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Greetings ! At present we have an urgent leadership opening with an esteemed client. Position : AI Architect - AVP / SAVP Job Location : Noida / Gurgaon / Pune / Bangalore ( Hybrid Model ) Mandatory Skills Required - AI Architect, Data Science, GenAI, Vertex AI, Python, NLP, Deep Learning Frameworks Designation- AI Architect Role- Permanent/ Full time Experience- 11-16 years Location- Noida/ Gurgaon/ Pune/ Bangalore Shift- 12:00 PM to 10PM (10 Hours Shift. Also depends on the project/work dependencies) Working Days- 5 days Work Mode- Hybrid Mandatory Skills- Transformer Models - either finetune or training on Open-Source Transformer based models. Build and fine-tune large-scale language models using PyTorch and modern libraries. Should have done projects on LLM, NLP, LLAMA, Hugging face, RAG, Gen AI. Hands-on on Python coding experience. Machine Learning or Deep Learning. Good experience into Agentic AI and should have done end -to-end projects in Gen AI. Skills: 1. Proven experience as an NLP and ML Engineer or similar role 2. Have worked on open-source LLM (like Falcon and llama) for various text generation task 3. Have done SFT or PEFT on decoder based LLM for a specific text generation task 4. Understanding of NLP techniques for text representation, semantic extraction techniques, data structures and modeling 5. Have worked on BERT models for intent classifiers and entity extraction techniques 6. Ability to write robust and testable code 7. Experience with machine learning frameworks (PyTorch) 8. Knowledge of Python, Groovy Script, BPMN 9. An analytical mind with problem-solving abilities 10. Degree in Computer Science, Mathematics, Computational Linguistics or similar field Responsibilities: 1. Study and transform data science prototypes 2. Design NLP applications 3. Use effective text representations to transform natural language into useful features 4. Find and implement the right algorithms and tools for NLP tasks 5. Develop NLP systems according to requirements 6. Train the developed model and run evaluation experiments 7. Perform statistical analysis of results and refine models 8. Extend ML libraries and frameworks to apply in NLP tasks 9. Remain updated in the rapidly changing field of machine learning Plz reply to the following queries along with your updated resume : Current Organization : Current Designation : Reason for change : Reporting to : Date of Birth : Qualification : PAN Number : Current CTC (Fixed + Variable) : CTC Expectation : Any other offer in hand or in process : If yes, Please mention the offer details : Official Notice Period : Are you serving the Notice period : Mention your Last Working Date : Present Team size : Maximum Team Size handled : Location Staying at : Location Interested in (Noida / Gurgaon / Pune / Bangalore) : Technical Skills Expertise : Domains worked on : Total years of experience : Yrs of exp as AI Architect : Yrs of exp in Data Scientist role : Yrs of exp in Artificial Intelligence : Yrs of exp in GenAI : No. of end -to-end projects done in Gen AI : Yrs of exp in Transformer based models : Yrs of exp in Open-Source Transformer based models : Mention the Transformer based models worked on (like BERT, T5, RoBerta) : Yrs of exp in Large Language Models (LLMs) : Yrs of exp in NLP : Yrs of exp into Hugging face : Yrs of exp in Retrieval-Augmented Generation (RAG) : Yrs of exp in Deep Learning Framework : Mention the deep learning frameworks you have worked on : Yrs of exp in Machine learning : Yrs of exp in open-source LLM (like Falcon and llama) : Mention the Open-source transformer models worked on : Yrs of exp in Pytorch : Yrs of exp in Tensorflow, Keras : Yrs of exp in Python : How much would you rate yourselves in Python on a scale of 1-10 (10 being the highest) : Yrs of exp in cloud platforms (AWS, Azure, GCP) : Ever interviewed by EXL SERVICE in past : About client : Client has been 25+ years in existence with 29000+ employees globally and deals in: Digital Intelligence: Outcomes: Real digital transformation creates deeper customer experiences, faster speed to market, growing revenues and profitability. Everything we do is outcome oriented, ensuring