Principal Software Engineer — Backend & Infrastructure - Noida / Bengaluru

You'll work alongside engineers from Amazon, Google, and Meta who chose to build here because the problems are unsolved and the ownership is real.

What you'll do

Own the architecture for real-time data processing at scale. Design and evolve distributed messaging systems that handle high-throughput streaming workloads with strict latency requirements.

Build the ML platform that lets us ship models faster. Define and execute the technical roadmap for training and serving infrastructure as our models grow in size, complexity, and inference cost.

Scale our GPU infrastructure. Own capacity planning, scheduling, and utilization across training and inference fleets — deciding what runs where, how we handle burst demand, and how we keep spend tied to actual throughput rather than idle reservations.

Scale inference. Drive down latency and cost per request as model complexity and traffic grow: batching and routing strategies, quantization and compilation, autoscaling, and caching — without degrading output quality.

Make reliability a property of the system, not a heroic effort. Drive uptime, observability, and incident response for serving systems that enterprise customers depend on in production.

Turn ambiguous business problems into executable technical plans. Partner with Product and GTM to scope large cross-functional initiatives, then break them into work other teams can run with.

Multiply the team. Lead design reviews, mentor senior engineers, and shape the engineering practices that outlast any single project.

Bring the outside in. Evaluate emerging tools and techniques with judgment — adopt what earns its complexity, skip what doesn't.

Security and Compliance - Comply with Level AI’s information security, privacy, data protection, and acceptable-use policies.Protect company, customer, and confidential information; use only approved systems and access information strictly based on business need. Complete required security and privacy training and promptly report any suspected security incident, data exposure, or policy violation.

What success looks like in your first year

90 days: You've mapped our critical paths and failure modes, shipped a meaningful improvement to a serving or pipeline bottleneck, and earned trust across both sites.

6 months: You own a published technical roadmap for [messaging / ML infra], with at least one major migration or redesign underway. GPU utilization and inference cost per request are measured, and trending the right way.

12 months: Our infrastructure scales predictably with customer growth, inference cost stays flat or falls as volume rises, on-call load is down, and other engineers are making better architectural decisions because of standards you established.

What you'll bring

[9-12] years building backend and infrastructure systems, with a track record of owning architecture and design at scale — not just implementing it.

Deep, hands-on experience with large-scale databases, high-throughput messaging systems, and real-time job queues.

Proven ability to navigate large, complex codebases and reason clearly about architectural tradeoffs in systems you didn't build.

Experience mentoring senior engineers and driving technical decisions through influence rather than authority.

Strong written communication—you'll be making technical cases to engineers in two time zones and business cases to executives.

BTech/MTech/PhD in Computer Science or equivalent. At this level we weigh track record well above pedigree.

Bonus points for

Production experience with our stack: Django, Celery, Redis, PostgreSQL, and Google Cloud.

Hands-on experience scaling GPU infrastructure and model inference in production — capacity planning, scheduling and utilization, autoscaling, and latency/cost optimization under real traffic.

Depth in specific inference tooling — vLLM, TensorRT, Triton, Ray Serve, or equivalents — and experience benchmarking tradeoffs between them.

Experience scaling a platform through a comparable growth stage (Series C → D, or equivalent), especially at a global product company's India site.

Background in speech, NLP, or information retrieval systems.

Compensation & benefits

Health coverage for you, your spouse, children, and parents.

[home-office and connectivity allowance / annual learning budget / parental leave — trim to what's accurate].

Real ownership over systems used by enterprises worldwide, with the autonomy to decide how they're built.

How we hire

[4] stages, typically [2] weeks end to end: recruiter screen → technical deep dive → system design → team and leadership conversations → offer. Interviews are conducted from our India team with [one/two] conversations with US-based leadership. We'll tell you where you stand at every step.

Level AI is an equal opportunity employer. We hire and promote on merit and evaluate all applicants without regard to caste, religion, sex, gender identity, sexual orientation, marital or parental status, disability, or any other personal characteristic unrelated to the job. We maintain a zero-tolerance policy on harassment in line with the POSH Act, 2013. If you need any accommodation during the interview process, tell us — we'll arrange it.