633 Gen Ai Jobs - Page 23

At Pfizer, technology drives everything we do. Our digital and 'data first' strategy focuses on implementing innovative and impactful technology solutions across all functions, from research to manufacturing. Whether you are digitizing drug discovery and development, identifying innovative solutions, or streamlining our processes, you will be making a significant impact on countless lives. What You Will Achieve In this role, you will: Set objectives and manage high-complexity projects. Lead and allocate people, technology, and financial resources, including those outside direct authority. Actively share knowledge across multiple functions and contribute to knowledge management processes. Assess talent needs, identify potential talent within the organization, and hold managers accountable for developing diverse teams. Lead teams to implement strategic direction and influence senior management decisions. Understand business requirements to recommend technical solutions and ensure successful technology implementation. ROLE SUMMARY The Pfizer Intelligent Assistance Solutions (IAS) Team within the AI Data and Analytics (AIDA) organization is responsible for the products/platforms strategy for Machine Learning (ML), Natural Language Process (NLP) and Generative AI capabilities across the enterprise. Its mission is to innovate, build, standardize and scale breakthrough AI/GenAI solutions, products and platforms that enable Pfizer to connect digitally with all our customers. The IAS Team seeks an AI Product Engineer who will be responsible for leading a portfolio focused on developing innovative and interactive AI products leveraging data, machine/deep learning, NLP, Generative AI and cloud engineering concepts to create breakthrough solutions that change patients lives. Day-to-day, the Product Engineer will actively contribute to a matrixed team consisting of Product Managers/Owners, UX/CX, Enterprise Architects, Solutions Engineers, and Platform Owners to innovative, design and scale AI products such as Chatbots, Voice Assistants, Call Center Virtual Agents and Avatar Digital Humans. ROLE RESPONSIBILITIES Responsible for delivering the solution architecture, technical and functional design for AI products and platforms. Manage the development efforts of multiple AI projects in parallel across various business functions Lead and monitor program progress, eliminate technical barriers, and ensure effective deliver of solutions to add values to our business customers. Integrate and collaborate with different technical teams across Digital to drive overall implementation and delivery. Develop strategic reusable AI components and micro-services that could be leveraged across projects to streamline delivery efforts. Act as a thought leader on emerging AI capabilities and solutions Provide technical leadership and oversight to a team of contractors. Collaborate with key stakeholders across Pfizer Digital and Strategic Partners around innovation, scale, and operational efficiencies. BASIC QUALIFICATIONS Master’s in engineering, Computer Science or Data Science 8+ years of experience in developing enterprise software solutions 5+ years of experience with developing Conversational AI/GenAI based Digital Assistant/Chatbot based Solutions 5+ Years of managing and providing technical oversight to software development teams 5+ years of experience building cloud native solutions in AWS (MS Azure and Google nice to have) Strong working knowledge of NLP/NLU, Conversational AI, Generative AI concepts, architectures, and best practices Hands-on experience with developing solutions on industry leading Conversational AI platforms (i.e.: AWS LEX, Kore.ai, Google Dialogflow) Experience with Conversational design flow principals for chat, voice and avatar based solution types Experience with agile software development life cycle methodology and tools. Proven ability in performance testing and optimization of developed source code for an enterprise business Ability to thrive in a fast-paced multi-disciplinary environment Ability to effectively multi-task and drive several workstreams forward Strong written and verbal communications skills GENERAL SKILLS REQUIREMENTS Advisor/Consultancy: Provides well-informed advice, typically within a specific technical specialization, ensuring that it is properly understood and appropriately exploited, to enhance the effectiveness of significant activities. Provides advice on technical aspects of Conversational UI and ensuresthat relevant technical strategies, policies, standards and practices are applied correctly. Technical Acumen: Maintains knowledge of specific technologies provides detailed advice regarding their application, executes specialized tasks. Thought Leadership & Advocacy: Develop awareness of industry developments and best practices Provide thought leadership in emerging technologies as they relate to AI and Conversational UI Presentation Skills: Ability to develop visually simple and appealing PowerPoint presentations and be comfortable with articulating and communicating with the right authority and messaging to a broad range of stakeholders Work Location Assignment: Hybrid

Mandatory Skills- NLP,GenAI,Machine Learning,Deployment,LLM,MLops Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

Mandatory Skills- NLP,GenAI,Machine Learning,Deployment,LLM,MLops Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

Mandatory Skills- NLP,GenAI,Machine Learning,Deployment,LLM,MLops Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

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

As an AI lead specializing in Generative AI, and Machine Learning Engineering (MLE), you will be at the forefront of AI innovation. Your role will involve designing, deploying, and operationalizing sophisticated modelsincluding large language models (LLMs)to solve complex business problems. You will work closely with cross-functional teams to create scalable, production-ready AI solutions that drive intelligent automation and creativity across industries. Your Key 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, and provide advisory on the adoption and operationalization of Generative AI and ML.

