58 Finetuning Jobs

As a Generative AI Developer, your role will involve designing, building, and deploying solutions powered by Large Language Models (LLMs), multimodal AI, and prompt engineering. You will collaborate closely with product and engineering teams to integrate GenAI capabilities into real-world applications such as chatbots, copilots, content generation, and intelligent automation. Key Responsibilities: - AI/ML Development: - Fine-tune and optimize LLMs (GPT, LLaMA, Claude, etc.) for specific business use cases. - Build and deploy GenAI-powered applications like chatbots, copilots, summarization, Q&A systems, and generative content. - Experiment with multimodal AI involving text, image, audio, and video. - Prompt Engineering & Orchestration: - Design effective prompts and workflows to maximize model performance. - Work with frameworks like LangChain, LlamaIndex, and Haystack for retrieval-augmented generation (RAG). - Backend & Integration: - Develop APIs and services to integrate GenAI models into existing platforms. - Connect AI with structured and unstructured data sources. - Cloud & Deployment: - Deploy models on AWS, Azure, or GCP (SageMaker, Vertex AI, Azure OpenAI). - Implement scalable and cost-efficient inference pipelines. - MLOps & Performance: - Monitor AI models in production including latency, accuracy, and hallucination rates. - Implement continuous training, evaluation, and improvement workflows. Required Skills & Qualifications: - Strong programming skills in Python (Node.js/Java is a plus). - Hands-on experience with GenAI/LLMs (OpenAI, Anthropic, Hugging Face, Cohere, Stability AI). - Familiarity with LangChain, LlamaIndex, and RAG pipelines. - Knowledge of NLP, embeddings, vector databases (Pinecone, Weaviate, Milvus, FAISS). - Experience with cloud platforms (AWS, Azure, GCP) and deployment of ML models. - Understanding of prompt design, fine-tuning, and model evaluation. - Solid grasp of APIs, microservices, and REST/GraphQL integrations. Preferred: - Experience with multimodal models (text-to-image, speech-to-text, etc.). - Exposure to containerization and orchestration (Docker, Kubernetes). - Knowledge of data privacy, security, and compliance in AI systems. - Prior work in AI-driven SaaS products. In this role, you will have the opportunity to contribute to cutting-edge projects in the field of Generative AI. If you have experience deploying applications or AI models on cloud platforms, familiarity with AI/ML frameworks, and working with CDN platforms for scaling applications, we encourage you to apply for this full-time position.,

As an Applied AI/GenAI ML Director at JPMorgan Chase, you will play a pivotal role in collaborating with agile teams to enhance, create, and deliver cutting-edge technology products in a secure and scalable manner. Your expertise will be instrumental in challenging the norm, driving innovation, and leading the strategic development of new products and technology portfolios. By staying updated on industry trends and best practices, you will establish common capabilities and frameworks for AI excellence across the organization. - Establish and promote a library of common ML assets, including reusable models, features stores, data pipelines, and standardized templates. - Lead the creation of shared tools and platforms to streamline the end-to-end ML lifecycle. - Develop curative solutions using GenAI workflows and advanced proficiency in Large Language Models (LLMs). - Gain experience in creating a Generative AI evaluation and feedback loop for GenAI/ML pipelines. - Provide guidance on the strategy and development of products, applications, and technologies. - Act as a lead advisor on the technical feasibility and business need for AIML use cases. - Collaborate with AI ML stakeholders across the organization. - Translate complex technical issues for leadership to drive innovation and informed decision-making. - Influence business, product, and technology teams while managing senior stakeholder relationships. - Champion a culture of diversity, opportunity, inclusion, and respect within the firm. Qualifications and Skills Required: - Formal training or certification in Machine Learning concepts with over 10 years of applied experience in programming languages like Python, Java, or C/C++. - MS and/or PhD in Computer Science, Machine Learning, or related field. - Strong understanding of ML techniques, especially in NLP and LLMs. - Experience in AI implementation in software development and legacy code transformation. - Familiarity with agentic workflows and frameworks like LangChain and LangGraph. - Proficiency in deep learning frameworks such as PyTorch or TensorFlow. - Knowledge in advanced ML areas like GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, and RAG. - Ability to work on system design from ideation to completion with limited supervision. - Excellent communication and teamwork skills, with a passion for detail and follow-through. - Demonstrated leadership in collaborating effectively with engineers, product managers, and ML practitioners. - Practical experience in cloud-native environments like AWS. Preferred Qualifications: - Experience with distributed training frameworks like Ray and MLFlow. - Understanding of advanced methodologies such as Embedding-based Search/Ranking, Recommender systems, and Graph techniques. - Proficiency in Reinforcement Learning or Meta Learning. - Deep knowledge of LLM techniques, including Agents, Planning, and Reasoning. - Experience in building and deploying ML models on AWS platforms using tools like Sagemaker and EKS.,

As a Software Engineer III at JPMorganChase within the AI/ML & Data Platform team, you will play a critical role in designing and delivering market-leading technology products. Here's a summary of what you can expect in this role: **Role Overview:** You will be a seasoned member of an agile team, responsible for executing software solutions, design, development, and technical troubleshooting. Your role involves creating secure and high-quality production code, maintaining algorithms, and producing architecture and design artifacts for complex applications. Additionally, you will gather, analyze, synthesize, and develop visualizations and reporting from large data sets to drive continuous improvement of software applications and systems. **Key Responsibilities:** - Execute software solutions with innovative approaches - Create secure and high-quality production code - Produce architecture and design artifacts for complex applications - Gather, analyze, synthesize, and develop visualizations and reporting from large data sets - Identify hidden problems and patterns in data for system improvements - Contribute to software engineering communities of practice and events - Foster a team culture of diversity, opportunity, inclusion, and respect **Qualifications Required:** - Formal training or certification in AI/ML concepts with 3+ years of applied experience - Hands-on experience in programming languages, particularly Python - Ability to apply data science and machine learning techniques to address business challenges - Strong background in Natural Language Processing (NLP) and Large Language Models (LLMs) - Expertise in deep learning frameworks such as PyTorch or TensorFlow - Strong communication skills and demonstrated leadership in collaborating effectively with team members Please note that the job description does not include any additional details about the company.,

