Job
Description
As a Manager, Machine Learning Engineering, you will collaborate with cross-functional teams to strategize, develop, and deliver machine learning models tailored to meet specific business objectives. You will be responsible for overseeing the entire lifecycle of these models, from data preprocessing and algorithm selection to performance evaluation and seamless integration into production systems. This role has a specific focus on Generative AI . Your Impact: What You'll Achieve As a Manager, Data Science specializing in Generative AI, you will: Lead AI-Driven Innovations: Drive the development of state-of-the-art AI and machine learning solutions that transform business strategies and deliver exceptional customer experiences. Strategic Collaboration: Work closely with cross-functional teams, including product managers, data engineers, and business stakeholders, to define and execute data-driven solutions aligned with organizational goals. Foster a High-Performance Team: Build, mentor, and lead a team of talented data scientists, cultivating a culture of innovation, collaboration, and continuous learning. Deliver Business Impact: Translate complex business problems into AI/ML solutions by leveraging advanced techniques such as generative AI, deep learning, and NLP , ensuring measurable outcomes. Optimize AI Pipelines: Oversee the development and deployment of scalable, efficient, and robust machine learning pipelines that address latency, responsiveness, and real-time data processing challenges. Customize AI Models: Direct the customization and fine-tuning of AI models, including large language models (LLMs) and other generative AI technologies, to meet domain-specific requirements. Promote Data-Driven Decision-Making: Advocate for data-centric approaches across teams, ensuring data quality, integrity, and readiness to maximize model performance and business impact. Develop Intelligent AI Agents: Architect and refine AI agents that solve complex business challenges, leveraging LLMs to deliver personalized, user-centric solutions. Advance Generative AI Applications: Innovate with cutting-edge generative AI models such as LLM, VLM, GANs, and VAEs to create tailored applications for dynamic content creation, predictive analytics, and enhanced automation. Scale AI with Cloud Technology: Deploy and scale LLM-based solutions on platforms like GCP, AWS, and Azure to address real-world business problems with precision and efficiency. Stay at the Cutting Edge: Keep up-to-date with emerging trends and innovations in AI and data science, identifying opportunities to incorporate the latest advancements into projects. Responsibilities Design AI Systems: Build AI agents for tasks such as content compliance, asset decomposition, and contextual personalization. Develop NLP Pipelines: Implement advanced NLP solutions for search relevance, intent detection, and dynamic content generation. Integrate Multi-Modal Systems: Combine data modalities such as text, images, and metadata for enriched user interactions and insights. Optimize AI Pipelines: Innovate in latency reduction, scalability, and real-time responsiveness for AI systems in production. Collaborate on AI Innovation: Work with business stakeholders to identify opportunities and deliver impactful AI-driven solutions. Qualifications Your Skills & Experience Overall Experience: 8 to 15 years of experience. Generative AI Experience: At least 2 years of Gen AI experience . LLM Fine-tuning: Fine-tuning experience with Large Language Models (LLMs, VLLMs, or Vision models) . Distributed Training/Inference: Experience with distributed training or inference frameworks like Ray, vllm, openllm, bentoML etc. Generative AI Frameworks: Experience with frameworks like LangChain, Llamaindex for building maintainable, scalable Generative AI applications. LLM Deployment/Optimization: Deployment experience or optimized hosting experience of Large Language Models (LLMs, VLLMs, or Vision models) . Vector Databases: Experience working with any Vector database like Milvus, FAISS, ChromaDB etc. Agent Development: Experience developing agents with frameworks like LangGraph, CrewAI, Autogen etc. Prompt Engineering: Experience with prompt engineering. Market Trends: Keeping up with latest market trends. Open Source LLMs: Experience working with open-source large language models from HuggingFace . Cloud Providers: Experience working with at least one public cloud provider such as Azure, AWS, or GCP . Container Technology: Experience working with container technology like Docker, ECS etc. DevOps & CI/CD: Experience with DevOps practices and CI/CD pipelines for data solutions. Production Deployment: Experience in deploying solutions to production with Kubernetes or OpenShift . ML Workflow Management: Experience with managing ML workflows with MLFlow or KubeFlow.
