458 Rag Jobs - Page 17

Skills: Excellent knowledge of Data Structures and Algorithms Great programming skills in Python Working knowledge C++ Experience in AI/ML, Data Science, CNN, RNN, LLM Classification, Clustering. Job Responsibilities: Designing and developing AI/ML algorithms to solve different technical problems in the domain of 3GPP. Explaining the designed data science algorithms clearly in document and presenting the same to support the innovation. Working in a team and share the responsibilities of integration, productisation and deployment.

Role & responsibilities As a Data Scientist specializing in Generative AI and NLP , you will be at the forefront of AI innovation. Your role will involve designing and deploying sophisticated models, including large language models (LLMs), to solve complex business problems. You will work closely with cross-functional teams to create scalable, data-driven solutions that bring AI-driven creativity and intelligence to life across various industries. Generative AI & NLP Development : Design, develop, and deploy advanced applications and solutions using Generative AI models (e.g., GPT, LLaMA, Mistral) and NLP algorithms to solve business challenges and unlock new opportunities for our clients. Model Customization & Fine-Tuning : Apply state-of-the-art techniques like LoRA , PEFT , and fine-tuning of large language models to adapt solutions to specific use cases, ensuring high relevance and impact. Innovative Problem Solving : Leverage advanced AI methodologies to tackle real-world business problems, providing creative and scalable AI-powered solutions that drive measurable results. Data-Driven Insights : Conduct deep analysis of large datasets, uncovering insights and trends that guide decision-making, improve operational efficiencies, and fuel innovation. Cross-Functional Collaboration : Work closely with Consulting, Engineering, and other teams to integrate AI solutions into broader business strategies, ensuring the seamless deployment of AI-powered applications. Client Engagement : Collaborate with clients to understand their unique business needs, provide tailored AI solutions, and educate them on the potential of Generative AI to drive business transformation. What do we expect? Generative AI & NLP Expertise : Extensive experience in developing and deploying Generative AI applications and NLP frameworks , with hands-on knowledge of LLM fine-tuning , model customization , and AI-powered automation . Hands-On Data Science Experience : 6+ years of experience in data science, with a proven ability to build and operationalize machine learning and NLP models in real-world environments. AI Innovation : Deep knowledge of the latest developments in Generative AI and NLP , with a passion for experimenting with cutting-edge research and incorporating it into practical solutions. Problem-Solving Mindset : Strong analytical skills and a solution-oriented approach to applying data science techniques to complex business problems. Communication Skills : Exceptional ability to translate technical AI concepts into business insights and recommendations for non-technical stakeholders.

Role: Gen AI Engineer Employment Type: FTE with Dimiour Work Location: Remote Shift Timings: General Day Shift IST Time Zone Experience Required: 5+ Years GenAI Engineers 5+ years of experts who are proficient in Python and familiar with AI/Gen AI frameworks. Experience with data manipulation libraries like Pandas and NumPy is crucial. Specific expertise in implementing and managing large language models (LLMs) is a must. Fast API experience for API development Should have hands on experience in Agentic AI Ability to diagnose accuracy drops in Retrieval-Augmented Generation (RAG) systems A solid grasp of software engineering principles, including version control (Git), continuous integration and continuous deployment (CI/CD) practices, and automated testing, is required. Experience in MLOps, ML engineering, and Data Science, with a proven track record of developing and maintaining AI solutions, is essential. Preferred candidate profile Immediate - 15days