As an AI lead specializing in Generative AI, and Machine Learning Engineering (MLE), you will be at the forefront of AI innovation. Your role will involve designing, deploying, and operationalizing sophisticated modelsincluding large language models (LLMs)to solve complex business problems. You will work closely with cross-functional teams to create scalable, production-ready AI solutions that drive intelligent automation and creativity across industries. Your Key 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, and provide advisory on the adoption and operationalization of Generative AI and ML.

As an AI lead specializing in Generative AI, and Machine Learning Engineering (MLE), you will be at the forefront of AI innovation. Your role will involve designing, deploying, and operationalizing sophisticated modelsincluding large language models (LLMs)to solve complex business problems. You will work closely with cross-functional teams to create scalable, production-ready AI solutions that drive intelligent automation and creativity across industries. Your Key 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, and provide advisory on the adoption and operationalization of Generative AI and ML.

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.

We are seeking a skilled, motivated, and quick-learner Full Stack Developer to join our team working on cutting-edge Gen AI development work. The successful candidate will be responsible for developing innovative applications and solutions using including frontend and backend. While the solutions will often utilize Retrieval Augmented Generation (RAG), Agentic frameworks the role will not be limited to this, and will involve various AI technologies. Develop and maintain web applications using Angular, NDBX frameworks, and other modern technologies. Design and implement databases in Postgres DB, apply & implement ingestion and retrieval pipelines using pgvector, neo4j, ensuring efficient and secure data practices. Use different generative AI models & frameworks such as LangChain, Haystack, LlamIndex etc for chucking, embeddings, chat completions, integration with different data sources etc. Familiarity and experience with different agentic frameworks and technique like Langgraph, AutoGen, CrewAI, tool using techniques like MCP (Model Context Protocol). Use Azure & AWS cloud platforms in implementation to stay relevant to company AI guidelines requirements. Usage of OpenAPI standards, API first approach to develop APIs for communication between different software components. Collaborate with the team members to integrate various GenAI capabilities into the applications, including but not limited to RAG. Write clean, maintainable, and efficient code that adheres to company standards. Conduct testing to identify and fix bugs or vulnerabilities. Use collaboration and versioning tools such as GitHub for effective team working and code management. Stay updated with emerging technologies and apply them into operations and activities. Show a strong desire for continuous learning and the ability to quickly adapt and implement new technologies. Bachelor's degree in Computer Science, Information Technology, or a related field with 6+ years of working experience. Proven experience as a Full Stack Developer or similar role in designing, developing and deploying end to end applications. Knowledge of multiple front-end languages and libraries (e.g. HTML/ CSS, JavaScript, XML, jQuery). Experience with Angular and NDBX frameworks. Good experience with database technology such as Postgres DB, vector databases. Experience developing APIs following the OpenAPI standards. Understanding & experience in various generative AI models on cloud platforms such as Azure/ AWS, including Retrieval Augmented Generation, Prompt engineering, Agentic RAG, Agentic frameworks, Model context protocols etc. Experience with collaboration and versioning tools such as GitHub Experience with docker images, containers to package up an application with all the parts it needs, such as libraries and other dependencies, and ship it all out as one package. Functional Demonstrated responsiveness and accuracy Strong collaboration and teamwork skills Behaviours Take Ownership & Accountability Assertiveness & Persuasiveness Great attention to detail and problem-solving skills. Self-motivated with a keen interest in learning and implementing new technologies.

Position :- Corporate AI Trainer Location:- Delhi NCR Mode :- Hybrid Role Summary: We are seeking an experienced and AI-certified Corporate Trainer to lead the design, delivery, and evaluation of training programs that enhance employees' capabilities in digital transformation and artificial intelligence. The ideal candidate will have a strong understanding of AI concepts and tools, combined with instructional design and training facilitation expertise. Key Responsibilities: Develop and deliver engaging training sessions on AI fundamentals, tools, and applications. Train non-technical teams on using AI tools like ChatGPT, Microsoft Copilot, Notion AI, etc. Customize learning content for different departments based on business needs. Design e-learning modules and interactive workshops using AI-enhanced platforms. Collaborate with department heads to assess skill gaps and recommend AI-based solutions. Stay up to date with emerging AI trends and integrate them into L&D strategies. Evaluate training effectiveness through surveys, assessments, and impact analysis. Support digital transformation efforts and change management initiatives. Qualifications: Bachelors or Masters degree Certification in AI (e.g., Coursera, IBM, Microsoft, edX, or equivalent). Experience: 3+ years of experience as a corporate trainer, L&D specialist, or similar role. At least 3 year of experience working with AI tools or teaching AI-related content. Skills: Strong knowledge of AI/ML concepts (no coding required, but technical understanding is a plus). Excellent communication, facilitation, and interpersonal skills. Proficiency in using LMS platforms, AI tools, and e-learning development software. Ability to simplify complex topics for non-technical audiences. Creative and forward-thinking mindset. Preferred: Experience with tools like ChatGPT, Synthesia, Midjourney, Microsoft Copilot, or similar. Knowledge of data literacy, prompt engineering, or digital productivity tools.

