Senior Machine Learning Engineer

4 days ago

Vadodara, India Tata Consultancy Services Full time

TCS is HiringJob Title: Machine Learning EngineerExperience: 6-8 YearsLocation: Ahmedabad, Chennai, Gurgaon, Hyderabad, KolkataJob Description:- Develops, deploys, and maintains scalable machine learning (ML) models, including specialized focus on Large Language Models (LLMs) and other foundation models. - Designs and implements robust APIs and microservices for real-time model serving and inference, particularly focusing on optimizing latency and throughput for large generative models. - Implements MLOps practices (CI/CD) to automate the testing, deployment, and monitoring of ML systems, including specific pipelines for fine-tuning and customizing LLMs. - Optimizes model performance, latency, and resource consumption through techniques like quantization, pruning, and low-rank adaptation (LoRA) for efficient GenAI deployment. - Establishes comprehensive monitoring systems for tracking model health, detecting data drift, concept drift, and monitoring prompt quality and token usage in production GenAI environments. - Designs and implements ethical AI guardrails, content filters, and safety mechanisms to mitigate risks (e.g., bias, toxicity, and hallucination) associated with deployed GenAI applications. - Manages the underlying infrastructure (e.g., specialized compute clusters) necessary to support the intensive training and serving requirements for large-scale GenAI workloads. - Contributes to the engineering wiki, defining model versioning strategies, prompt management standards, and documenting deployment architectures and runbooks for GenAI systems. - Expert proficiency in Python and specialized ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn), with specific expertise in transformer-based architecture. - Deep understanding of various GenAI applications (e.g., text generation, summarization, code synthesis, vector storage and retrieval) and the trade-offs between different foundation models.Proficiency in GenAI orchestration frameworks such as LangChain or LlamaIndex to build complex, multi-step generative applications. - Expertise in managing and integrating vector databases (e.g., Pinecone, Weaviate, Milvus) and embedding generation pipelines for semantic search and RAG systems. - Ability to manage and process large, unstructured datasets, often leveraging tools like Spark for data preparation pipelines, including cleaning data specifically for LLM fine-tuning. - Strong command of prompt engineering methodologies and capacity to design reusable, efficient prompt templates and management systems. - Familiarity with cloud platforms (AWS, Azure, or GCP) and specialized GenAI model hubs and services (e.g., Hugging Face, OpenAI APIs, Anthropic, or proprietary cloud offerings). - Skill in diagnosing and resolving complex technical issues related to inference latency, memory management, and cost optimization for large foundation models.

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