Position Title: AI/ML Engineer Company: Cyfuture India Pvt. Ltd. Industry: IT Services and IT Consulting Location: Sector 81, NSEZ, Noida (5 Days Work From Office) Website: www.cyfuture.com About Cyfuture Cyfuture is a trusted name in IT services and cloud infrastructure, offering state-of-the-art data center solutions and managed services across platforms like AWS, Azure, and VMWare. We are expanding rapidly in system integration and managed services, building strong alliances with global OEMs like VMWare, AWS, Azure, HP, Dell, Lenovo, and Palo Alto. Position Overview We are hiring an experienced AI/ML Engineer to lead and shape our AI/ML initiatives. The ideal candidate will have hands-on experience in machine learning and artificial intelligence, with strong leadership capabilities and a passion for delivering production-ready solutions. This role involves end-to-end ownership of AI/ML projects, from strategy development to deployment and optimization of large-scale systems. Key Responsibilities Lead and mentor a high-performing AI/ML team. Design and execute AI/ML strategies aligned with business goals. Collaborate with product and engineering teams to identify impactful AI opportunities. Build, train, fine-tune, and deploy ML models in production environments. Manage operations of LLMs and other AI models using modern cloud and MLOps tools. Implement scalable and automated ML pipelines (e.g., with Kubeflow or MLRun). Handle containerization and orchestration using Docker and Kubernetes. Optimize GPU/TPU resources for training and inference tasks. Develop efficient RAG pipelines with low latency and high retrieval accuracy. Automate CI/CD workflows for continuous integration and delivery of ML systems. Key Skills & Expertise 1. Cloud Computing & Deployment Proficiency in AWS, Google Cloud, or Azure for scalable model deployment. Familiarity with cloud-native services like AWS SageMaker, Google Vertex AI, or Azure ML. Expertise in Docker and Kubernetes for containerized deployments Experience with Infrastructure as Code (IaC) using tools like Terraform or CloudFormation. 2. Machine Learning & Deep Learning Strong command of frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost. Experience with MLOps tools for integration, monitoring, and automation. Expertise in pre-trained models, transfer learning, and designing custom architectures. 3. Programming & Software Engineering Strong skills in Python (NumPy, Pandas, Matplotlib, SciPy) for ML development. Backend/API development with FastAPI , Flask , or Django . Database handling with SQL and NoSQL (PostgreSQL, MongoDB, BigQuery). Familiarity with CI/CD pipelines (GitHub Actions, Jenkins). 4. Scalable AI Systems Proven ability to build AI-driven applications at scale. Handle large datasets, high-throughput requests, and real-time inference. Knowledge of distributed computing: Apache Spark, Dask, Ray . 5. Model Monitoring & Optimization Hands-on with model compression, quantization, and pruning . A/B testing and performance tracking in production. Knowledge of model retraining pipelines for continuous learning. 6. Resource Optimization Efficient use of compute resources: GPUs, TPUs, CPUs . Experience with serverless architectures to reduce cost. Auto-scaling and load balancing for high-traffic systems. 7. Problem-Solving & Collaboration Translate complex ML models into user-friendly applications. Work effectively with data scientists, engineers, and product teams. Write clear technical documentation and architecture reports . Udisha Parashar Senior Talent Acquisition Specialist Mob: +91- 9301895707 Email: udisha.parashar@cyfuture.com URL: www.cyfuture.com

Role & responsibilities Develop and maintain AI-based solutions using Python. Design, develop, and deploy AI-driven applications with a focus on GenAI Chatbots & AI Agents. Implement GenAI Chatbots, including ingestion and query pipelines, using at least one cloud platform ( Azure, or GCP). Implement Agentic AI framework-based solutions. Develop data ingestion pipelines for efficient data processing and transformation. Ensure high data integrity and performance in AI-driven workflows. Deploy AI models on cloud platforms such as Azure, or GCP. Implement best practices for scalability, security, and cost optimization in cloud-based AI solutions. Conduct regular performance tuning, debugging, and troubleshooting of AI models and applications. Implement monitoring and logging mechanisms for AI-driven systems. Work closely with cross-functional teams, including data scientists, ML engineers, and software developers, to integrate AI solutions. Document technical specifications, workflows, and best practices for AI development. Stay updated with the latest advancements in GenAI, Agentic AI technologies to bring innovation into the organization. Preferred candidate profile Extensive experience in AI software development with strong proficiency in Python. Hands-on experience with GenAI, Agentic AI. Experience in developing Generative AI chatbots, Agentic AI frameworks including ingestion pipelines, query pipelines, and Retrieval-Augmented Generation (RAG) using at least one cloud platform. Solid understanding of machine learning techniques and algorithms. Strong knowledge of data structures, algorithms, and software design principles. Healthcare domain experience is a plus.

We are seeking a skilled and experienced Machine Learning Engineer specialized in Natural Language Processing (NLP) and Computer Vision to join our team. The ideal candidate should have a strong foundation in ML and AI principles, coupled with a proven track record of developing and deploying a minimum of 20+ models in these domains. Role & responsibilities Develop and implement state-of-the-art machine learning algorithms and models for NLP and computer vision applications. Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions. Conduct thorough data analysis, pre-processing, and feature engineering to extract meaningful insights from structured and unstructured data. Design and implement scalable and efficient machine learning pipelines for training, evaluation, and inference. Experiment with various deep learning architectures, hyperparameters, and optimization techniques to improve model performance. Stay updated with the latest advancements in ML and AI research and evaluate their potential applications in our domain. Work closely with software engineers to integrate ML models into production systems and ensure their robustness and scalability. Perform rigorous testing and validation of models to ensure their accuracy, reliability, and generalization capability. Continuously monitor and maintain deployed models, and implement necessary updates and enhancements as required. Document code, methodologies, and findings effectively, and contribute to internal knowledge sharing initiatives Preferred candidate profile Strong theoretical understanding of machine learning algorithms, deep learning frameworks, and statistical modeling techniques. Proficiency in programming languages such as Python, along with experience in popular ML libraries such as TensorFlow, PyTorch, or scikit-learn. Hands-on experience in developing and deploying machine learning models for NLP and computer vision tasks, with a demonstrable portfolio of projects. Solid understanding of natural language processing techniques, including text classification, sentiment analysis, named entity recognition, etc. Familiarity with computer vision concepts such as image classification, object detection, image segmentation, etc., and experience with frameworks like OpenCV or TensorFlow. Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes) is a plus.

