Job Description :

We are looking for a Machine Learning Engineer with expertise in MLOps (Machine Learning Operations) and LLMOps (Large Language Model Operations) to design, deploy, and maintain scalable AI/ML systems.

You will work on automating ML workflows, optimizing model deployment, and managing large-scale AI applications, including LLMs (Large Language Models), ensuring they run efficiently in production.

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.).