the business is not only transformed, its built to stay ahead. Context: Were experts in more than technology and advanced analytics; we’re experts in our clients’ industries and businesses. This enables us to look deeper to identify and capitalize on opportunities to outperform. Orchestration: Our digital specialists understand both technology and the context in which it is applied. This enables to orchestrate complex and interdependent technologies – AI, robotics, analytics, machine learning and more – to deliver targeted solutions. Operations: BFSI (Banking, Financial service & Insurance) IT (Product Based Organization) & Consulting, BPO Analytics: Acquired Inductis. 2000+ employees in analytics division. Ranked 2nd best Analytics organization across the world. 22 Offices in India : 6 in Noida, 3 in Gurgaon / Pune / Chennai / Kochi each and 1 in Jaipur / Bangalore / Hyderabad / Ahmedabad. Thanks and Regards, Rashmi K. Vishwakarma M : 9833965671 / 9321442718 rashmi@ultimatesearch.in Ultimate Search Pvt. Ltd. Life is too short for a wrong job

About the Role We are seeking a highly skilled Senior NLP Engineer with expertise in large language models (LLMs), prompt engineering, and transformer-based architectures. The ideal candidate will have strong experience fine-tuning, deploying, and optimizing advanced NLP models for real-world applications such as summarization, text generation, and question answering. You will collaborate with cross-functional teams to design scalable, production-ready solutions while addressing challenges such as bias, hallucinations, and knowledge cutoffs. Key Responsibilities: Fine-tune pre-trained models on domain-specific datasets to optimize for summarization, text generation, question answering, and related tasks. Prompt Engineering: Design, test, and iterate on contextually relevant prompts to guide model outputs for desired performance. Implement and refine instruction-based prompting strategies to achieve contextually accurate results. Apply zero-shot, few-shot, and many-shot learning methods to maximize model performance without extensive retraining. Leverage Chain-of-Thought (CoT) prompting for structured, step-by-step reasoning in complex tasks. Evaluate model performance using BLEU, ROUGE, and other relevant metrics; identify opportunities for improvement. Deploy trained and fine-tuned models into production environments, integrating with real-time systems and pipelines. Identify, monitor, and mitigate issues related to bias, hallucinations, and knowledge cutoffs in LLMs. Work closely with cross-functional teams (data scientists, engineers, product managers) to design scalable and efficient NLP-driven solutions. Must-Have Skills: 7+ years of overall experience in software/AI development with at least 2+ years in transformer-based NLP models. 4+ years of hands-on expertise with transformer architectures (GPT, BERT, T5, RoBERTa, etc.). Strong understanding of attention mechanisms, self-attention layers, tokenization, embeddings, and context windows. Proven experience in fine-tuning pre-trained models for NLP tasks (summarization, classification, text generation, translation, Q&A). Expertise in prompt engineering, including zero-shot, few-shot, many-shot learning, and prompt template creation. Experience with instruction-based prompting and Chain-of-Thought prompting for reasoning tasks. Proficiency in Python and NLP libraries/frameworks such as Hugging Face Transformers, SpaCy, NLTK, PyTorch, TensorFlow. Strong knowledge of model evaluation metrics (BLEU, ROUGE, perplexity, etc.). Experience in deploying models into production environments. Awareness of bias, hallucinations, and limitations in LLM outputs. Good to Have: Experience with LLM observability tools and monitoring pipelines. Exposure to cloud platforms (AWS, GCP, Azure) for scalable model deployment. Knowledge of MLOps practices for model lifecycle management. Location-Remote,Delhi NCR,Bengaluru,Chennai,Pune,Kolkata,Ahmedabad, Mumbai, Hyderabad

JD: 7+ yrs exp with 2+ yrs in NLP using transformer models (GPT, BERT, T5, RoBERTa). Skilled in text generation, summarization, Q&A, and classification. Strong expertise in model fine-tuning, deployment, and optimization.