Product Manager Experience: 2 - 4 Years Exp Salary : Competitive Preferred Notice Period : Within 15 Days Shift : 10:00AM to 7:00PM IST Opportunity Type: Onsite (Bengaluru) Placement Type: Permanent (*Note: This is a requirement for one of Uplers' Clients) Must have skills required : Product Management, Technical Project Management, Gen AI Infibeam (One of Uplers' Clients) is Looking for: Product Manager who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you. Role Overview Description Were seeking a passionate Product Manager to join our growing Bangalore team. Who You Are Youre a self-starter who thrives in the fast-paced environment of a startup. You can take ownership of projects, see them through to completion, and wear multiple hats as needed. Youre customer obsessed. Understanding developer needs and pain points is your top priority. Youll leverage user research to constantly improve our product's functionality and user experience. ¢ Youre a product roadmap pro. You can translate business goals and user insights into a clear and actionable product vision. ¢ Youre a fast learner with a knack for communication. You can bridge the gap between technical and non-technical teams, effectively communicating with deep tech and research teams to understand complex concepts and translate them into actionable product features. What Youll Do ¢ Partner with engineering and data science teams in Bangalore to define the product roadmap. ¢ Conduct user research to understand developer needs and pain points. ¢ Develop and prioritize product features based on market research, user feedback, and business goals. ¢ Create clear and concise product specifications for the products functionalities (model orchestration, video processing, multimodal models). ¢ Define and track key performance indicators (KPIs) to measure product success. ¢ Own the user experience (UX) , ensuring a smooth and intuitive platform for developers of all skill levels. ¢ Collaborate with marketing and sales teams to create compelling product messaging and launch strategies. You bring the... ¢ 2-3 years of experience in product management, ideally in the software or technology industry. ¢ A strong understanding of the software development lifecycle. ¢ Experience in user research and user experience (UX) design. ¢ Excellent analytical and problem-solving skills. ¢ Strong communication and collaboration skills. ¢ Familiarity with Artificial Intelligence (AI) concepts, particularly computer vision. Bonus points for... ¢ Experience working in a fast-paced startup environment. ¢ Experience working in a deep-tech or research-oriented environment. If youre a passionate product leader who thrives on innovation and customer obsession, we want to hear from you! How to apply for this opportunity: Easy 3-Step Process: 1. Click On Apply! And Register or log in on our portal 2. Upload updated Resume & Complete the Screening Form 3. Increase your chances to get shortlisted & meet the client for the Interview! About Our Client: Infibeam Avenues Limited (IAL) is a leading digital payments and e-commerce technology platforms company in India and provides a comprehensive suite of web services spanning digital payment solutions, data centre infrastructure and software platforms. We provide solutions to merchants, enterprises, corporations and governments in both domestic as well as international markets to enable online commerce. Our digital technology facilitates businesses and governments to execute e-commerce transactions in a safe and secure manner. About Uplers: Our goal is to make hiring and getting hired reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant product and engineering job opportunities and progress in their career. (Note: There are many more opportunities apart from this on the portal.) So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!

Role & responsibilities Location : Gurgaon/Bangalore/Mumbai Must have skills : Python Programming, Machine Learning, Statistical Analysis, Data Science, Generative AI (GPT, DALL•E, GANs), Cloud (AWS/Azure/GCP), Model Deployment, API Development, Docker/Kubernetes Good to have skills : PyTorch, TensorFlow, Large-Scale Data Processing, Cloud Integrations, NLP, Computer Vision, Security Best Practices, Performance Optimization, Open-Source Contributions Experience : Minimum 3+ years of experience in Data Science, Machine Learning, or related fields with a focus on Generative AI models (LLMs) and API development. Email : ashwini.patil@sigmacareers.in Current CTC- Expected CTC - Location - Total Years of experience - Notice Period(LWD) -

If you are interested fill this form: https://developers.turing.com/invite/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJqb2JJZCI6MzMyNDcsInVzZXJJZCI6MTE3OCwidXNlclJvbGUiOiJUQV9TT1VSQ0VEIiwiaWF0IjoxNzQ0NzM3ODUwfQ.WndOiWb7zy8UiszDNKT9xFHjw4SHqCKEGClcvkXh83s Senior LLM Engineer GenAI / ML (Python, Langchain) Full Time Location: India Overall Experience: 7- 12 Years Focus: 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: 1+ years of 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.

