Senior Machine Learning /LLM Engineer

Machine Learning Engineer

Key Responsibilities:

• Design and implement

  end-to-end MLOps pipelines

  for training, validation, deployment, monitoring, and retraining of ML models.
• Optimize and fine-tune

  large language models (LLMs)

  for various applications, ensuring performance and efficiency.
• Develop

  CI/CD pipelines

  for ML models to automate deployment and monitoring in production.
• Monitor model performance,

  detect drift

  , and implement automated retraining mechanisms.
• Work with cloud platforms (

  AWS, GCP, Azure

  ) and containerization technologies (

  Docker, Kubernetes

  ) for scalable deployments.
• Implement best practices in

  data engineering

  , feature stores, and model versioning.
• Collaborate with data scientists, engineers, and product teams to integrate ML models into production applications.
• Ensure compliance with

  security, privacy, and ethical AI standards

  in ML deployments.
• Optimize inference performance and cost of

  LLMs using quantization, pruning, and distillation techniques

  .
• Deploy

  LLM-based APIs

  and services, integrating them with real-time and batch processing pipelines.

Key Requirements:

Technical Skills:

• Strong programming skills

  in Python, with experience in ML frameworks (

  TensorFlow, PyTorch, Hugging Face, JAX

  ).
• Experience with

  MLOps tools

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

  LLM architectures

  , prompt engineering, and fine-tuning.
• Hands-on experience with

  containerization (Docker, Kubernetes) and orchestration tools

  .
• Proficiency in

  cloud services

  (AWS/GCP/Azure) for ML model training and deployment.
• Experience with

  monitoring ML models

  (Prometheus, Grafana, Evidently AI).
• Knowledge of

  feature stores

  (Feast, Tecton) and data pipelines (Kafka, Apache Beam).
• Strong background in

  distributed computing (Spark, Ray, Dask)

  .

Soft Skills:

• Strong problem-solving and debugging skills.
• Ability to work in cross-functional teams and communicate complex ML concepts to stakeholders.
• Passion for staying updated with the latest

  ML and LLM research & technologies

  .

Preferred Qualifications:

• Experience with

  LLM fine-tuning

  , Reinforcement Learning with Human Feedback (

  RLHF

  ), or

  LoRA/PEFT techniques

  .
• Knowledge of

  vector databases

  (FAISS, Pinecone, Weaviate) for retrieval-augmented generation (

  RAG

  ).
• Familiarity with

  LangChain, LlamaIndex

  , and other LLMOps-specific frameworks.
• Experience deploying

  LLMs in production (ChatGPT, LLaMA, Falcon, Mistral, Claude, etc.)

  .