You will be working at SAP, a company that is dedicated to helping the world run better through collaboration and a shared passion for creating a workplace that values differences, flexibility, and purpose-driven work. As part of the team, you will have the opportunity to contribute to building a better future while enjoying a highly collaborative and caring team environment that prioritizes learning and development, recognizes individual contributions, and offers various benefit options. In this role focused on the Concur Solution Area, you will be developing reusable AI capabilities using Python-based machine learning models such as BERT or ROBERTA for products like Joule and GenAI Hub. Your responsibilities will include streamlining process operations, providing production-level support and improvements, and working on AI, Robotics, and Automation Services. You will collaborate with stakeholder teams, develop AI-based solutions, and use Robotics to transform intelligence into action. To excel in this position, you are expected to have 1-3 years of experience as a Full stack developer with proficiency in Java and Python, develop Python-based AI models, understand Concur as a product capability, and be able to translate customer pain points into AI problems to be solved. You will also be involved in evaluating the impact of AI deliverables, designing solutions, and developing applications. Additionally, you will have the opportunity to enhance your knowledge of LLM, Gen AI, machine learning, Python, JavaScript, HANA ML, and more. Furthermore, you will have the chance to gain real-time experience in a SaaS business model, collaborate with internal stakeholders, deliver business presentations to executive sponsors and leadership teams, and develop a deeper understanding of complex Python code and BTP capabilities. SAP values inclusion, health, well-being, and flexible working models to ensure that every individual, regardless of background, can thrive and contribute their unique talents to the company. As an equal opportunity workplace, SAP is committed to creating a diverse and equitable environment where all employees can reach their full potential. If you are interested in applying for a role at SAP and require accommodation or assistance, please reach out to the Recruiting Operations Team at Careers@sap.com. SAP is dedicated to unleashing all talent and creating a better world for everyone.,

As an AIML - LLM Gen AI Engineer, your primary responsibility will be to design, develop, and implement solutions using transformer-based models for various Natural Language Processing (NLP) tasks. This includes tasks such as text generation, summarization, question answering, classification, and translation. You will be working extensively with transformer models like GPT, BERT, T5, RoBERTa, and similar architectures. Your deep understanding of model architectures, attention mechanisms, and self-attention layers will be crucial in effectively utilizing Large Language Models (LLMs) to generate human-like text. You will lead efforts in fine-tuning pre-trained LLMs and other transformer models on domain-specific datasets to optimize performance for specialized NLP tasks. In your role, you will apply your knowledge of attention mechanisms, context windows, tokenization, and embedding layers in model development and optimization. It will be essential to address and mitigate issues related to biases, hallucinations, and knowledge cutoffs that can impact LLM performance and output quality. You will craft clear, concise, and contextually relevant prompts to guide LLMs towards generating desired outputs, including the use of instruction-based prompting. Additionally, you will implement and experiment with zero-shot, few-shot, and many-shot learning techniques to maximize model performance without extensive retraining. Your iterative approach to prompt engineering strategies will involve refining outputs, rigorously testing model performance, and ensuring consistent and high-quality results. You will also be responsible for creating prompt templates for repetitive tasks that are adaptable to different contexts and inputs. Expertise in chain-of-thought (CoT) prompting will enable you to guide LLMs through complex reasoning tasks by encouraging step-by-step breakdowns. Your contribution will span the entire lifecycle of machine learning models in an NLP context, including training, fine-tuning, and deployment. **Required Skills & Qualifications:** - A minimum of 8 years of experience working with transformer-based models and NLP tasks, focusing on text generation, summarization, question answering, classification, and similar applications. - Proficiency in transformer models such as GPT, BERT, T5, RoBERTa, and foundational models. - Strong familiarity with model architectures, attention mechanisms, and self-attention layers for generating human-like text. - Proven experience in fine-tuning pre-trained models on domain-specific datasets for various NLP tasks. - Knowledge of attention mechanisms, context windows, tokenization, and embedding layers. - Awareness of biases, hallucinations, and knowledge cutoffs affecting LLM performance. - Ability to craft clear, concise, and contextually relevant prompts for LLMs. - Experience in zero-shot, few-shot, and many-shot learning techniques. - Proficiency in Python and NLP libraries like Hugging Face Transformers, SpaCy, NLTK. - Solid experience in training, fine-tuning, and deploying ML models in an NLP context. - Strong problem-solving skills and analytical mindset. - Excellent communication and collaboration abilities for remote or hybrid work environments. - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or related quantitative field. This role requires a combination of technical expertise in AI/ML, NLP, and transformer models, along with strong problem-solving and communication skills to achieve effective results in the field.,