Role & 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 pipelinesincluding data preprocessing, model training, versioning, testing, and deploymentusing 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, andprovide advice on the adoption and operationalization of Generative AI and ML. Preferred candidate profile Overall 5-9 years of experience with real-time experience in GenAI and MLE/MLOps. Expertise in Generative AI: Hands-on experience in designing and deploying LLM-based solutions with frameworks such as HuggingFace, LangChain, Transformers, etc. MLE & Production Readiness: Proven experience in building ML models that are scalable, reliable, and production-ready, including exposure to MLE/MLOps workflows and tools. Deployment Tools & Best Practices : Familiarity with containerization (Docker), orchestration (Kubernetes), model tracking (MLflow), and cloud platforms (AWS/GCP/Azure) for deploying AI solutions at scale. Proficiency in development using Python frameworks (such as Django/Flask) or other similar technologies. In-depth understanding of APIs, microservices architecture, and cloud-based deployment strategies. Innovation & Curiosity: A passion for staying updated with the latest in Gen AI, LLMs, and ML engineering practices. Communication : Ability to translate complex technical concepts into business-friendly insights and recommendations

Role & 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 pipelinesincluding data preprocessing, model training, versioning, testing, and deploymentusing 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, andprovide advice on the adoption and operationalization of Generative AI and ML. Preferred candidate profile Overall 5-9 years of experience with real-time experience in GenAI and MLE/MLOps. Expertise in Generative AI: Hands-on experience in designing and deploying LLM-based solutions with frameworks such as HuggingFace, LangChain, Transformers, etc. MLE & Production Readiness: Proven experience in building ML models that are scalable, reliable, and production-ready, including exposure to MLE/MLOps workflows and tools. Deployment Tools & Best Practices : Familiarity with containerization (Docker), orchestration (Kubernetes), model tracking (MLflow), and cloud platforms (AWS/GCP/Azure) for deploying AI solutions at scale. Proficiency in development using Python frameworks (such as Django/Flask) or other similar technologies. In-depth understanding of APIs, microservices architecture, and cloud-based deployment strategies. Innovation & Curiosity: A passion for staying updated with the latest in Gen AI, LLMs, and ML engineering practices. Communication : Ability to translate complex technical concepts into business-friendly insights and recommendations

Role & 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 pipelinesincluding data preprocessing, model training, versioning, testing, and deploymentusing 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, andprovide advice on the adoption and operationalization of Generative AI and ML. Preferred candidate profile Overall 5-9 years of experience with real-time experience in GenAI and MLE/MLOps. Expertise in Generative AI: Hands-on experience in designing and deploying LLM-based solutions with frameworks such as HuggingFace, LangChain, Transformers, etc. MLE & Production Readiness: Proven experience in building ML models that are scalable, reliable, and production-ready, including exposure to MLE/MLOps workflows and tools. Deployment Tools & Best Practices : Familiarity with containerization (Docker), orchestration (Kubernetes), model tracking (MLflow), and cloud platforms (AWS/GCP/Azure) for deploying AI solutions at scale. Proficiency in development using Python frameworks (such as Django/Flask) or other similar technologies. In-depth understanding of APIs, microservices architecture, and cloud-based deployment strategies. Innovation & Curiosity: A passion for staying updated with the latest in Gen AI, LLMs, and ML engineering practices. Communication : Ability to translate complex technical concepts into business-friendly insights and recommendations