We are looking for an experienced and innovative Generative AI Tech Lead to join our Grid team. In this role, you will lead the development of advanced AI systems using Large Language Models (LLMs), transformers, and other GenAI architectures. You will collaborate with cross-functional teams to build scalable solutions in NLP, multimodal learning, and content generation. Project Description The engineer is supposed to participate in pre-sales, discovery phases of projects and lead projects. They are also expected to be capable of starting the project from scratch and lead the team. Details on Tech Stack Python Prompt engineering Best practices for prompt engineering How LLM can be used in applications for a variety of tasks NLP Understanding of typical NLP problems: classification, NER, summarization, question answering, sentiment analysis, etc. Theoretical intuitive understanding of how Transformers work (tokenization, attention, etc). Word and sentence embeddings Vector search Vector databases, performance tuning Document chunking techniques LLM applications development LangChain, LlamaIndex Chain of Thoughts, DSP, and other techniques Agents and tools Google cloud (GCP) Nice to Have Requirements Preferable, the engineers are expected to have IT services/consulting experience. Proficient in developing LLM-powered systems using advanced prompt engineering techniques, RAG and agentic design patterns. Experienced with frameworks like LangChain, LlamaIndex, and DSPy. Familiar with evaluation approaches and metrics for different types of LLM-based systems. Experienced with keyword and vector search methods, including understanding of their underlying algorithms. Familiar with popular vector search engines. Competent in various document understanding models and techniques to parse complex documents and implement effective chunking strategies for RAG systems. Familiar with LLM and embedding models fine-tuning techniques. Competent in using joint vision-language and generative models to solve various problems related to image generation, visual question answering, and multi-modal search. Familiar with diffusion models and associated techniques like LoRA, Dreambooth, and ControlNet. Understanding of the challenges and risks associated with the development of Generative AI systems and how to mitigate them. Familiar with various architecture design patterns for different types of LLM-based applications such as chatbots, text2sql, document understanding, etc. Familiar with various approaches to scalability and cost reduction in Generative AI systems. Ability to stay updated with the latest advancements in Generative AI and integrate emerging technologies to drive innovation and improve the performance of AI systems. Familiar with Responsible AI principles and Human-AI interaction design best practices. Perks & Benefits: Competitive salary & performance-based bonuses Remote work flexibility / Hybrid options Continuous learning budget & GenAI certifications Opportunity to work on cutting-edge AI projects Dynamic and collaborative team environment

We are looking for an experienced and innovative Generative AI Tech Lead to join our Grid team. In this role, you will lead the development of advanced AI systems using Large Language Models (LLMs), transformers, and other GenAI architectures. You will collaborate with cross-functional teams to build scalable solutions in NLP, multimodal learning, and content generation. Project Description The engineer is supposed to participate in pre-sales, discovery phases of projects and lead projects. They are also expected to be capable of starting the project from scratch and lead the team. Details on Tech Stack Python Prompt engineering Best practices for prompt engineering How LLM can be used in applications for a variety of tasks NLP Understanding of typical NLP problems: classification, NER, summarization, question answering, sentiment analysis, etc. Theoretical intuitive understanding of how Transformers work (tokenization, attention, etc). Word and sentence embeddings Vector search Vector databases, performance tuning Document chunking techniques LLM applications development LangChain, LlamaIndex Chain of Thoughts, DSP, and other techniques Agents and tools Google cloud (GCP) Nice to Have Requirements Preferable, the engineers are expected to have IT services/consulting experience. Proficient in developing LLM-powered systems using advanced prompt engineering techniques, RAG and agentic design patterns. Experienced with frameworks like LangChain, LlamaIndex, and DSPy. Familiar with evaluation approaches and metrics for different types of LLM-based systems. Experienced with keyword and vector search methods, including understanding of their underlying algorithms. Familiar with popular vector search engines. Competent in various document understanding models and techniques to parse complex documents and implement effective chunking strategies for RAG systems. Familiar with LLM and embedding models fine-tuning techniques. Competent in using joint vision-language and generative models to solve various problems related to image generation, visual question answering, and multi-modal search. Familiar with diffusion models and associated techniques like LoRA, Dreambooth, and ControlNet. Understanding of the challenges and risks associated with the development of Generative AI systems and how to mitigate them. Familiar with various architecture design patterns for different types of LLM-based applications such as chatbots, text2sql, document understanding, etc. Familiar with various approaches to scalability and cost reduction in Generative AI systems. Ability to stay updated with the latest advancements in Generative AI and integrate emerging technologies to drive innovation and improve the performance of AI systems. Familiar with Responsible AI principles and Human-AI interaction design best practices. Perks & Benefits: Competitive salary & performance-based bonuses Remote work flexibility / Hybrid options Continuous learning budget & GenAI certifications Opportunity to work on cutting-edge AI projects Dynamic and collaborative team environment

