6 Rag Architecture Jobs

Responsibilities: * Collaborate with dev team on API testing using GIT and CI/CD pipeline. * Develop automated tests with Python, PyTest, and frameworks.

Client Name: WIPRO Location: PAN INDIA Mode: Hybrid Job Description: Experience Required: 10+ years in AI/ML and Solution Architecture roles (Strong programming and system integration background required) Key Responsibilities: Troubleshoot and fix an existing BERT-integrated Outsystems+AMP solution, including scheduler code debugging. Translate complex business requirements into scalable and integrative AI/GenAI solutions. Architect and guide end-to-end implementation of AI systems aligned with Ericssons target architecture (e.g., TAMP). Lead architectural reviews and support development teams to ensure solution compliance. Manage risks associated with AI models via standard risk assessment protocols. Create, maintain, and own architectural documentation and blueprints. Present technical concepts and designs to stakeholders. Collaborate with cross-functional teams including development and platform teams for successful project delivery. Additional Responsibilities: Apply advanced ML algorithms to solve business problems and drive value. Build and deploy data science solutions at scale (AWS Sagemaker, Kubernetes, Docker). Stay updated with current AI trends and contribute to applied research, innovation, and IP creation (e.g., publications, patents). Deliver insights to business leaders to influence decision-making. Qualifications: Bachelors or Masters degree in Computer Science, Engineering, or related field. Proven track record in delivering AI/ML solutions (preferably in Finance domain). Recognized experience on data science platforms (e.g., Kaggle achievements is a plus). Strong coding background in Python and experience in cloud-native environments. Good to Have: Domain expertise in Finance. Contributions to open-source or research community (e.g., Kaggle, GitHub, papers).

Department/ Group : Advanced Rail Technology Job Description, duties & responsibilities Progress Rails data science team is looking for a motivated and talented Data Scientist who will primarily focus on developing Machine Learning/Artificial Intelligence based data models for condition-based monitoring of its assets. In this role, the candidate will contribute to the design, development and deployment of world class rail products and services vital to our customers needs. Reporting to the Director of Data Science, this role will enable innovative, strategic, and high-tech solutions for the rail industry through the application of specialized knowledge, skills, and abilities. Work involves independent judgement, problem solving skills, resourcefulness, teamwork, and creativity in ambiguous situations. A high degree of personal initiative is a prerequisite. Typical data science team efforts are a combination of some, or all the key job elements listed below. The ideal candidate is an experienced self-starter, strong attention to details, with excellent written and verbal skills. Enjoys working in a collaborative, fast-paced, environment and is willing to take on roles outside of comfort zone. Technical aptitude and being well versed in Machine Learning and Data Science tools and processes is a must. The role will work closely with the different engineering teams. Key Job Elements Contribute to the design, development, testing, and deployment of software systems and applications. Processing, cleansing, and verifying the integrity of data used for analysis. Apply Machine Learning and other advanced analytical techniques to develop models for condition-based monitoring of locomotive systems. Apply Natural Language Processing (NLP) and Large Language Model (LLM) to support text mining, document summarization and others. Understand the business needs and develop data-based solutions. Doing ad-hoc analysis and presenting results in a clear manner Supporting field reported issue resolution through data analysis System integration of Machine Learning models Mentor and assist data scientists providing technical assistance and direction as needed Technical Skill Experienced Data Scientist with 7+ years experience in Data Extraction, Data Modelling, Data Wrangling, Statistical Modeling, Data Mining, Machine Learning and Data Visualization. Expertise in transforming business resources and requirements into manageable data formats and analytical models, designing algorithms, building models, developing data mining, and reporting solutions that scale across a massive volume of structured and unstructured data. Proficiency in managing entire data science project life cycle and actively involved in all the phases of project life cycle including data acquisition, data cleaning, data engineering, features scaling, features engineering, testing and validation and data visualization. Expertise in applying Machine Learning algorithms (such as Regression Models, XGBoost, Neural Network, and others) for predictive analytics. Experience in Generative AI developing LLM based solutions for document search/summarization using RAG architecture. Expertise in applied statistics skills, such as distributions, statistical testing, regression, etc. Strong experience with Python, SQL, and R. Experience and knowledge of AWS cloud which includes Machine Learning related services, S3, Elastic search, Lambda, and others. Experience in integrating Machine Learning models into larger deployed systems. Proficiency in data visualization tools such as PowerBI, Python Matplotlib, R Shiny to create visually powerful and actionable interactive reports and dashboards. Experience in Natural Language Processing and Text Mining. Strong business sense and abilities to communicate data insights to both technical and non-technical clients. Competent to perform all job duties without close supervision. Desired : Rail industry experience Experience in developing models using telematics (sensor) data from equipment such as engines, machines, and others. Qualifications and Education Requirements B.S, M.S, or PhD degree in quantitative discipline such as data science, data analytics, computer science, engineering, statistics, mathematics, or other related degree. 7+ years of data science experience with B.S., or 5+ years of experience with Advanced degrees 7+ years of experience with Python, R, SQL, and relational data bases

