Backend Software Engineer - AI/ML (m/f/d)
Crewmeister (company site, opens in a new tab)BengaluruOn-siteDirect career pageEst. 12–22 LPA
Posted 18h ago
Reach people at Crewmeister
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Crewmeister you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, build, and operate scalable, reliable backend services and APIs integrating AI-powered functionality at enterprise scale within the ATOSS platform.
- Own the full lifecycle of backend services, including architecture, implementation, deployment, monitoring, and ongoing operations, primarily using Java and Python.
- Collaborate with AI Engineers and Cloud Operations to deliver AI capabilities, build observability features, and improve system reliability and performance.
Minimum Requirements
- Strong experience in backend development using Java and Python.
- Proven experience designing, building, and operating distributed systems and APIs in production environments.
- Hands-on experience with cloud platforms (Azure, AWS, or GCP), containerization (Docker), CI/CD, and monitoring/observability tools.
- Professional experience integrating or operating AI-powered applications, including LLMs, AI agents, or retrieval-based systems. Location: Bangalore (Whitefield, ITPL) with hybrid work model; first 6 months onsite required.
Ideal Candidate Profile
- Backend software engineer with solid expertise in Java and practical Python skills, focused on high-quality, maintainable, and reliable production software.
- Experienced in operating distributed systems with cloud-native and containerized environments employing modern engineering practices (CI/CD, Terraform, Kubernetes).
- Prior hands-on experience with integrating and maintaining AI-driven systems at scale, including familiarity with LLMs and retrieval-augmented architectures.