Lead Machine Learning Engineer

Lead Machine Learning Engineer

You will lead the design of robust ML platforms, optimize LLM performance and cost, and work closely with data science, platform, and product teams to deliver reliable AI solutions at scale.

Key Responsibilities

Technical Leadership & Architecture

• Architect and implement

  end-to-end MLOps and LLMOps pipelines

  covering training, validation, deployment, monitoring, and automated retraining.
• Define

  engineering standards, best practices, and reference architectures

  for ML and LLM systems in production.
• Review designs and code, providing technical guidance and mentorship to ML engineers and data scientists.
  

Hands-on (IC Ownership)

• Design and deploy

  scalable, fault-tolerant ML and LLM services

  for real-time and batch use cases.
• Fine-tune, optimize, and serve

  large language models (LLMs)

  with a focus on performance, latency, and cost efficiency.
• Implement

  CI/CD pipelines

  for ML models, ensuring reproducibility, versioning, and automated rollout.
• Monitor model performance, data quality, and drift; build automated feedback and retraining loops.
  
• Optimize inference using

  quantization, pruning, distillation, LoRA/PEFT

  , and efficient serving strategies.

Platform & Cloud Engineering

• Build and operate ML platforms using

  AWS, GCP, or Azure

  , leveraging managed ML services where appropriate.
• Containerize and orchestrate ML workloads using

  Docker and Kubernetes

  .
• Implement feature stores, model registries, and experiment tracking for scalable collaboration.
  

Collaboration & Governance

• Partner with product, data, and engineering teams to translate business problems into ML solutions.
  
• Ensure ML systems meet

  security, privacy, compliance, and ethical AI standards

  .
• Contribute to roadmap planning and technical decision-making for AI initiatives.
  

Required Qualifications

Technical Skills

• 811 years of experience in software engineering and machine learning, with significant

  production ML ownership

  .
• Expert-level

  Python

  programming skills.
• Strong hands-on experience with ML frameworks such as

  PyTorch, TensorFlow, Hugging Face, JAX

  .
• Proven experience with

  MLOps/LLMOps tools

  : MLflow, Kubeflow, Vertex AI, SageMaker, Airflow.
• Deep understanding of

  LLM architectures, prompt engineering, and fine-tuning workflows

  .
• Solid experience with

  Docker, Kubernetes

  , and cloud-native ML deployments.
• Experience with

  model monitoring and observability

  (Prometheus, Grafana, Evidently AI).
• Strong background in

  distributed computing

  (Spark, Ray, Dask).
• Experience with

  data pipelines and feature stores

  (Kafka, Apache Beam, Feast, Tecton).

Leadership & Soft Skills

• Demonstrated experience

  leading or mentoring ML engineers

  while remaining hands-on.
• Strong problem-solving, debugging, and system-level thinking skills.
  
• Ability to clearly communicate complex ML concepts to technical and non-technical stakeholders.
  
• Proactive mindset with a passion for learning and adopting emerging ML and LLM technologies.
  

Preferred Qualifications

• Hands-on experience with

  LLM fine-tuning, RLHF, LoRA, and PEFT techniques

  .
• Experience building

  RAG pipelines

  using vector databases such as

  FAISS, Pinecone, Weaviate

  .
• Familiarity with

  LangChain, LlamaIndex

  , or similar LLM orchestration frameworks.
• Production experience deploying and operating LLMs such as

  LLaMA, Mistral, Falcon, Claude, or GPT-based models

  .
• Experience designing

  cost-efficient, high-availability LLM inference architectures

  .