We are looking for an experienced and innovative Generative AI Tech Lead to join our Grid team. In this role, you will lead the development of advanced AI systems using Large Language Models (LLMs), transformers, and other GenAI architectures. You will collaborate with cross-functional teams to build scalable solutions in NLP, multimodal learning, and content generation. Project Description The engineer is supposed to participate in pre-sales, discovery phases of projects and lead projects. They are also expected to be capable of starting the project from scratch and lead the team. Details on Tech Stack Python Prompt engineering Best practices for prompt engineering How LLM can be used in applications for a variety of tasks NLP Understanding of typical NLP problems: classification, NER, summarization, question answering, sentiment analysis, etc. Theoretical intuitive understanding of how Transformers work (tokenization, attention, etc). Word and sentence embeddings Vector search Vector databases, performance tuning Document chunking techniques LLM applications development LangChain, LlamaIndex Chain of Thoughts, DSP, and other techniques Agents and tools Google cloud (GCP) Nice to Have Requirements Preferable, the engineers are expected to have IT services/consulting experience. Proficient in developing LLM-powered systems using advanced prompt engineering techniques, RAG and agentic design patterns. Experienced with frameworks like LangChain, LlamaIndex, and DSPy. Familiar with evaluation approaches and metrics for different types of LLM-based systems. Experienced with keyword and vector search methods, including understanding of their underlying algorithms. Familiar with popular vector search engines. Competent in various document understanding models and techniques to parse complex documents and implement effective chunking strategies for RAG systems. Familiar with LLM and embedding models fine-tuning techniques. Competent in using joint vision-language and generative models to solve various problems related to image generation, visual question answering, and multi-modal search. Familiar with diffusion models and associated techniques like LoRA, Dreambooth, and ControlNet. Understanding of the challenges and risks associated with the development of Generative AI systems and how to mitigate them. Familiar with various architecture design patterns for different types of LLM-based applications such as chatbots, text2sql, document understanding, etc. Familiar with various approaches to scalability and cost reduction in Generative AI systems. Ability to stay updated with the latest advancements in Generative AI and integrate emerging technologies to drive innovation and improve the performance of AI systems. Familiar with Responsible AI principles and Human-AI interaction design best practices. Perks & Benefits: Competitive salary & performance-based bonuses Remote work flexibility / Hybrid options Continuous learning budget & GenAI certifications Opportunity to work on cutting-edge AI projects Dynamic and collaborative team environment

As a Software Engineer III at JPMorganChase within the AI/ML & Data Platform team, you will play a crucial role in designing and delivering trusted technology products in a secure, stable, and scalable manner. Your responsibilities will involve executing software solutions, troubleshooting technical issues, and creating secure production code that aligns with design constraints. Additionally, you will be involved in analyzing and synthesizing data sets to drive continuous improvement in software applications. **Key Responsibilities:** - Execute software solutions, design, development, and technical troubleshooting with innovative approaches - Create secure and high-quality production code, maintaining algorithms for synchronous operation - Produce architecture and design artifacts for complex applications, ensuring design constraints are met - Analyze and synthesize data sets to develop visualizations and reporting for software applications enhancement - Identify hidden problems and patterns in data to drive improvements in coding hygiene and system architecture - Contribute to software engineering communities of practice and events exploring new technologies - Foster a team culture of diversity, opportunity, inclusion, and respect **Qualifications Required:** - Formal training or certification in AI/ML concepts with a minimum of 3 years of applied experience - Hands-on experience in programming languages, especially Python - Ability to apply data science and machine learning techniques to solve business challenges - Strong background in Natural Language Processing (NLP) and Large Language Models (LLMs) - Expertise in deep learning frameworks like PyTorch or TensorFlow, and advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, and RAG (Similarity Search) - Capable of completing tasks independently with minimal supervision, demonstrating attention to detail and follow-through - Excellent communication skills, team player, and proven leadership in collaborating with engineers, product managers, and ML practitioners *Note: Preferred qualifications, capabilities, and skills are not provided in the job description.*,

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Employee Platforms team, you serve as a seasoned member of an Incubation and Research team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. This role requires a unique ability to apply state-of-the-art technical skills, work with ambiguity, and deliver projects to completion. You will be instrumental in developing innovative solutions, finding market fit, and delivering products that resonate with our users. As a hands-on engineer, you will bring in cutting-edge technologies, including Generative AI and ML. Job responsibilities Execute software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems. Embrace ambiguity and lead the development of innovative solutions without a predefined roadmap. Implement the Build-Measure-Lean loop by rapidly prototyping, testing, and iterating on engineering solutions. Analyze user feedback and technical challenges to refine product offerings and ensure alignment with user needs. Create secure and high-quality production code and maintain algorithms that run synchronously with appropriate systems. Produce architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development. Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems. Proactively identify hidden problems and patterns in data and use these insights to drive improvements to coding hygiene and system architecture. Contribute to software engineering communities of practice and events that explore new and emerging technologies. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Hands-on practical experience in Python, SQL, advanced GenAI technologies like multimodality (Voice & Images), Agentic AI and ML technologies Highly proficient in coding in one or more languages such as Python, SQL, Java and R programming languages Experience with one or more platform tech stacks such as AWS, Docker, Kubernetes, Data bricks and CI/CD pipelines. Solid understanding of using ML techniques specially in Natural Language Processing (NLP), Knowledge Graph and Large Language Models (LLMs) Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search) Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages Overall knowledge of the Software Development Life Cycle Solid understanding of agile methodologies such as CI/CD, application resiliency, and security Preferred qualifications, capabilities, and skills Proficiency in optimizing and tuning AI models to ensure efficient, scalable solutions, with experience in building and deploying ML models on cloud platforms such as AWS and using tools like Sagemaker and EKS. Knowledge of data engineering practices to support AI model training and deployment, along with a strong understanding of machine learning algorithms and techniques-including supervised, unsupervised, and reinforcement learning-and hands-on experience with libraries such as TensorFlow, PyTorch, Scikit-learn, and Keras. Skills in collaborating with cross-functional teams to integrate generative AI solutions into broader business processes and applications, leveraging advanced LLM techniques such as Agents, Planning, and Reasoning. In-depth understanding of embedding-based search/ranking, recommender systems, graph techniques, and other advanced methodologies to enhance AI solution capabilities.

