and super-paced projects. If this is you, come chart your own path at BBI!
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.
Mentorship & Knowledge Sharing
● 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
- 3+ years building and operating production software systems (ML software/inference platform experience strongly preferred).
- Strong Python engineering plus solid Linux/bash debugging skills.
- Hands-on experience with NVIDIA Triton Inference Server (or equivalent model serving platform).
- Practical experience in model optimization + deployment pipeline (e.g., ONNX/TensorRT, performance/latency tuning, packaging for production).
- Proven experience deploying and operating services on AWS, including ECS, plus Docker/container workflows, S3/ECR, IAM/secrets, and safe rollout/rollback practices.
- Experience with CI/CD and artifact/version management for ML software (DVC/MLflow-equivalent workflows are a plus).
- Production reliability mindset: monitoring, incident triage, and staged release safety.
- Strong ownership, communication, and demonstrated ability to ramp quickly on missing stack-specific pieces within a 3-6 month onboarding window.
Skills Required
- Postgraduate or graduate qualification
- 3+ years building and operating production software systems
- Strong Python engineering skills
- Solid Linux and Bash debugging skills
- Hands-on experience with NVIDIA Triton Inference Server or an equivalent model serving platform
- Practical experience with model optimization and deployment pipelines, including ONNX/TensorRT, performance and latency tuning, and production packaging
- Experience deploying and operating services on AWS, including ECS, Docker, S3/ECR, IAM/secrets, and safe rollout/rollback practices
- Experience with CI/CD and artifact/version management for ML software
- Production reliability experience involving monitoring, incident triage, and staged release safety
- ML software or inference platform experience
- Experience with DVC or MLflow-equivalent workflows
- Strong ownership and communication skills
- Ability to ramp quickly on missing stack-specific technologies within 3–6 months
- Experience mentoring junior ML engineers
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The Company
What We Do
Blackbuck Insights is a global data and AI consultancy providing data engineering services, cloud modernization, AI activation, and platform operations. The company helps enterprises modernize legacy environments, migrate and scale data platforms, build AI-enabled business applications, and establish trusted data foundations for analytics and intelligent decision-making. Its services support organizations seeking reliable, performant, and scalable technology solutions.
