Senior Applied AI Scientist

15 hours ago

Odhav, 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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