Minimum experience: 5 years. Basic Guidelines : Ensure all submitted CVs are up to date. Maximum Notice Period 3 weeks Willingness to go for face 2 face interview in 24 hours. Employment verification 5 years and 7 years criminal background check. No fake and proxy interviews. Vendors will be blacklisted by TSI/ our Client, if guilty. Maintain the below tracker. Java Fullstack developer Primary skills: Java 8/11, Spring boot, Microservices, Rest API, UI REACT JS, REDUX Location: Bangalore and Chennai 2. Python + UI/API developer Primary skills: Python, Rest API, UI REACT JS, REDUX Location: Gurgaon 3.UI Engineer Primary skills: UI REACT JS, REDUX, JavaScript,ES6/Typescript,HTML5,CSS3,Bootstrap) Secondary skills: Java 8/11, Spring boot, Microservices, Rest API Location: Gurgaon and Bangalore 4. API Automation tester + Java scripting API Automation testing, Java scripting, SQL, MongoDB, Selenium Secondary skills: Rest assured / karate, Cucumber framework. DB Concepts, json queries Location: Bangalore and Gurgaon Job Description: 5+ years in Quality Assurance and testing. Demonstrated experience with Java or Python. Good experience in API automation using Selenium (Rest Assured or any other framework for automation and POSTMAN for manual). Preferably worked on creating frameworks. Must be well versed in BDD Cucumber frameworks. Working knowledge of MySQL and its implementation. Experience in creating scripts through REST APIs. Able to manage multiple work streams, independent, able to communicate well. Required Details Candidate's Full Name (As per passport): Contact Number: Email ID: Passport: LinkedIn ID link: Location (City & State): Relocation (Yes / No): Availability to join the project: Total working Experience: Currently on project (Yes/No): Have you Ever worked with Client: (If yes details) Any Interviews Pending: Any Offers in hand: Have you been submitted by Client: Any Issue with BGC: Bachelors details: Masters details: Rate: please submit your resumes to Naveen@tanishasystems.com

Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 4 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, Agentic Framework to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 4 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models Utilize optimization tools and techniques, including MIP (Mixed Integer Programming. Deep knowledge of classical AIML (regression, classification, time series, clustering) Drive DevOps and MLOps practices, covering CI/CD and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models.

Role: AI Engineer Microsoft CoPilot Experience: 4 - 8 Years Location: Pune. Mandatory Skills: Microsoft Copilot, Gen AI, Azure AI, LLM, Langchain, RAG. Notice Period: Immediate Joiners Job Description: Requirements: Bachelors or Master’s degree in Computer Science, Data Science, or a related field. Proven experience in developing and deploying AI/ML models in real-world applications. Strong programming skills, especially in Python, and familiarity with version control systems like Git. Extensive experience in the Microsoft environment, with expertise in CoPilot and related technologies. Excellent problem-solving and communication skills. Ability to work independently and collaboratively in a fast-paced, dynamic environment. Responsibilities: Agent Development: Design, implement, and optimize AI agents using Microsoft CoPilot and related technologies. Develop custom AI solutions leveraging Power Automate, Azure OpenAI, and other Microsoft tools. Solution Integration: Deploy AI solutions within client environments, ensuring scalability and seamless integration with exiiting systems. Work with stakeholders to identify automation opportunities and tailor solutions to business needs. AI Algorithm and Model Implementation: Design and implement machine learning algorithms, focusing on natural language processing (NLP) and conversational AI. Perform data preprocessing, feature engineering, and model training to create high-performing solutions. Collaboration and Support: Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to deliver integrated solutions. Provide technical guidance and support to ensure the successful adoption and use of AI-driven tools. Continuous Improvement: Stay updated on advancements in AI, machine learning, and Microsoft’s AI technologies. Contribute to knowledge sharing by conducting training sessions and documenting best practices. Preferred Skills: Strong knowledge of Microsoft Power Automate, Azure AI, and CoPilot features. Proficiency in Python and its AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Familiarity with building and managing cloud-based solutions, preferably on Microsoft Azure. Understanding of conversational AI technologies and chatbot frameworks. Experience with data analysis tools and techniques to uncover insights and optimize models.

Hiring AI & Data Retrieval Engineer with expertise in NLQ, Text-to-SQL, LLMs, LangChain, pgVector, PostgreSQL, vector search, Python, AI libraries, Agentic AI & API integration. Exp with NLP, RAG, BI tools, live projects & LLM fine-tuning preferred.

Preferred candidate profile 1. LLM Basics : (Llama, Gemini ) : Understand the basics of generative AI and LLMs, such as key terminology, uses, potential issues, and primary frameworks. One should know what the data is trained on and any potential biases/issues that there may be with the data . Knowledge on know exactly how big LLMs can be, how computationally expensive training will be, and the differences between training LLMs and machine learning models. 1. Prompt Engineering : Knowledge on designing inputs for LLMs once theyre developed. 2. Prompt Engineering with OpenAI : As a leading figure in LLMs and generative AI, it’s important to know how to use prompt engineering specifically with OpenAI tools, as you’ll likely be using them at some point in your career. 3. Question-Answering :Question-answering (QA) LLMs are a type of large language model that has been trained specifically to answer questions. 4. Fine-Tuning : Knowledge on Fine-tuning to improve the performance of an LLM on a variety of tasks, including text generation, translation, summarization, and question-answering. Customize LLMs for specific applications, such as customer service chatbots or medical diagnosis systems. Awareness on supervised learning. This involves providing the LLM with a dataset of labelled data, where each data point is a pair of input and output.. 1. Lang Chain : To architect complex LLM pipelines by chaining multiple models together (Classification, text generation, code generation, etc.)`Agents` to interact with all these external systems to execute actions dictated by LLMs. 2. Parameter Efficiency/Tuning : LORA 3. RAG Building : Generative AI, mastering RAG building—short for Retrieval-Augmented Generation—is becoming increasingly crucial 4. ML OPS and in particular LLMOps : Large Language Model Operations, is the practice of managing and maintaining large language models (LLMs) in a production setting 5. TensorFlow i s like a versatile toolbox for creating intelligent programs that can learn and understand various concepts, including machine learning, deep learning, and data science