Role & responsibilities Design, prototype, and deploy AI-driven applications leveraging LLMs (GPT-4, Perplexity, Claude, Gemini, etc.) and open-source transformer models. Lead or co-lead end-to-end AI/GenAI solutions : from data ingestion, entity extraction, and semantic search to user-facing interfaces. Implement RAG (Retrieval-Augmented Generation) architectures, knowledge grounding pipelines, and prompt orchestration logic. Fine-tune transformer models (BERT, RoBERTa, T5, LLaMA) on custom datasets for use cases like: Document understanding Conversational AI Question answering Summarization & Topic Modeling Integrate LLM workflows into scalable backend architectures with APIs and frontends. Work closely with business teams and pharma SMEs to translate requirements into GenAI solutions . Mentor junior engineers and contribute to AI capability development across the organization. Tech Stack & Skills Required Programming & Libraries : Python, FastAPI, LangChain, Pandas, PyTorch/TensorFlow, Transformers (HuggingFace), OpenAI SDK. Data Extraction & Processing : PDFMiner, PyMuPDF, Tabula, PyPDF2, Tesseract OCR, python-pptx. Gen AI / LLMs : OpenAI (GPT), Gemini, Perplexity, Cohere, DeepSeek, Mistral, LLaMA, BERT, RoBERTa, T5, Falcon. Use Cases : NER, Summarization, QA, Document Parsing, Clustering, Topic Modeling, QA over docs. Embedding & Vector Databases : Pinecone, FAISS, ChromaDB. RAG & Retrieval Pipelines : LangChain, Haystack, custom retrievers. Frontend/Backend Integration : React (preferred), FastAPI/Flask, REST/GraphQL APIs. Versioning & Deployment : Git, Docker, CI/CD (basic), cloud knowledge is a plus (AWS/GCP/Azure). Preferred candidate profile Degree in Computer Science, Engineering, Data Science, or related field (BE/BTech/MTech/MCA). 35 years of hands-on experience in AI/ML/NLP/LLM solution development. Strong understanding of GenAI, Prompt Engineering, LLM internals , and multi-layered data architectures. Exposure to pharma/healthcare domain is a significant plus. Excellent problem-solving skills, self-learner, and ability to work in cross-functonal teams. Experience developing new applications within an agile environment preferred. Ability to work independently and as part of a team. Exposure to MLOPs will be an added advantage.

You will be part of a dynamic team at SAP, focused on developing AI capabilities for Concur Solution Area products like Joule and GenAI Hub. Your role will involve creating reusable AI solutions using Python-based machine learning and deep learning models such as BERT or ROBERTA. A critical aspect of your responsibilities will be to streamline process operations through AI capabilities and provide ongoing support and enhancements to LoBs operational methodologies. Your tasks will include prioritization, execution, and feedback collection to ensure efficient delivery of AI solutions. As a Full Stack Developer with 1-3 years of experience, proficiency in Java and Python is essential for this role. You will work on developing Python-based AI models, utilizing Joule and BTP products, and understanding Concur as a product capability. Your responsibilities will also include translating customer pain points into AI problems, evaluating AI deliverables, designing solutions, and developing applications to address those problems. Collaboration will be a key aspect of your role, as you will closely interact with stakeholder teams, engage in AI-based developments, and leverage Robotics to translate intelligence into actionable insights. Furthermore, you will be expected to create pitch documents, collaborate with product owners, and demonstrate a deep understanding of AI technologies and their applications. In this role, you will have the opportunity to enhance your technical skills by gaining real-time experience in the SaaS business model, cloud servicing, and support functions. You will also collaborate with internal stakeholders, deliver business presentations to executive sponsors, and leadership teams. Proficiency in LLM, Gen AI, machine learning, Python, JavaScript, and HANA ML will be advantageous in this position. At SAP, we value inclusion, health, well-being, and flexible working models to ensure that every individual, regardless of background, can thrive. As an equal opportunity workplace, we are committed to fostering a diverse and inclusive environment where all employees can reach their full potential. We believe in leveraging the unique capabilities of each individual to create a better and more equitable world. If you are excited about developing complex Python code, learning BTP capabilities, and contributing to innovative AI solutions, this role at SAP offers a rewarding opportunity to showcase your skills and grow within a purpose-driven and future-focused organization.,

