Senior Machine Learning Engineer
- Tredence
- 5 - 8 years
- Kolkata
- 3 months ago
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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.).
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Machine Learning EngineerEmployement Type: Full timeContact Details:
Company: TredenceLocation(s): Kolkata
Keyskills: Vertex Ai Machine Learning Algorithms Azure Databricks Deploying Models Gcp Cloud Orchestration Fine Tuning Docker Azure Cloud Services Ml Pipelines Kubernetes
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