Machine Learning Engineer

About Simplismart

Simplismart is a GenAI inference platform to deploy, scale, and monitor any GenAI model (LLMs, speech, vision, or diffusion) across cloud or on-prem. Built for strict SLAs, enterprise-grade security, and full observability. Its modular design lets you optimize for cost or latency or auto-select the best topology per workload.

Role Overview

In this role, you will design and build the high-level architecture of Simplismart’s MLOps platform from the ground up, enabling scalable, reliable, and GPU-accelerated ML workflows across the product ecosystem.

What is expected from you-

Design and implement the core architecture of a next-generation MLOps platform capable of running diverse GPU-accelerated workloads at scale.

Formalise and standardize heterogeneous ML workloads—including LLM/VLM/ASR/diffusion pipelines and build orchestration abstractions for them.

Build internal systems for continuous deployment of services, modules, and model pipelines across multi-cloud and hybrid environments.

Create frameworks for high reliability, observability, and fault-tolerance for mission-critical inference, training, and data pipelines.

Collaborate closely with Applied ML and Core ML teams to improve system reliability, latency, and cost efficiency.

Develop internal tooling to benchmark, evaluate, and deploy models quickly and consistently.

Ship production-grade code and infrastructure using strong engineering fundamentals and test-driven development (TDD), aligning with Simplismart’s engineering culture.

Troubleshoot complex systems, performance bottlenecks, GPU behavior, and distributed workloads.

What We’re Looking For-

Deep technical expertise in system design, distributed systems, and GPU-based ML workloads.

Strong software engineering fundamentals (data structures, APIs, testing, debugging).

Experience with infrastructure-as-code (Terraform, Ansible) and cloud platforms (AWS/GCP/Azure).

Strong knowledge of ML fundamentals, model architectures (Transformers, CNNs), and inference behavior.

Ability to build, maintain, and reason about multi-step pipelines (ETL → model → evaluation → deploy).

Strong systems knowledge: Linux internals, networking, performance tuning, GPU memory behavior.

Ability to work independently, own large ambiguous problems, and collaborate across teams.

Excellent communication skills—able to articulate design decisions, tradeoffs, and system impacts clearly.

Good to Have-

Experience with modern inference stacks such as TensorRT, Triton, vLLM/TGI, SGLang.

Exposure to quantization, model optimization, or CUDA concepts.

Hands-on experience with Llama/Mistral, Whisper, or Stable Diffusion pipelines.

Familiarity with CI/CD, Docker, GitHub workflows, and IaC-driven deployments.

Experience designing high-availability or fault-tolerant production systems.

Why Join Simplismart?

Opportunity to define and lead the brand identity of a fast-growing GenAI company.

Work closely with leadership on high-impact initiatives from global event campaigns to overall storytelling.

Be part of a team that values design as a strategic lever, not just execution.

Competitive compensation and growth opportunities in a high-energy startup environment.