Machine Learning Engineer

1 week ago

Greater Bengaluru Area, India Valiance Solutions Full time US$ 1,25,000 - US$ 1,75,000 per year

About the Role:

We are seeking an experienced MLOps Engineer to lead the deployment, scaling, and performance optimization of open-source Generative AI models on cloud infrastructure. You'll work at the intersection of machine learning, DevOps, and cloud engineering to help productize and operationalize large-scale LLM and diffusion models.

Key Responsibilities:

  • Design and implement scalable deployment pipelines for open-source Gen AI models (LLMs, diffusion models, etc.).
  • Fine-tune and optimize models using techniques like LoRA, quantization, distillation, etc.
  • Manage inference workloads, latency optimization, and GPU utilization.
  • Build CI/CD pipelines for model training, validation, and deployment.
  • Integrate observability, logging, and alerting for model and infrastructure monitoring.
  • Automate resource provisioning using Terraform, Helm, or similar tools on GCP/AWS/Azure.
  • Ensure model versioning, reproducibility, and rollback using tools like MLflow, DVC, or Weights & Biases.
  • Collaborate with data scientists, backend engineers, and DevOps teams to ensure smooth production rollouts.

Required Skills & Qualifications:

  • 5+ years of total experience in software engineering or cloud infrastructure.
  • 3+ years in MLOps with direct experience in deploying large Gen AI models.
  • Hands-on experience with open-source models (e.g., LLaMA, Mistral, Stable Diffusion, Falcon, etc.).
  • Strong knowledge of Docker, Kubernetes, and cloud compute orchestration.
  • Proficiency in Python and familiarity with model-serving frameworks (e.g., FastAPI, Triton Inference Server, Hugging Face Accelerate, vLLM).
  • Experience with cloud platforms (GCP preferred, AWS or Azure acceptable).
  • Familiarity with distributed training, checkpointing, and model parallelism.

Good to Have:

  • Experience with low-latency inference systems and token streaming architectures.
  • Familiarity with cost optimization and scaling strategies for GPU-based workloads.
  • Exposure to LLMOps tools (LangChain, BentoML, Ray Serve, etc.).

Why Join Us:

  • Opportunity to work on cutting-edge Gen AI applications across industries.
  • Collaborative team with deep expertise in AI, cloud, and enterprise software.
  • Flexible work environment with a focus on innovation and impact.

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