The role requires you to lead the collaboration with ML Engineers and DevOps Engineers to formulate AI designs that can be built, tested, and deployed through the Route to Live and into Production using continuous integration/deployment. In this role, you will be responsible for Model Development & Deployment, including model fine-tuning using open-source libraries like DeepSpeed, Hugging Face Transformers, JAX, PyTorch, and TensorFlow to enhance model performance. You will also work on deploying and managing Large Language Models (LLMs) on cloud platforms, training and refining LLMs, and scaling LLMs up and down while ensuring blue/green deployments and roll back bad releases. Your tasks will also involve Data Management & Pipeline Operations, such as curating and preparing training data, monitoring data quality, transforming and aggregating data, building vector databases, and making data visible and shareable across teams. Monitoring & Evaluation will be a crucial part of your role, where you will track LLM performance, identify errors, optimize models, and create model and data monitoring pipelines with alerts for model drift and malicious user behavior. Infrastructure & DevOps tasks will include continuous integration and delivery (CI/CD), managing infrastructure for distributed model training using tools like SageMaker, Ray, Kubernetes, and deploying ML models using containerization technologies like Docker. Required Technical Skills for the role include proficiency in programming languages like Python, frameworks like PyTorch and TensorFlow, expertise in cloud platforms like AWS, Azure, or GCP, experience with containerization technologies, and familiarity with LLM-Specific Technologies such as vector databases, prompt engineering, and fine-tuning techniques. The position is located at DGS India in Pune, Baner, and falls under the Brand Merkle. It is a Full-Time role with a Permanent contract.,

The ideal candidate should have a minimum of 3 years of experience in GenAI for client-side deployments. You must possess significant expertise in Prompt engineering, GenAI frameworks, LLM understanding, Finetuning, NLP, and text analysis. Strong communication skills and practical technical knowledge are essential for this role. This is a contingency supply opportunity, and we require the selected candidate to be onboarded before 25th September. Please note that individuals with a notice period of 2-3 months will not be considered for this position. If you meet the requirements mentioned above, kindly submit your profile to suhail.s@kudzuinfotech.com. Thank you for considering this opportunity. Best regards, Banibrata Sarkar,

As a Python Developer at our company, you will be responsible for developing, deploying, and optimizing Python-based applications for Visual AI tasks such as image classification, object detection, and image generation. Your expertise in Django and Flask, along with your proficiency in Django Rest Framework (DRF), will be crucial in this role. Additionally, you will work with machine learning concepts and tools like PyTorch, TensorFlow, and transformer architectures. Your key responsibilities will include designing and implementing machine learning models using frameworks like TensorFlow, PyTorch, and OpenCV. You will collaborate with cross-functional teams to integrate Visual AI capabilities into applications and conduct exploratory data analysis for AI/ML tasks. Optimizing models for performance, scalability, and accuracy will also be a part of your role. To excel in this position, you must demonstrate a strong proficiency in Python with experience in data science and machine learning. Hands-on experience with frameworks like TensorFlow, PyTorch, OpenCV, and scikit-learn is essential. You should have a good understanding of computer vision techniques, such as object detection, image segmentation, and image generation, and be familiar with Generative AI techniques like GANs, VAEs, diffusion models, and LLaMA. Moreover, experience with data manipulation tools like Pandas, NumPy, and visualization tools like Matplotlib and Seaborn is required. Knowledge of cloud platforms (AWS, GCP, or Azure) and tools like Docker for model deployment will be beneficial. Familiarity with model optimization techniques like transfer learning, fine-tuning, and LoRA is also desirable. In this role, you will have the opportunity to work with cutting-edge technologies and contribute to the development of innovative solutions in the field of Visual AI. Your ability to collaborate with teams, optimize models, and present findings to stakeholders will be key to your success in this dynamic position.,

We are searching for a highly skilled and experienced Python Developer to become a valuable part of our dynamic team. The ideal candidate should possess a robust background in developing web applications utilizing Django and Flask, along with expertise in deploying and managing applications on AWS. It is essential to have proficiency in Django Rest Framework (DRF), a solid grasp of machine learning concepts, and practical experience with tools such as PyTorch, TensorFlow, and transformer architectures. Your primary responsibilities will include developing, deploying, and optimizing Python-based applications for Visual AI tasks like image classification, object detection, and image generation. You will also be tasked with designing, implementing, and fine-tuning machine learning models using frameworks like TensorFlow, PyTorch, and OpenCV. Additionally, you will work with diffusion models, GANs, VAEs, and other generative models for tasks like text-to-image and image-to-image generation. Collaboration with cross-functional teams to integrate Visual AI capabilities into applications, conducting exploratory data analysis, and implementing feature engineering for AI/ML tasks will also be part of your role. Furthermore, you will optimize models for performance, scalability, and accuracy, as well as document and present findings, tools, and best practices to technical and non-technical stakeholders. The ideal candidate should demonstrate strong proficiency in Python with experience in data science and machine learning. Hands-on experience with frameworks like TensorFlow, PyTorch, OpenCV, and scikit-learn is required. A good understanding of computer vision techniques such as object detection, image segmentation, and image generation is essential. Familiarity with Generative AI techniques including GANs, VAEs, diffusion models, and LLaMA is advantageous. Experience with data manipulation tools like Pandas, NumPy, and visualization tools like Matplotlib and Seaborn is beneficial. Knowledge of cloud platforms (AWS, GCP, or Azure) and tools like Docker for model deployment, as well as familiarity with model optimization techniques including transfer learning, fine-tuning, and LoRA, will be a plus. This position offers an exciting opportunity to work with cutting-edge technologies in a collaborative environment. If you are passionate about Python development, machine learning, and Visual AI tasks, we encourage you to apply for this role and be a part of our innovative team.,

