Data Science Manager (Architect exp. is mandatory) Spectrum Talent Management

9.0 - 12.0 years

40 - 45 Lacs

Noida, Bengaluru, Delhi / NCR

Work from Office

Mandatory skill set: Python, any cloud, MLE, Deep learning, NLP, MLOPS, {Gen AI (LLM,Langchain,RAG ,Open AI}

Posted 2 days ago

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Senior / Lead AI (Gen AI, MLE) Tiger Analytics

5 - 9 years

15 - 30 Lacs

Hyderabad, Chennai, Bengaluru

Hybrid

Role & responsibilities As an AI lead specializing in Generative AI, and Machine Learning Engineering (MLE), you will be at the forefront of AI innovation. Your role will involve designing, deploying, and operationalizing sophisticated modelsincluding large language models (LLMs)—to solve complex business problems. You will work closely with cross-functional teams to create scalable, production-ready AI solutions that drive intelligent automation and creativity across industries. Your Key Responsibilities: Generative AI, NLP & MLE: Design, develop, deploy, and scale advanced applications using Generative AI models (e.g., GPT, LLaMA, Mistral), NLP techniques, and MLE/MLOps best practices to solve business challenges and unlock new opportunities. Model Customization & Fine-Tuning: Apply techniques such as LoRA, PEFT, and fine-tuning of LLMs to build domain-specific models aligned with business use cases, with a focus on making them deployable in real-world environments. ML Engineering & Deployment: Implement end-to-end ML pipelines—including data preprocessing, model training, versioning, testing, and deployment—using tools like MLflow, Docker, Kubernetes, and CI/CD practices. Innovative Problem Solving: Leverage cutting-edge AI and ML methodologies to solve practical business problems and deliver measurable results. Scalable AI Solutions: Ensure robust deployment, monitoring, and retraining of models in production environments, working closely with data engineering and platform teams. Data-Driven Insights: Conduct deep analysis of structured and unstructured data to uncover trends, guide decisions, and optimize AI models. Cross-Functional Collaboration: Partner with Consulting, Engineering, and Platform teams to integrate AI/ML solutions into broader architectures and business strategies. Client Engagement: Work directly with clients to understand requirements, present tailored AI solutions, and provide advisory on the adoption and operationalization of Generative AI and ML.

Posted 1 month ago

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