You will be part of a dynamic team at SAP, where the primary focus is on collaboration and a shared passion to help the world run better. Embracing diversity and fostering a workplace that values flexibility, we are committed to building a strong foundation for the future. As a member of our team, you will have the opportunity to contribute to the development of reusable AI capabilities for Concur Solution Area products, including Joule and GenAI Hub. Your role will involve working with Python-based Machine learning and deep learning models such as BERT or ROBERTA, necessitating a deep understanding of model operations. Your responsibilities will include delivering AI capabilities to streamline process operations and providing ongoing production-level support and enhancements to Line of Business (LoBs) operational methodologies. You will engage in a cycle of prioritization, execution, and feedback collection to ensure efficient outcomes. Additionally, you will collaborate with stakeholder teams, leverage AI-based developments, and utilize Robotics to translate intelligence into actionable insights. To excel in this role, you should possess 1-3 years of experience as a Full stack developer proficient in Java and Python, with a focus on developing Python-based AI models. Familiarity with Joule and BTP products, as well as a solid understanding of Concur's product capabilities, will be essential. You will be tasked with translating customer pain points into AI problems, evaluating the impact of AI deliverables, designing solutions, and developing applications to address identified challenges. Furthermore, you will have the opportunity to enhance your technical skills by gaining real-time experience in the SaaS business model, servicing and supporting cloud-based solutions, collaborating with internal stakeholders, and delivering business presentations to executive sponsors and leadership teams. Demonstrating a high level of responsibility, along with strong presentation, analytical, and troubleshooting skills, will be vital to your success in this role. If you are a highly talented and flexible individual with a strong foundation in LLM, Gen AI, and Machine learning, proficient in Python and JavaScript, and have knowledge of HANA ML, we encourage you to apply. At SAP, we are dedicated to fostering an inclusive culture that prioritizes well-being and offers flexible working arrangements to ensure that every individual can thrive and contribute to their fullest potential. We believe in the strength of diversity and invest in our employees to empower them to achieve their goals and create a more equitable world. As an equal opportunity workplace and an affirmative action employer, SAP is committed to providing accessibility accommodations and supporting applicants with disabilities. If you require assistance or accommodation during the application process, please reach out to the Recruiting Operations Team at Careers@sap.com. Join us at SAP, where you can unleash your full potential and be part of a team that strives to provide innovative solutions to global challenges.,

Role & responsibilities Overall 7 years with at least 5+ years of experience working with transformer-based models and NLP tasks, with a focus on text generation, summarization, question answering, classification, and similar tasks. Expertise in transformer models like GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), T5 (Text-to-Text Transfer Transformer), RoBERTa, and similar models. Familiarity with model architectures, attention mechanisms, and self-attention layers that enable LLMs to generate human-like text. Experience in fine-tuning pre-trained models on domain-specific datasets for tasks such as text generation, summarization, question answering, classification, and translation. Familiarity with concepts like attention mechanisms, context windows, tokenization, and embedding layers. Awareness of biases, hallucinations, and knowledge cutoffs that can affect LLM performance and output quality. Expertise in crafting clear, concise, and contextually relevant prompts to guide LLMs towards generating desired outputs. Experience in instruction-based prompting Use of zero-shot, few-shot, and many-shot learning techniques for maximizing model performance without retraining. Experience in iterating on prompts to refine outputs, test model performance, and ensure consistent results. Crafting prompt templates for repetitive tasks, ensuring prompts are adaptable to different contexts and inputs. Expertise in chain-of-thought (CoT) prompting to guide LLMs through complex reasoning tasks by encouraging step-by-step breakdowns. Proficiency in Python and experience with NLP libraries (e.g., Hugging Face, SpaCy, NLTK). Experience with transformer-based models (e.g., GPT, BERT, T5) for text generation tasks. Experience in training, fine-tuning, and deploying machine learning models in an NLP context. Understanding of model evaluation metrics (e.g., BLEU, ROUGE)