As a General AI Engineer at Nallas, a software services and product company, you will be responsible for developing, implementing, and maintaining advanced AI systems to drive innovation and solve complex problems. Collaborating with cross-functional teams, your role will involve designing and deploying AI solutions that enhance products and services, pushing the boundaries of what AI can achieve. Your role will require expertise in Python, Data Structures, and API Calls, providing a solid foundation for working with generative AI models and frameworks. Strong communication skills, both in documentation and presentations, will be essential for effectively conveying complex technical concepts to technical and non-technical audiences. In addition, you will need to excel in teamwork and solo work, collaborating on large projects while independently driving research and development efforts. Proficiency in data mining and text processing to extract valuable insights from various data sources for training and improving generative models will be crucial. Experience with building RAG Pipelines is highly desired, showcasing your ability to construct retrieval-augmented generation pipelines, a key technique in generative AI. Your expertise should also cover Machine Learning (ML), Natural Language Processing (NLP), Generative Adversarial Networks (GANs), Transformers & BERT flavors, Hugging Face Model Implementation, and Fine-Tuning, with a solid understanding of these core concepts for generative AI development. Hands-on experience with Vector Databases such as Chroma DB, PineCone, Milvus, FAISS, Arango DB for data storage and retrieval relevant to generative AI projects is expected. Collaboration skills are essential to work effectively with Business Analysts (BAs), Development Teams, and DevOps teams to bring generative AI solutions to fruition. Familiarity with deploying generative models on resource-constrained devices, like the Open AI Ada Embedding 002 model, can be advantageous. Proficiency with POC Tools like Streamlit and Gradio is required for rapid prototyping and showcasing generative AI concepts. Cloud experience, particularly with AWS Bedrock or similar platforms, is crucial for managing and deploying large-scale generative AI models. Basic knowledge of cloud services like EC2, ECS, ECR, S3, and Sagemaker is expected. Moreover, expertise in specific LLMs (such as OpenAI, Jurassic-1 Jumbo, LLAMA, mistral, Mixtral, Gemma, Gemini Pro) may be necessary based on the project focus. Your adeptness in these areas will contribute to the successful development and deployment of generative AI solutions at Nallas. If you are passionate about working on cutting-edge technologies and eager to advance your career in AI and ML, this role at Nallas presents an exciting opportunity for your personal and professional growth. For further consideration, you may share your resume with karthicc@nallas.com.,

We are seeking a skilled Machine Learning Engineer / Data Scientist with specialized knowledge in ML, NLP, and Large Language Models (LLMs). The perfect candidate should possess over 4 years of experience in ML/NLP and a minimum of 1 year of practical exposure to LLMs, particularly in fine-tuning, RAG, and AI-driven agents. Your primary responsibilities will include refining and optimizing LLMs such as GPT, LLaMA, and Mistral, as well as creating AI agents utilizing CrewAI, LangChain, and AutoGPT. Additionally, you will be tasked with constructing and improving RAG pipelines using FAISS, Pinecone, and Weaviate, working on various NLP models like text classification, summarization, and NER, and staying abreast of the latest advancements in LLM technology. To excel in this role, you should have at least 4 years of experience in ML/NLP and a minimum of 1 year in LLMs, be proficient in Python, PyTorch, and TensorFlow, possess expertise in LLM fine-tuning methods such as LoRA, PEFT, and QLoRA, and have practical experience with AI agents and RAG tools. Desirable qualifications for this position include familiarity with multi-modal AI applications like text-to-image and speech-to-text, knowledge of contrastive learning and RLHF, and a background in open-source contributions or AI publications. This opportunity has been presented by Aarushi Madhani on behalf of FiftyFive Technologies.,

You are seeking an AI Architect to lead the design, development, and deployment of advanced AI systems, with a strong emphasis on Large Language Models (LLMs), fine-tuning, and customer experience (CX) technologies. This role blends deep technical expertise with leadership, research, and infrastructure planning to deliver intelligent, scalable, and customer-centric AI solutions across cloud and on-premise environments. Key Responsibilities LLM Development & Fine-Tuning - Architect and implement scalable solutions for training and fine-tuning LLMs. - Apply prompt engineering, transfer learning, and optimization techniques to enhance model performance. - Integrate LLMs into customer-facing applications such as chatbots, VOICE AI agents, Expert AI agents, and recommendation engines. Customer Experience (CX) Technology Integration - Collaborate with CX and product teams to embed AI into customer journeys, improving personalization, automation, and engagement. - Design AI-driven solutions for omnichannel support, sentiment analysis, and real-time feedback loops. - Ensure AI systems align with customer satisfaction goals and ethical AI principles. Technical Leadership & Team Management - Lead and mentor a multidisciplinary team of AI engineers, data scientists, and MLOps professionals. - Drive agile development practices and foster a culture of innovation and accountability. Research & Innovation - Conduct and apply research in NLP, LLMs, and AI infrastructure to solve real-world customer experience problems. - Contribute to publications, patents, or open-source initiatives as appropriate. - Guide team and maintain detailed architecture diagrams, design documents, and technical specifications. Product Roadmap & Delivery - Define and execute the AI product roadmap in collaboration with engineering and business stakeholders. - Manage timelines, deliverables, and cross-functional dependencies. - Plan and manage GPU infrastructure for training and inference (e.g., A100, H100, L40S). Deployment & MLOps - Deploy AI models on Azure, AWS, and on-premise GPU clusters using containerized and scalable architectures. - Integrate with CI/CD pipelines and ensure robust monitoring, logging, and rollback mechanisms. Qualifications - Masters or Ph.D. in Computer Science, AI, Machine Learning, or related field. - 5+ years of experience in AI/ML, with 1.5 + years in a leadership or GEN AI architect role. - Proven experience with LLMs, Transformers, and fine-tuning frameworks (e.g., Hugging Face). - Strong understanding of customer experience platforms and how AI can enhance them. - Proficiency in Python, PyTorch, TensorFlow, and MLOps tools. - Experience with cloud platforms (Azure, AWS) and on-premise GPU infrastructure. Why Join Us - Shape the future of AI-powered customer experiences, visibility to global customer AI deployments. - Lead a high-impact team working on state-of-the-art technologies. - Competitive compensation, and continuous learning opportunities.,