Role & responsibilities We are looking for Gen AI Engineer position permanent position with US MNC for Bangalore/Chennai/Pune/Noida/Gurgaon location (Hybrid). Preferred candidate profile Experience Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions. The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration. Key Responsibilities: Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines. Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions. Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives. Translate business needs into scalable, production-ready AI solutions. Work closely with business SMEs and data teams to ensure alignment of AI models with real-world use cases. Contribute to architecture discussions, code reviews, and performance optimization. Skills Required: Proficient in Python, Langchain, and SQL. Understanding of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows. Background in machine learning or software engineering with a focus on system-level thinking. Experience working with cloud platforms like AWS, Azure, or GCP. Ability to work independently while collaborating effectively across teams. Excellent communication and stakeholder management skills. Preferred Qualifications: Hands-on experience in LLMs and Generative AI techniques. Experience contributing to ML/AI product pipelines or end-to-end deployments. Familiarity with MLOps and scalable deployment patterns for AI models. Prior exposure to client-facing projects or cross-functional AI teams.

Role & responsibilities We are looking for Gen AI Engineer position permanent position with US MNC for Bangalore/Chennai/Pune/Noida/Gurgaon location (Hybrid). Preferred candidate profile Experience Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions. The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration. Key Responsibilities: Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines. Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions. Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives. Translate business needs into scalable, production-ready AI solutions. Work closely with business SMEs and data teams to ensure alignment of AI models with real-world use cases. Contribute to architecture discussions, code reviews, and performance optimization. Skills Required: Proficient in Python, Langchain, and SQL. Understanding of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows. Background in machine learning or software engineering with a focus on system-level thinking. Experience working with cloud platforms like AWS, Azure, or GCP. Ability to work independently while collaborating effectively across teams. Excellent communication and stakeholder management skills. Preferred Qualifications: Hands-on experience in LLMs and Generative AI techniques. Experience contributing to ML/AI product pipelines or end-to-end deployments. Familiarity with MLOps and scalable deployment patterns for AI models. Prior exposure to client-facing projects or cross-functional AI teams.

Role & responsibilities Lead the architecture and deployment of LLM and GenAI-based applications. Fine-tune and optimize state-of-the-art LLMs including GPT, LLaMA, Claude, Mistral, etc. Design and implement advanced prompt engineering and Retrieval-Augmented Generation (RAG) systems. Collaborate with data scientists, backend engineers, and product teams to deliver robust AIdriven solutions. Ensure security, performance, and cost optimization in model training and deployment. Translate complex business requirements into scalable GenAI solutions. Maintain best practices in code quality, version control, and documentation. Proven expertise in LLM fine-tuning, deployment, and inference workflows. Strong experience in Prompt Engineering and RAG (Retrieval-Augmented Generation) systems. Proficient in Python with hands-on experience in building AI pipelines. Demonstrated ability to lead system design and architecture for GenAI applications Preferred candidate profile Familiarity with GenAI toolkits such as LangChain, LlamaIndex, Haystack, or custom-built frameworks. Exposure to Microsoft Azure (preferred over AWS or GCP) for cloud deployments. Experience with scalable, secure AI infrastructure and CI/CD pipelines for ML models