Design and implement RAG-based solutions to enhance LLM capabilities with external knowledge sources Develop and optimize LLM fine-tuning strategies for specific use cases and domain adaptation Create robust evaluation frameworks for measuring and improving model performance Build and maintain agentic workflows for autonomous AI systems Collaborate with cross-functional teams to identify opportunities and implement AI solutions Required Qualifications: Bachelor's or Master's degree in Computer Science, or related technical field 3+ years of experience in Machine Learning/AI engineering Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow) Practical experience with LLM deployments and fine-tuning Experience with vector databases and embedding models Familiarity with modern AI/ML infrastructure and cloud platforms (AWS, GCP, Azure) Strong understanding of RAG architectures and implementation Preferred Qualifications: Experience with popular LLM frameworks (Langchain, LlamaIndex, Transformers) Knowledge of prompt engineering and chain-of-thought techniques Experience with containerization and microservices architecture Background in NLP and deep learning Background in Reinforcement Learning Contributions to open-source AI projects Experience with ML ops and model deployment pipelines Skills and Competencies: Strong problem-solving and analytical skills Excellent communication and collaboration abilities Experience with agile development methodologies Ability to balance multiple projects and priorities Strong focus on code quality and best practices Understanding of AI ethics and responsible AI development

Role Overview Join our Pune AI Center of Excellence to drive software and product development in the AI space. As an AI/ML Engineer, youll build and ship core components of our AI products—owning end-to-end RAG pipelines, persona-driven fine-tuning, and scalable inference systems that power next-generation user experiences. Key Responsibilities Model Fine-Tuning & Persona Design Adapt and fine-tune open-source large language models (LLMs) (e.g. CodeLlama, StarCoder) to specific product domains. Define and implement “personas” (tone, knowledge scope, guardrails) at inference time to align with product requirements. RAG Architecture & Vector Search Build retrieval-augmented generation systems: ingest documents, compute embeddings, and serve with FAISS, Pinecone, or ChromaDB. Design semantic chunking strategies and optimize context-window management for product scalability. Software Pipeline & Product Integration Develop production-grade Python data pipelines (ETL) for real-time vector indexing and updates. Containerize model services in Docker/Kubernetes and integrate into CI/CD workflows for rapid iteration. Inference Optimization & Monitoring Quantize and benchmark models for CPU/GPU efficiency; implement dynamic batching and caching to meet product SLAs. Instrument monitoring dashboards (Prometheus/Grafana) to track latency, throughput, error rates, and cost. Prompt Engineering & UX Evaluation Craft, test, and iterate prompts for chatbots, summarization, and content extraction within the product UI. Define and track evaluation metrics (ROUGE, BLEU, human feedback) to continuously improve the product’s AI outputs. Must-Have Skills ML/AI Experience: 3–4 years in machine learning and generative AI, including 18 months on LLM- based products. Programming & Frameworks: Python, PyTorch (or TensorFlow), Hugging Face Transformers. RAG & Embeddings: Hands-on with FAISS, Pinecone, or ChromaDB and semantic chunking. Fine-Tuning & Quantization: Experience with LoRA/QLoRA, 4-bit/8-bit quantization, and model context protocol (MCP). Prompt & Persona Engineering: Deep expertise in prompt-tuning and persona specification for product use cases. Deployment & Orchestration: Docker, Kubernetes fundamentals, CI/CD pipelines, and GPU setup. Nice-to-Have Multi-modal AI combining text, images, or tabular data. Agentic AI systems with reasoning and planning loops. Knowledge-graph integration for enhanced retrieval. Cloud AI services (AWS SageMaker, GCP Vertex AI, or Azure Machine Learning)