You will be joining a well-funded Generative AI startup that is dedicated to creating outstanding, user-centric conversational experiences for a global audience. The primary goal is to enhance the accessibility, impact, and engagement of AI interactions by merging advanced Generative AI with intuitive, human-centered design principles. The leadership team consists of industry experts with extensive knowledge in AI, personalization, and NLP, gained from their tenure at leading global technology companies. Your main responsibilities will include: - Conducting research on various topics such as research papers, case studies, and industry benchmarks to extract valuable insights for enhancing AI performance and staying up-to-date with the latest advancements. - Defining, executing, and iterating on experiments across different AI models to enhance accuracy, engagement, and response quality. - Analyzing model performance through A/B tests, comparing key metrics, and refining the AI pipeline. - Developing feedback loops to measure user satisfaction and model effectiveness. - Collaborating closely with engineers, data scientists, and product teams to drive AI advancements and feature enhancements. - Utilizing analytics to identify trends, evaluate model success, and implement strategic improvements. - Testing new AI capabilities and ideas quickly to optimize performance and scalability. The ideal candidate for this role will possess: - 1-3 years of experience in AI-focused product roles, preferably from renowned institutions such as IIT, NIT, BITS, etc. - Proficiency in LLMs, fine-tuning, prompt engineering, and AI evaluation techniques. - Experience in designing experiments for testing and iterating on model performance. - Ability to interpret AI/ML metrics and convert them into actionable insights. - Strong problem-solving and analytical skills. - Hands-on experience with LLM APIs (OpenAI, Hugging Face, etc.), A/B testing, and user analytics tools. - Capability to thrive in a fast-paced, uncertain environment while maintaining clarity and driving execution. Additionally, it would be advantageous if you have experience in AI-powered conversations or a background in astrology or related fields. This is a full-time position that requires in-person work at the designated location.,

As a Senior Data Scientist in the Data & Analytics field, you will be responsible for a variety of tasks that involve advanced analytics, modeling, data strategy, engineering collaboration, business impact, AI strategy, thought leadership, mentorship, and technical expertise. You will design and implement advanced machine learning models, including deep learning, time-series forecasting, recommendation engines, and LLM-based solutions such as GPT, LLaMA, and Claude. Additionally, you will develop use cases around enterprise search, document summarization, conversational AI, and automated knowledge retrieval using large language models. Your role will also involve fine-tuning or prompt-engineering foundation models for domain-specific applications and evaluating and optimizing LLM performance, latency, cost-effectiveness, and hallucination mitigation strategies for production use. Collaboration with data and ML engineering teams will be crucial as you work closely to integrate LLM-powered applications into scalable, secure, and reliable pipelines. You will contribute to the development of retrieval-augmented generation architectures using vector databases and support the deployment of models using MLOps principles. Partnering with cross-functional stakeholders, you will identify opportunities for applying LLMs and generative AI to solve complex business challenges. Leading workshops or proofs-of-concept will be part of your responsibilities to demonstrate the value of LLM use cases across business units. You will also translate complex model outputs into clear insights and decision support tools for non-technical audiences. As an internal thought leader on AI and LLM innovation, you will keep the organization at the forefront of industry advancements. Mentoring and upskilling data science team members in advanced AI techniques and contributing to strategic roadmaps for generative AI and model governance within the enterprise will be key aspects of your role. To qualify for this position, you should have education in Data Science, Artificial Intelligence, Computer Science, or related quantitative discipline, along with 5+ years of hands-on experience in data science. Demonstrated success in deploying machine learning and NLP solutions at scale and proven experience with cloud AI platforms are required. Proficiency in Python and SQL, experience with prompt engineering, fine-tuning, and LLM orchestration tools, familiarity with data storage and retrieval systems, and strong understanding of model evaluation techniques for generative AI are also necessary. Additionally, you should possess leadership skills and be a strategic thinker with the ability to align AI initiatives to business goals. Excellent communication and storytelling skills are crucial for articulating the value of LLMs and advanced analytics. Strong collaboration skills and the ability to influence stakeholders across various teams are also essential. Preferred qualifications for this role include experience with IoT, edge analytics, or smart building systems, familiarity with LLMOps, LangChain, Semantic Kernel, or similar orchestration frameworks, and knowledge of data privacy and governance considerations specific to LLM usage in enterprise environments.,

The Data and Artificial Intelligence team in Microsoft's Developer Division, part of newly formed CoreAI group, is working on the future of AI for Developers! We are a diverse, entrepreneurial and multi-disciplinary group of scientists, researchers and engineers with passion for using AI to improve the productivity of millions of developers around the world. We have released AI for code advancements in Github Copilot, VS Code, Visual Studio and other Microsoft Copilots. We are seeking a Principal Data & Applied Science Manager to lead high-impact projects at the intersection of AI and software engineering. In this role, you will drive the development and application of advanced data science techniques-especially those involving LLMs-to real-world developer workflows. You'll collaborate closely with data scientists, engineers, researchers, and product teams across Microsoft and GitHub to shape the next generation of AI-powered developer workflows . You will have the opportunity to design and lead experiments, build and evaluate state-of-the-art models (including RAG pipelines , finetuning and evaluation frameworks), and guide your team in turning insights into scalable product features. Most importantly, you'll play a key role in bridging cutting-edge research and production, using data to inform decisions, drive iteration, and deliver measurable impact at scale. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

The Data and Artificial Intelligence team in Microsoft's Developer Division, part of newly formed CoreAI group, is working on the future of AI for Developers! We are a diverse, entrepreneurial and multi-disciplinary group of scientists, researchers and engineers with passion for using AI to improve the productivity of millions of developers around the world. We have released AI for code advancements in Github Copilot, VS Code, Visual Studio and other Microsoft Copilots. We are seeking a Principal Data & Applied Scien tist to work on groundbreaking and high-impact projects at the intersection of AI and software engineering. In this role, you will drive the development and application of advanced data science techniques-especially those involving LLMs-to real-world developer workflows. You'll collaborate closely with data scientists, engineers, researchers, and product teams across Microsoft and GitHub to shape the next generation of AI-powered developer workflows . You will have the opportunity to design and lead experiments, build and evaluate state-of-the-art models (including RAG pipelines , finetuning and evaluation frameworks), and turn insights into scalable product features. Most importantly, you'll play a key role in bridging cutting-edge research and production, using data to inform decisions, drive iteration, and deliver measurable impact at scale. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond .

The Data and Artificial Intelligence team in Microsoft's Developer Division, part of newly formed CoreAI group, is working on the future of AI for Developers! We are a diverse, entrepreneurial and multi-disciplinary group of scientists, researchers and engineers with passion for using AI to improve the productivity of millions of developers around the world. We have released AI for code advancements in Github Copilot, VS Code, Visual Studio and other Microsoft Copilots. We are looking for a passionate and growth-oriented Senior Applied Scientist to join our team working on high-impact projects at the intersection of AI and software engineering . As an Applied Scientist, you will play a pivotal role in development and application of advanced data science techniques-especially those involving LLMs-to real-world developer workflows. You'll collaborate closely with data scientists, engineers, researchers, and product teams across Microsoft and GitHub to shape the next generation of AI-powered developer workflows . You will have the opportunity to design and lead experiments, build and evaluate state-of-the-art models (including RAG pipelines , finetuning and evaluation frameworks), turning insights into scalable product features. Most importantly, you'll play a key role in bridging cutting-edge research and production, using data to inform decisions, drive iteration, and deliver measurable impact at scale. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

The Data and Artificial Intelligence team in Microsoft's Developer Division, part of newly formed CoreAI group, is working on the future of AI for Developers! We are a diverse, entrepreneurial and multi-disciplinary group of scientists, researchers and engineers with passion for using AI to improve the productivity of millions of developers around the world. We have released AI for code advancements in Github Copilot, VS Code, Visual Studio and other Microsoft Copilots. We are looking for a passionate and growth-oriented Applied Scientist II to join our team working on high-impact projects at the intersection of AI and software engineering . As an Applied Scientist, you will play a pivotal role in development and application of advanced data science techniques-especially those involving LLMs-to real-world developer workflows. You'll collaborate closely with data scientists, engineers, researchers, and product teams across Microsoft and GitHub to shape the next generation of AI-powered developer workflows . You will have the opportunity to design and lead experiments, build and evaluate state-of-the-art models (including RAG pipelines , finetuning and evaluation frameworks), turning insights into scalable product features. Most importantly, you'll play a key role in bridging cutting-edge research and production, using data to inform decisions, drive iteration, and deliver measurable impact at scale. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

As an experienced professional with a minimum of 8 years of experience, you will be responsible for architecting and implementing scalable solutions for training and fine-tuning Language Model Models (LLMs). Your role will involve applying prompt engineering, transfer learning, and optimization techniques to enhance model performance. Additionally, you will integrate LLMs into customer-facing applications such as chatbots, VOICE AI agents, Expert AI agents, and recommendation engines. Collaboration with Customer Experience (CX) and product teams will be a key aspect of this role, as you work towards embedding AI into customer journeys to improve personalization, automation, and engagement. Designing AI-driven solutions for omnichannel support, sentiment analysis, and real-time feedback loops will also be part of your responsibilities. Ensuring that AI systems align with customer satisfaction goals and ethical AI principles is essential. In this position, you will provide technical leadership and manage a multidisciplinary team of AI engineers, data scientists, and MLOps professionals. It will be crucial to drive agile development practices and cultivate a culture of innovation and accountability within the team. Your role will involve conducting and applying research in Natural Language Processing (NLP), LLMs, and AI infrastructure to address real-world customer experience challenges. You will have the opportunity to contribute to publications, patents, or open-source initiatives as appropriate. Guiding the team and maintaining detailed architecture diagrams, design documents, and technical specifications will also be part of your responsibilities. This position is based in Pune (Wakad) and requires an immediate joiner or a notice period of up to 30 days.,

About the Company: At Anyscale, we are dedicated to democratizing distributed computing and making it accessible to software developers of all skill levels. We are commercializing Ray, a popular open-source project that has created a robust ecosystem of libraries for scalable machine learning. Ray is already being utilized by prominent companies like OpenAI, Uber, Spotify, Instacart, Cruise, among others, to accelerate the advancement of AI applications in the real world. With Anyscale, we aim to create the optimal platform for running Ray, enabling any developer or data scientist to scale a machine learning application from their personal laptop to a cluster without requiring expertise in distributed systems. Backed by prestigious investors such as Andreessen Horowitz, NEA, and Addition, Anyscale has raised over $250 million to date. Job Summary: As a Technical Services Engineer at Anyscale, your primary responsibility will be to act as a technical advisor and advocate for Anyscale customers. You will play a pivotal role in guiding customers through their post-sale journey, assisting with onboarding, adoption, and growth on the Anyscale platform. Additionally, you will be tasked with troubleshooting and resolving customer tickets while driving consumption. Given the continuous evolution of the Anyscale platform, close collaboration with our engineering teams will be essential to debug complex issues. This role is ideal for individuals who are passionate about ML/AI, LLM, vLLM, and the impact of AI on next-generation applications. It offers an opportunity to make a significant impact in a collaborative, fast-paced environment while contributing to the development of a new segment in this field. Key Responsibilities: - Serve as a solutions architect, technical advisor, and internal advocate for key customers, facilitating successful onboarding and adoption of the Anyscale platform. - Take ownership of customer issues from start to finish, including troubleshooting, triaging, escalations, and resolution. - Engage in our follow-the-sun customer support model to ensure consistent resolution of high-priority tickets. - Monitor open customer bugs and feature requests to influence prioritization and provide timely updates upon resolution. - Manage the current workload status of key customers and identify new workloads to drive account expansion. - Contribute to the enhancement of internal tools and documentation, such as playbooks, guides, and best practices, based on observed patterns. - Offer feedback and collaborate with product and engineering teams to address customer issues and enhance the product experience. - Cultivate and maintain strong relationships with technical stakeholders within customer accounts. Qualifications: - 5+ years of experience in a customer-facing technical role, with at least 2 years in a dynamic, fast-paced, startup-like environment. - Strong organizational skills and the ability to manage multiple customer needs simultaneously. - Hands-on experience in ML/AI, Data Engineering, and Data Science. - Proficiency in cloud platforms such as AWS, GCP, or Azure. - Excellent communication and interpersonal skills. - Strong sense of ownership, self-motivation, and a willingness to acquire new skills. - Willingness to enhance the knowledge and skills of peers through mentorship, trainings, and shadowing. Bonus Qualifications: - Experience with Ray. - Knowledge of MLOps platforms. - Familiarity with container orchestration platforms (e.g., Kubernetes), infrastructure as code (e.g., Terraform), CI/CD tools (e.g., Github Actions). - Knowledge or training in fine-tuning and inference/serving of LLMs. Compensation: At Anyscale, we adopt a market-based approach to compensation, guided by data-driven, transparent, and consistent practices. The target salary for this role may be adjusted over time to align with market trends. Anyscale Inc. is an E-Verify company and you may review the Notice of E-Verify Participation and the Right to Work posters in English and Spanish.,