AI/ML Architecture Development Implement and deploy machine learning models in production environments Work with generative AI models and NLP techniques (e.g., OpenAI, Claude APIs) Apply prompt engineering and retrieval-augmented generation (RAG) techniques Build end-to-end ML workflows from data preprocessing to model evaluation Contribute to the development of conversational AI solutions Software Engineering Data Pipeline Development Design, build, and maintain data pipelines and end-to-end ML workflows. Build and deploy web services that integrate ML models Implement APIs using frameworks like FastAPI or Flask Work with databases (preferably MySql) and data processing tools Ensure code quality, performance, and security in all implementations Deployment Integration Build production-grade ML models and APIs and deploy them on cloud platforms with the help of the DevOps team. Ability to Monitor, analyze, and optimize the performance of deployed models and data workflows with the help of Devops team Collaboration Work closely with cross-functional teams including data scientists and software engineers Contribute to a culture of learning and innovation Adapt to challenges even when requirements are ambiguous Technical Requirements: Programming Frameworks: Strong proficiency in Python with hands-on experience in TensorFlow, PyTorch, Keras, and related ML libraries/Ecosystem. Experience with data science tools (pandas, NumPymatplotlib, scikit-learn) Generative AI Expertise: Knowledge of generative AI models and frameworks, including OpenAI APIs, LangChain, LangGraph, Hugging Face Transformers, and related technologies. Experience in fine-tuning large language models (LLMs) and implementing RAG systems leveraging vector databases like Pinecone or similar. Experience in developing multi-agent Retrieval-Augmented Generation (RAG) applications, integrating automated workflows to streamline data retrieval,processing, and response generation. API Development: Experience in developing and deploying APIs using frameworks like FastAPI or Flask. Qualifications: Bachelor s degree in Computer Science, Engineering or related field. 5+ years of hands-on experience in AI/ML engineering, with expertise in generative AI and data engineering. Excellent problem-solving, analytical, and communication skills. Ability to work independently in a fast-paced, dynamic environment while effectively collaborating with cross-functional teams.

AI/ML Architecture Development Implement and deploy machine learning models in production environments Work with generative AI models and NLP techniques (e.g., OpenAI, Claude APIs) Apply prompt engineering and retrieval-augmented generation (RAG) techniques Build end-to-end ML workflows from data preprocessing to model evaluation Contribute to the development of conversational AI solutions Software Engineering Data Pipeline Development Design, build, and maintain data pipelines and end-to-end ML workflows. Build and deploy web services that integrate ML models Implement APIs using frameworks like FastAPI or Flask Work with databases (preferably MySql) and data processing tools Ensure code quality, performance, and security in all implementations Deployment Integration Build production-grade ML models and APIs and deploy them on cloud platforms with the help of the DevOps team. Ability to Monitor, analyze, and optimize the performance of deployed models and data workflows with the help of Devops team Collaboration Work closely with cross-functional teams including data scientists and software engineers Contribute to a culture of learning and innovation Adapt to challenges even when requirements are ambiguous Technical Requirements: Programming Frameworks: Strong proficiency in Python with hands-on experience in TensorFlow, PyTorch, Keras, and related ML libraries/Ecosystem. Experience with data science tools (pandas, NumPymatplotlib, scikit-learn) Generative AI Expertise: Knowledge of generative AI models and frameworks, including OpenAI APIs, LangChain, LangGraph, Hugging Face Transformers, and related technologies. Experience in fine-tuning large language models (LLMs) and implementing RAG systems leveraging vector databases like Pinecone or similar. Experience in developing multi-agent Retrieval-Augmented Generation (RAG) applications, integrating automated workflows to streamline data retrieval,processing, and response generation. API Development: Experience in developing and deploying APIs using frameworks like FastAPI or Flask. Qualifications: Bachelor s degree in Computer Science, Engineering or related field. 5+ years of hands-on experience in AI/ML engineering, with expertise in generative AI and data engineering. Excellent problem-solving, analytical, and communication skills. Ability to work independently in a fast-paced, dynamic environment while effectively collaborating with cross-functional teams.

RAG Pipeline Architectures Fine/Prompt/Instruction Tuning of LLMs machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn). data wrangling, data cleaning, data preprocessing, and data

In this role you will: Develop and fine-tune LLMs for contract analysis, regulatory classification, and risk assessment. Implement Retrieval-Augmented Generation (RAG) using vector embeddings and hybrid DB-based querying to power DPIA and compliance workflows. Build AI-driven contract analysis systems to detect dark patterns, classify clauses, and provide remediation suggestions. Develop knowledge graph-based purpose taxonomies for privacy policies and PII classification. Automate data discovery for structured and unstructured data, classifying it into PII categories. Optimize sliding window chunking, token-efficient parsing, and context-aware summarization for legal and compliance texts. Build APIs and ML services for deploying models in a high-availability production environment. Collaborate with privacy, legal, and compliance teams to build AI solutions that power Privy s data governance tools. Stay ahead of the curve with agentic RAG, multi-modal LLMs, and self-improving models in the compliance domain. Skills Required: LLM , RAG , AgenticAI , NLP , Python , Problem solving Candidate Attributes: Must-Have Skills 3-5 years of experience in Machine Learning, NLP, and LLM-based solutions. Strong expertise in fine-tuning and deploying LLMs (GPT-4, Llama, Mistral, or custom models). Experience with RAG-based architectures, including vector embeddings (FAISS, ChromaDB, Weaviate, Pinecone, or similar). Hands-on with agentic RAG, sliding window chunking, and efficient context retrieval techniques. Deep understanding of privacy AI use cases, including contract analysis, regulatory classification, and PII mapping. Proficiency in Python and frameworks like PyTorch, TensorFlow, JAX, Hugging Face, or LangChain. Experience in building scalable AI APIs and microservices. Exposure to MLOps practices, including model monitoring, inference optimization, and API scalability. Experience working with at least one cloud provider (AWS, GCP, or Azure). Good-to-Have Skills Experience in hybrid AI architectures combining vector search + relational databases. Familiarity with functional programming languages (Go, Elixir, Rust, etc.). Understanding of privacy compliance frameworks (DPDP Act, GDPR, CCPA, ISO 27701). Exposure to Kubernetes, Docker, and ML deployment best practices. Contributions to open-source LLM projects or privacy AI research.

