Role & responsibilities

Core Responsibilities
• Production Model Serving: Build, configure, and maintain high-throughput inference
endpoints using advanced orchestration layers and model backends.
• Model Optimization & Quantization: Apply advanced compression techniques (such
as AWQ, GPTQ, and FP8 native quantization) to large open-weight models (like
Qwen2.5-VL and Llama-3-8B) to make them run smoothly on localized Edge
hardware or low-power nodes.
• Data Flywheel Engineering: Build secure, privacy-compliant local pipeline loops that
capture failed inferences or edge cases on-premise, allowing clients to aggregate
data locally for future fine-tuning blocks.
• CI/CD for Machine Learning: Automate the packaging, testing, and deployment of
fine-tuned model artifacts into production-ready containers, integrating with offline
registries like MLflow.




Required Technical Skillset
• Experience: 1+ years of pure MLOps experience deploying, scaling, and optimizing
large language, vision, or speech models in production.
• Inference Runtimes: Deep, hands-on production experience with Triton Inference
Server, vLLM, TensorRT-LLM, and ONNX Runtime.
• GPU & Hardware Optimization: Proven ability to manage GPU memory allocations,
optimize CUDA kernels, handle multi-LoRA weight swapping on the fly, and mitigate
cold-start latencies.
• Tooling & Pipelines: Mastery of Python, Docker, shell scripting, and MLflow/Weights
& Biases for model lineage and artifact versioning.

Preferred candidate profile

Perks and advantages

📌 Mlops engineer (Mumbai)
🏢 Acura solutions
📍 Mumbai