As a part of Cowbell's innovative team in the field of cyber insurance, you will play a crucial role in designing and implementing RAG-based systems, integrating LLMs with vector databases, search pipelines, and knowledge retrieval frameworks. Your responsibilities will include developing intelligent AI agents that automate tasks, retrieve relevant information, and enhance user interactions. You will work with APIs, embeddings, and multi-modal retrieval techniques to improve the performance of AI applications. Additionally, you will be tasked with optimizing inference pipelines and enhancing LLM serving, fine-tuning, and distillation for efficiency. Staying abreast of the latest advancements in generative AI and retrieval techniques will be essential, along with collaborating with stakeholders and cross-functional teams to address business needs and develop impactful ML models and AI-driven automation solutions. The ideal candidate for this position should hold a Master's degree in Computer Science, Data Science, AI, Machine Learning, or a related field (or a Bachelor's degree with significant experience). You should have at least 5 years of experience in machine learning, deep learning, and NLP for real-world applications, as well as a minimum of 1 year of hands-on experience with LLMs and generative AI. Expertise in RAG architectures, vector search, and retrieval methods is required, along with proficiency in Python and experience with LLM APIs such as OpenAI, Hugging Face, Anthropic, etc. Experience in integrating LLMs into real-world applications, solid foundation in machine learning, statistical modeling, and AI-driven software development, as well as knowledge of prompt engineering, few-shot learning, and prompt chaining techniques are also preferred qualifications. Strong software engineering skills, including experience with cloud platforms like AWS, and excellent problem-solving abilities, communication skills, and the capacity to work independently are crucial for this role. Preferred qualifications for the position include proficiency in PyTorch or TensorFlow for deep learning model development, experience in LLM fine-tuning, model compression, and optimization, familiarity with frameworks like LangChain, LlamaIndex, or Ollama, experience with multi-modal retrieval systems (text, image, structured data), and contributions to open-source AI projects or published research in AI/ML. At Cowbell, employees are offered an equity plan, wealth enablement plan for select customer-facing roles, comprehensive wellness program, meditation app subscriptions, lunch and learn sessions, a book club, happy hours, and more for professional development and growth opportunities. The company is committed to fostering a collaborative and dynamic work environment where transparency and resilience are valued, and every employee is encouraged to contribute and thrive. Cowbell is an equal opportunity employer, promoting diversity and inclusivity in the workplace and providing competitive compensation, comprehensive benefits, and continuous opportunities for professional development. To learn more about Cowbell and its mission in the cyber insurance industry, please visit https://cowbell.insure/.,

The Cisco Security AI team is dedicated to delivering AI products and platforms for all Cisco secure products and portfolios, enabling businesses worldwide to defend against threats and protect critical aspects of their operations with security resilience. We are committed to enhancing business security and simplifying security measures through the use of AI and Machine Learning. We are currently looking for a talented Machine Learning Engineer who can contribute significantly to the cybersecurity industry. As an accomplished and forward-thinking Senior Machine Learning Engineer, you have a proven track record of leading teams and designing machine learning solutions that have had a substantial impact. Your passion for machine learning is evident through your successful development and implementation of models that effectively address real-world challenges. You possess deep knowledge of LLM Architecture, including a comprehensive understanding of large language models such as Transformer-based models like GPT and their variations. Your expertise extends to Language Model Training and Fine-Tuning, Data Preprocessing for NLP, Transfer Learning and Adaptation, Handling Ambiguity and Context in Text, Application of LLMs, Data Annotation and Evaluation, as well as Scalability and Deployment. In your role, you will be responsible for tasks such as Model Training, Optimization, and Evaluation, Algorithm Development for Enhanced Language Understanding, Applying LLMs to Cybersecurity, Deployment Strategy, Documentation and Best Practices, Experimentation with Emerging Technologies and Methods, and Mentoring and Cross-Functional Collaboration. Basic qualifications for this position include a BA/BS degree with 7+ years of experience or an MS degree with 5+ years of experience as a machine learning engineer. You should have solid experience in machine learning engineering, a strong portfolio of successful projects, and expertise in building machine learning systems and scalable solutions. Proficiency in machine learning algorithms, deep learning, and statistical modeling is also essential. Preferred qualifications include an advanced degree in Computer Science, Data Science, Statistics, Computational Linguistics, or a related field, proficiency in programming languages such as Python or R, experience with machine learning libraries like TensorFlow, PyTorch, scikit-learn, excellent problem-solving and communication skills, and a proven ability to work collaboratively in cross-functional teams.,