Position Summary: As a member of Redaptive's AI team, you will be driving Agentic AI and Generative AI integration across all of Redaptives business units. You will drive AI development and integration across the organization, directly impacting Redaptives global sustainability efforts and shaping how we leverage AI to serve Fortune 500 clients. Responsibilities and Duties: Strategic Leadership (10%): Champion the AI/ML roadmap, driving strategic planning and execution for all initiatives. Provide guidance on data science projects (Agentic AI, Generative AI, and Machine Learning), aligning them with business objectives and best practices. Foster a data-driven culture, advocating for AI-powered solutions to business challenges and efficiency improvements. Collaborate with product management, engineering, and business stakeholders to identify opportunities and deliver impactful solutions Technical Leadership (40%): Architect and develop Proof-of-Concept (POC) solutions for Agentic AI, Generative AI, and ML. Utilize Python and relevant data science libraries, leveraging MLflow. Provide technical guidance on AI projects, ensuring alignment with business objectives and best practices. Assist in developmentand documentation of standards for ethical and regulatory-compliant AI usage. Stay current with AI advancements, contributing to the team's knowledge and expertise. Perform hands-on data wrangling and AI model development Operational Leadership (50%): Drive continuous improvement through Agentic AI, Generative AI, and predictive modeling. Participate in Agile development processes (Scrum and Kanban). Ensure compliance with regulatory and ethical AI standards. Other duties as assigned Required Abilities and Skills: Agentic AI development and deployment. Statistical modeling, machine learning algorithms, and data mining techniques. Databricks and MLflow for model training, deployment, and management on AWS. Working with large datasets on AWS and Databricks Strong hands-on experience with: Agentic AI development and deployment. Working with large datasets on AWS and Databricks. Desired Experience: Statistical modeling, machine learning algorithms, and data mining techniques. Databricks and MLflow for model training, deployment, and management on AWS. Experience integrating AI with IoT/event data. Experience with real-time and batch inference integration with SaaS applications. International team management experience. Track record of successful product launches in regulated environments Education and Experience: 5+ years of data science/AI experience Bachelor's degree in Statistics, Data Science, Computer Engineering, Mathematics, or a related field (Master's preferred). Proven track record of deploying successful Agentic AI, Generative AI, and ML projects from concept to production. Excellent communication skills, able to explain complex technical concepts to both technical and non-technical audiences.

Role & responsibilities Expertise in Data Analysis, Statistics, Computer Vision, AI & Machine Learning Concepts Unflinching zeal to learn, to try out Proof of Technology for new and emerging AI/ML Design and develop applications powered by Large Language Models (LLMs) like GPT-4, Claude, Gemini, and open-source models (LLaMA, Mistral, etc.) Implement RAG pipelines using LangChain and LangGraph for production-ready applications. Build and orchestrate autonomous and tool-using LLM agents. Integrate with vector databases (e.g., Pinecone, Chroma, Weaviate, FAISS) for semantic search and memory storage. Use tools like OpenAI Function Calling, React, or LangGraph Agents for decision-based workflows. Deploy applications using cloud platforms (Azure, AWS, GCP) with APIs, microservices, or serverless functions. Collaborate with product and design teams to develop chatbots, copilots, and multimodal interfaces. Monitor performance, cost, latency, and evaluate prompt effectiveness through prompt engineering best practices. Fine-tune, quantize, or use adapters (LoRA, PEFT) for open-source models where applicable. Stay up-to-date with the latest advancements in Gen AI, MLOps, and AI safety principles. Preferred candidate profile Bachelor's/Master's degree in Computer Science, Mathematics, Statistics, or a related field. 4-9 years of experience as a Data Scientist with strong skills in Python, NLP, Deep Learning, and Statistics. Hands-on experience with Generative AI, LangChain, LangGraph, and building LLM Agents for real-world AI applications. Strong Python skills with experience in deploying Gen AI apps using frameworks like FastAPI or Streamlit. Expertise in Retrieval-Augmented Generation (RAG) and working with vector databases like Pinecone, FAISS, or Chroma. Deep understanding of prompt engineering, OpenAI function calling, and chaining logic for dynamic interactions. Familiarity with cloud platforms such as Azure and AWS and deploying models to these platforms. Strong problem-solving and analytical skills with excellent communication and presentation skills. Disclaimer: The following job description serves as an informative reference for the tasks you may be required to perform. However, it does not constitute an integral component of your employment agreement and is subject to periodic modifications to align with evolving circumstances

