Senior ML Backend Engineer

Jobgether; India; On-site; INR 3,500,000 - 6,500,000; Full time

Benefits offered by this job

Advanced ML projects

Geospatial analytics exposure

Cloud-native infra

Collaborative environment

Professional growth

Job summary

Jobgether is seeking a Senior ML Backend Engineer based in India to build scalable ML infrastructure, pipelines, and tooling for large-scale training, evaluation, deployment, and monitoring. You will collaborate with ML researchers and software teams to productionize models and automate workflows.

You should have deep Python expertise, experience with MLOps (CI/CD, model monitoring, data lineage, governance), and strong cloud/Kubernetes skills.

Qualifications

• Senior backend experience with ML focus.
• Experience building ML infrastructure for large-scale training and deployment.
• Proficiency in Python and ML tooling.
• Hands-on MLOps: CI/CD, model monitoring, data lineage, governance.
• Cloud-native tech, Kubernetes, distributed computing.
• Familiarity with AI tools, coding assistants, and LLM agents.
• Experience applying responsible and ethical AI principles.
• Strong analytical, problem-solving, and communication skills.
• Ability to collaborate with ML researchers, software devs, and product teams.
• Equivalent practical experience welcomed.

Responsibilities

• Build, maintain, and evolve scalable ML infrastructure for development to monitoring.
• Design reliable ML pipelines and tooling for large-scale workloads.
• Collaborate with ML researchers to productionize models from prototypes.
• Develop automation, testing, observability, data governance across ML environments.
• Evaluate new data sources and tools to improve model performance.
• Collaborate with software, product, and business stakeholders to deliver ML solutions.
• Apply AI-powered tools to automate workflows and improve productivity.
• Ensure ML systems meet security, governance, and risk standards.
• Identify trade-offs and contribute to infra architecture decisions.

Skills

Python

ML tooling

CI/CD

Model monitoring

Data lineage

Kubernetes

Cloud-native

Distributed computing

Communication

Education

PhD in STEM

Masters with relevant exp

Bachelors with hands-on exp

Tools

Docker

Experiment tracking

Containerization

Job description

Senior ML Backend Engineer based in India.

Accountabilities

• Build, maintain, and evolve scalable machine learning infrastructure supporting model development, training, evaluation, deployment, and monitoring.
• Design reliable, cost-effective ML pipelines, platforms, and engineering tooling for large-scale workloads.
• Partner with machine learning engineers and researchers to transition new models and approaches from prototypes into production-ready systems.
• Develop automation, testing, observability, monitoring, reproducibility, data lineage, and governance capabilities across ML environments.
• Evaluate and integrate new data sources, technologies, platforms, and tools that can improve model performance and operational efficiency.
• Collaborate with software engineering, technology, product, and commercial stakeholders to deliver scalable machine learning solutions.
• Apply AI-powered development tools, coding assistants, and LLM-based agents to automate workflows and improve engineering productivity.
• Ensure machine learning systems meet security, governance, responsible AI, and model risk management standards.
• Identify technical trade-offs and contribute to architectural decisions involving infrastructure scalability, reliability, performance, and cost.

Requirements

• Senior-level backend software engineering experience with a strong understanding of machine learning and a demonstrated interest in developing deeper ML expertise.
• Experience designing, building, and maintaining machine learning infrastructure, platforms, and tooling for large-scale training, evaluation, and deployment.
• Strong proficiency in Python and modern machine learning engineering tools, including deep learning frameworks, experiment tracking, version control, containerization, and automated workflows.
• Hands-on experience with MLOps practices such as CI/CD, model monitoring, reproducibility, data lineage, model governance, and production operations.
• Proven experience with cloud-native technologies, Kubernetes, distributed computing environments, and scalable infrastructure supporting machine learning workloads.
• Demonstrated understanding of artificial intelligence concepts and practical experience using AI tools, coding assistants, and LLM-based agents to enhance engineering workflows.
• Experience implementing AI-powered solutions to address business challenges, with awareness of responsible and ethical AI principles.
• Strong analytical, problem-solving, and communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
• Ability to collaborate effectively with machine learning engineers, researchers, software developers, product teams, and business stakeholders.
• PhD in a science, technology, engineering, or mathematics discipline is preferred; a Master's degree with significant relevant industry experience or a Bachelor's degree with extensive hands-on experience is also considered.
• Equivalent practical experience and non-traditional career paths are welcome.

Benefits

• Opportunity to work on advanced machine learning, computer vision, geospatial analytics, and AI challenges.
• Exposure to large-scale aerial and satellite imagery and technology supporting property intelligence solutions.
• Work with modern cloud-native infrastructure, distributed computing, ML platforms, and AI-enabled engineering tools.
• Opportunity to contribute to responsible AI, model governance, security, and risk management practices.
• Collaborative environment involving machine learning engineers, researchers, software engineers, product teams, and other stakeholders.
• Professional growth opportunities through work on complex, high-impact machine learning infrastructure challenges.
• Inclusive workplace focused on curiosity, diverse perspectives, integrity, collaboration, and continuous improvement.
• Candidates who do not meet every listed requirement are encouraged to apply if they can demonstrate relevant skills and experience.