AI Engineer

Role overview

AI Engineer at FLAM

Flam builds multimodal AI systems that ship to real customers.

Our stack spans four production surfaces: Falcon — Our LLM system, built on a sparse-Mo E backbone.

Finesse — Our TTS and cross-lingual voice cloning system.

We are a small team.

Whoever joins will own systems end-to-end training, evaluation, serving, and the latency budget.

Responsibilities

• Help increase the GDP of the internet as part of our AI team
• Fine-tune and evaluate open-weight models Lo RA/QLo RA, full SFT, preference tuning) and get them into production without a quality regression.
• Optimize inference: quantization FP8/NVFP4/INT4, speculative decoding, prefix and KV-cache strategies, batching and scheduling on v LLM or SGLang.
• Build evaluation harnesses that tell us something true — benchmark suites, regression gates, and per-release comparisons against both our own prior checkpoints and external baselines.
• Own latency.
• Profile the pipeline, find where the milliseconds go, and remove them.
• Take research to production: read the paper, replicate it, decide honestly whether it's worth shipping, and then ship it.
• Write and maintain the serving infrastructure around your models —containers, autoscaling, GPU scheduling, observability What we're looking for Required 2+ years building ML systems that ran in production, not only in notebooks.
• Strong Python and Py Torch.
• You can read a model implementation and modify it, not just call .fit.
• Hands-on experience with at least one modern inference stack (v LLM, SGLang, Tensor RTLLM, or TGI) and a real understanding of what makes it fast.
• Demonstrated fine-tuning experience — you've trained something, evaluated it properly, and know why your eval numbers meant what you claimed.
• Comfort with GPU-level reasoning: memory layout, precision trade-offs, where the bottleneck actually is.
• Ability to work from a paper.
• We move on recent research and expect you to be able to read it.
• Quantization experience beyond running an off-the-shelf script.
• Real-time streaming systems Web RTC, low-latency audio/video pipelines).
• Indic language NLP, multilingual tokenizer work, or code-mixed data.
• Cloud GPU deployment on GCP, AWS, or Run Pod — including the unglamorous parts.
• Our stack Py Torch
• v LLM
• SGLang
• Tensor RT / ONNX
• Unsloth
• LLa MAFactory
• TRL Milvus
• BGEM3
• Redis
• NATS
• Docker
• GCP
• Run Pod
• Web RTC
• Comfy UI.

Preferred qualifications and skills

• Strongly preferred Experience in one or more of: speech ASR/TTS, diffusion and image generation, or video/avatar generation.

Employment details

• Location: Bengaluru [San Francisco] [India]