AIML Engineer, NLP, LLM, AWS, RAG Systems

Role & responsibilities We are seeking a highly skilled and passionate GenAI & Data Science Engineer with 5-6 years of experience in Python development, Generative AI, and Data Science. The ideal candidate will have a strong background in AI agent workflows, LLM fine-tuning, and Retrieval-Augmented Generation (RAG) models. You will play a key role in designing, developing, and deploying cutting-edge AI solutions using frameworks such as Lang Chain, Llama Index, and Hugging Face. This role offers the opportunity to work on transformative AI-driven solutions, leveraging state-of-the-art tools and frameworks to create impactful solutions in real-world applications. Key Responsibilities: Design, develop, and deploy AI solutions with a focus on Generative AI and Data Science. Fine-tune Large Language Models (LLM) and implement Retrieval-Augmented Generation (RAG) models. Collaborate with cross-functional teams to integrate AI models into business workflows. Utilize frameworks such as Lang Chain, Llama Index, and Hugging Face to build scalable AI solutions. Participate in end-to-end AI model development, including data preprocessing, model selection, training, evaluation, and deployment. Continuously monitor and optimize the performance of AI models to ensure they meet business requirements. Work with stakeholders to understand AI requirements and contribute to solution design and architecture. Stay up to date with the latest advancements in AI technologies and industry trends Preferred candidate profile Qualifications Bachelors or Master’s degree in Computer Science, Data Science, AI, or a related field. 3-5 years of professional experience in Python development, AI, and Data Science. Proven experience with Generative AI, including fine-tuning LLMs and working with RAG models. Hands-on experience with frameworks like Lang Chain, Llama Index, and Hugging Face. Strong understanding of machine learning algorithms, deep learning, and natural language processing (NLP). Experience in AI model deployment and scaling in production environments. Technical Skills Programming: Python, including libraries like TensorFlow, PyTorch, Pandas, NumPy, etc. AI/ML Frameworks: Lang Chain, Llama Index, Hugging Face, etc. Machine Learning Algorithms: Supervised and Unsupervised Learning, NLP, Reinforcement Learning. Data Engineering: Data preprocessing, data wrangling, ETL processes. Cloud Platforms: AWS, GCP, Azure (experience with AI tools on cloud platforms). Version Control: Git, GitHub, GitLab. Familiarity with containerization tools like Docker and Kubernetes. Soft Skills Strong problem-solving skills and analytical thinking. Excellent communication and collaboration skills. Ability to work independently and as part of a team. Adaptability to evolving technologies and requirements. Strong attention to detail and high quality of work. Time management and ability to meet deadlines. Work Experience 3-5 years of experience working in AI, Data Science, or a related field. Practical experience in working with Generative AI, LLM fine-tuning, and RAG models. Experience with deployment of AI models in cloud environments. Proven track record delivering AI-driven solutions to solve real business problems. Good to Have Experience with other AI tools and frameworks like OpenAI GPT, DeepPavlov, or similar. Exposure to data integration and API development. Knowledge of advanced topics in NLP, such as transformers and attention mechanisms. Experience with building AI-powered applications or chatbots. Compensation & Benefits Salary: Competitive base salary based on experience and skills. Bonus: Annual performance-based bonus. Benefits: Health insurance, paid time off, work-from-home options, and retirement benefits. Learning & Development: Access to AI and Data Science training, conferences, and certifications. Key Performance Indicators (KPIs) & Key Result Areas (KRAs) KPIs: Timely delivery of AI projects and solutions. Quality and accuracy of fine-tuned AI models. Successful integration of AI solutions into business workflows. Continuous improvement in AI model performance (accuracy, speed, scalability). Stakeholder satisfaction and feedback on AI-driven solutions. Contribution to knowledge sharing and team collaboration. KRAs: AI model development, fine-tuning, and deployment. End-to-end ownership of AI solution delivery. Collaboration with cross-functional teams to define and implement business requirements. Optimization and monitoring of AI solutions in production environments.