Position Summary: We are looking for a skilled and experienced Lead Data Scientist with a strong background in building AI-driven applications and solutions. In this role, you will play a critical role in advancing our generative AI capabilities, creating solutions that streamline processes, and optimizing data insights across the organization. The ideal candidate will be highly analytical, with a deep understanding of machine learning algorithms, advanced data processing techniques, and hands-on experience with Azure Open AI services. Key Responsibilities: AI Solution Development: Lead the development and enhancement of AI tools, including generative AI models, natural language processing (NLP) applications, and predictive analytics, that align with CDM Smiths business goals. Machine Learning & Deep Learning: Utilize supervised and unsupervised learning methods to address complex data science challenges. Apply advanced techniques, such as deep learning and neural networks, to optimize model performance. Data Engineering & Processing: Implement and improve data collection methods to ensure robust data pipelines. Focus on building systems that can efficiently process high volumes of data. Data Visualization: Design impactful visualizations using tools such as Power BI, making data insights accessible and actionable for stakeholders. Azure Open AI Expertise: Leverage Azures suite of AI tools, including LLMs and Custom Vision, for scalable, cloud-based deployment of AI solutions. Indexing & Document Optimization: Develop optimized indexing methods and document management strategies for high-volume data handling, especially within Azure Blob storage. Prompt Engineering: Apply prompt engineering techniques to improve the efficiency and accuracy of AI models, focusing on maximizing the effectiveness of LLM-based systems. Cross-functional Collaboration: Work with various teams to identify business challenges, translate them into data science projects, and implement data-driven solutions. Mentorship: Mentor junior data scientists, fostering an environment of growth and continuous learning. Required Qualifications: Educational Background: Bachelors or Masters degree in computer science, Data Science, Engineering, or a related field. Experience: At least 9+ years of relevant experience in data science, with a focus on AI model deployment and application development in a cloud environment, specifically Azure Open AI with at least 2 of those years in a supervisory or leadership capacity. Technical Skills: o Proficient in Python and SQL for data manipulation, model building, and analysis. o Extensive experience with large language models (LLMs) and RAG architecture. o Advanced skills in machine learning algorithms, deep learning, and NLP. o Familiarity with Azure Blob storage and optimization techniques for high-volume document processing. o Strong data visualization abilities, with proficiency in tools such as Power BI, Tableau, or similar. Problem-Solving Skills: Strong analytical and debugging skills to identify, analyze, and solve complex data challenges. Communication Skills: Ability to communicate complex technical concepts to non- technical stakeholders effectively. Preferred Qualifications: Additional Skills: o Experience with Azure Custom Vision and related AI tools. o Exposure to prompt engineering and document content optimization techniques. o Familiarity with data architecture and optimization practices for AI scalability. Soft Skills: o Ability to establish and build relationships across departments. o Strong organizational skills and attention to detail. o Ability to adapt quickly in a dynamic, fast-paced environment.