5 Milvus Jobs

Here's a detailed overview of the Manager, Machine Learning Engineering (Specializing in Generative AI) role at Publicis Sapient in Hyderabad, Telangana, India: Company Description Publicis Sapient is a digital transformation partner that helps established organizations achieve their future, digitally-enabled state, both in how they work and how they serve their customers. They unlock value through a start-up mindset and modern methods, fusing strategy, consulting, and customer experience with agile engineering and creative problem-solving . United by their core values and purpose of helping people thrive in the brave pursuit of next, their 20,000+ people in 53 offices around the world combine experience across technology, data sciences, consulting, and customer obsession to accelerate their clients businesses by designing the products and services their customers truly value. Overview: Manager, Machine Learning Engineering (Generative AI Specialist) Publicis Sapient is seeking an experienced Manager, Machine Learning Engineering to lead their talented team of AI and data science experts. In this influential role, you will be responsible for developing and implementing solutions that address complex business challenges across a wide range of industries, empowering clients to revolutionize their businesses by harnessing the potential of advanced technology. 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 .

Tech stack required: Programming languages: Python Public Cloud: AzureFrameworks: Vector Databases such as Milvus, Qdrant/ ChromaDB, or usage of CosmosDB or MongoDB as Vector stores. Knowledge of AI Orchestration, AI evaluation and Observability Tools. Knowledge of Guardrails strategy for LLM. Knowledge on Arize or any other ML/LLM observability tool. Experience: Experience in building functional platforms using ML, CV, LLM platforms. Experience in evaluating and monitoring AI platforms in production Nice to have requirements to the candidate Excellent communication skills, both written and verbal. Strong problem-solving and critical-thinking abilities. Effective leadership and mentoring skills. Ability to collaborate with cross-functional teams and stakeholders. Strong attention to detail and a commitment to delivering high-quality solutions. Adaptability and willingness to learn new technologies. Time management and organizational skills to handle multiple projects and priorities.

We are seeking skilled Full Stack Developers with expertise in backend and frontend development. JD Link- https://docs.google.com/document/d/1n5vpXaApnptBBlavO8Bwlm7Dtd8po-NQN7X8fghixS4/edit?usp=sharing Required Candidate profile Backend-Focused – Expertise in Python, Django with proficiency in frontend technologies like Angular and React Frontend-Focused – Expertise in Angular and React, with proficiency in Python and Django

Position Overview: We seek a skilled and innovative AI Engineer with background experience in Python, LangChain, AI, ML, and Data Science principles to design, develop, and deploy Agentic AI Agents / Vertical LLM Agents. The ideal candidate will possess extensive experience with LangChain, data science workflows, prompt engineering, retrieval-augmented generation (RAG), and LLM fine-tuning. You will be working to integrate structured and unstructured data into scalable knowledge bases and evaluate systems for continuous improvement. The role involves developing solutions for use cases primarily for UK-based clients and solving industry-specific challenges with cutting-edge AI technologies. Job Type: Full-Time Location: Powai, Mumbai Salary Range: Competitive, based on experience Working Hours: 10:30 am to 7:30 pm Indian Standard Time Days of work: Monday to Friday Key Responsibilities: 1. Knowledge Base Development and Integration Define Knowledge Base Scope: Collaborate with domain experts to identify industry-specific requirements and tasks. Assess and select appropriate structured and unstructured data sources. Data Curation and Organization: Collect and preprocess data from authoritative sources (e.g., research papers, databases, manuals). Structure unstructured data using techniques like knowledge graphs. Implement data cleaning workflows to ensure high-quality input. Knowledge Integration: Embed knowledge bases into LLM workflows using tools like Pinecone, Weaviate, or Milvus. 2. LLM Fine-Tuning Fine-tune LLMs using frameworks such as Hugging Face Transformers or OpenAI APIs. Use domain-specific datasets to adapt base models to specialized industries. Apply transfer learning techniques to enhance model performance for niche applications. Monitor and improve fine-tuned models using validation metrics and feedback loops. 3. Prompt Engineering Design, test, and optimize prompts for industry-specific tasks. Implement contextual prompting strategies to enhance accuracy and relevance. Iterate on prompt designs based on system evaluations and user feedback. 4. Retrieval-Augmented Generation (RAG) Implement RAG workflows to integrate external knowledge bases with LLMs. Develop and optimize embedding-based retrieval systems using vector databases. Combine retrieved knowledge with user queries to generate accurate and context-aware responses. 5. System Integration Build APIs and middleware to interface between the LLM, knowledge base, and user-facing applications. Develop scalable and efficient query-routing mechanisms for hybrid retrieval and generation tasks. Ensure seamless deployment of LLM-powered applications. 6. Validation and Testing Evaluate model responses against domain-specific benchmarks and ground truths. Collaborate with domain experts to refine system outputs. Conduct user testing and gather feedback to improve system performance iteratively. 7. Maintenance and Updates Implement strategies to keep the knowledge base current with periodic updates. Develop monitoring tools to track system performance and identify areas for improvement. Address ethical, regulatory, and privacy considerations (e.g., GDPR, HIPAA compliance). Qualifications: Technical Skills Programming: Strong knowledge of Python and frameworks like Flask, FastAPI, or LangChain for API development. Data Preprocessing: Familiarity with preprocessing pipelines for structured and unstructured data. LLM Proficiency: Experience with LLM platforms such as OpenAI GPT, Hugging Face Transformers, or similar. Knowledge Base Management: Hands-on experience with vector databases (e.g., Pinecone, Milvus, Weaviate) and relational databases (e.g., PostgreSQL, MySQL). Fine-Tuning Expertise: Proficiency in adapting LLMs for specialized domains using domain-specific datasets. RAG Implementation: Practical experience with retrieval-augmented generation workflows. Prompt Engineering: Ability to craft and optimize prompts for complex, context-driven tasks. Soft Skills Strong problem-solving skills and attention to detail. Ability to collaborate effectively with cross-functional teams, including domain experts. Excellent communication and documentation skills. Experience 6+ years of experience in AI/ML roles, focusing on LLM agent development and deployment in recent years. 2 + years would creating AI solution with langChain, experience. Demonstrated experience in designing domain-specific AI systems. Hands-on experience in integrating structured/unstructured data into AI models.

strong in NLP, embeddings Vector Search tools FAISS, Milvus, Pinecone, or ANN libraries Search Algorithms including cosine similarity, dot-product scoring, clustering methods. Strong coding abilities in Python libraries as NumPy, Pandas, Scikit-learn