Senior Machine Learning Engineer
- Tredence
- 5 - 8 years
- Kolkata
- 5 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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Tredence
Tredence is a global data science solutions provider focused on solving the last mile problem in AI. The last mileis the gap between insight creation and value realization.Headquartered in San Jose, the company embraces a vertical-first approach and an ou
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