We are seeking high-energy, driven, and innovative Data Scientists to join our Data Science Practice to develop new, specialized capabilities for Axtria, and to accelerate the company's growth by supporting our clients commercial & clinical strategies. Job Responsibilities Be an Individual Contributor to the Data Science team and solve real-world problems using cutting-edge capabilities and emerging technologies. Help clients translate the business use cases they are trying to crack into data science solutions . Provide genuine assistance to users by advising them on how to leverage Dataiku DSS to implement data science projects, from design to production. Data Source Configuration, Maintenance, Document and maintain work-instructions . Deep working on machine learning frameworks such as TensorFlow, Caffe, Keras, SparkML. Expert knowledge in Statistical and Probabilistic methods such as SVM, Decision-Trees, Clustering. Expert knowledge of python data-science and math packages such as NumPy, Pandas, Sklearn. Proficiency in object-oriented languages (Java and/or Kotlin), Python and common machine learning frameworks (TensorFlow, NLTK, Stanford NLP, Ling Pipe etc.). Education Bachelor Equivalent - Engineering Master's Equivalent - Engineering Work Experience Data Scientist: 3-5 years of relevant experience in advanced statistical and mathematical models and predictive modeling using Python. Experience in the data science space prior relevant experience in Artificial intelligence and Machine Learning algorithms for developing scalable models, supervised and unsupervised techniques like NLP and deep Learning Algorithms. Ability to build scalable models using Python, R-Studio, R Shiny, PySpark, Keras, and TensorFlow. Experience in delivering data science projects leveraging cloud infrastructure. Familiarity with cloud technology such as AWS / Azure and knowledge of AWS tools such as S3, EMR, EC2, Redshift, and Glue; viz tools like Tableau and Power BI. Relevant experience in Feature Engineering, Feature Selection, and Model Validation on Big Data. Knowledge of self-service analytics platforms such as Dataiku/ KNIME/ Alteryx will be an added advantage. ML Ops Engineering: 3-5 years of experience with MLOps Frameworks like Kubeflow, MLFlow, Data Robot, Airflow, etc., experience with Docker and Kubernetes, OpenShift. Prior experience in end-to-end automated ecosystems including, but not limited to, building data pipelines, developing & deploying scalable models, orchestration, scheduling, automation, and ML operations. Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure, or GCP). Programming languages like Python, Go, Ruby, or Bash, a good understanding of Linux, knowledge of frameworks such as Keras, PyTorch, TensorFlow, etc. Ability to understand tools used by data scientists and experience with software development and test automation. Good understanding of advanced AI/ML algorithms & their applications. Gen AI: Minimum of 4-6 years develop, test, and deploy Python based applications on Azure/AWS platforms. Must have basic knowledge on concepts of Generative AI / LLMs / GPT . Deep understanding of architecture and work experience on Web Technologies. Python, SQL hands-on experience . Expertise in any popular python web frameworks e.g. flask, Django etc. Familiarity with frontend technologies like HTML, JavaScript, REACT. Be an Individual Contributor in the Analytics and Development team and solve real-world problems using cutting-edge capabilities and emerging technologies based on LLM/GenAI/GPT. Can interact with client on GenAI related capabilities and use cases.

We are seeking high-energy, driven, and innovative Data Scientists to join our Data Science Practice to develop new, specialized capabilities for Axtria, and to accelerate the company's growth by supporting our clients commercial & clinical strategies. Job Responsibilities Be an Individual Contributor to the Data Science team and solve real-world problems using cutting-edge capabilities and emerging technologies. Help clients translate the business use cases they are trying to crack into data science solutions . Provide genuine assistance to users by advising them on how to leverage Dataiku DSS to implement data science projects, from design to production. Data Source Configuration, Maintenance, Document and maintain work-instructions . Deep working on machine learning frameworks such as TensorFlow, Caffe, Keras, SparkML. Expert knowledge in Statistical and Probabilistic methods such as SVM, Decision-Trees, Clustering. Expert knowledge of python data-science and math packages such as NumPy, Pandas, Sklearn. Proficiency in object-oriented languages (Java and/or Kotlin), Python and common machine learning frameworks (TensorFlow, NLTK, Stanford NLP, Ling Pipe etc.). Education Bachelor Equivalent - Engineering Master's Equivalent - Engineering Work Experience Data Scientist: 3-5 years of relevant experience in advanced statistical and mathematical models and predictive modeling using Python. Experience in the data science space prior relevant experience in Artificial intelligence and Machine Learning algorithms for developing scalable models, supervised and unsupervised techniques like NLP and deep Learning Algorithms. Ability to build scalable models using Python, R-Studio, R Shiny, PySpark, Keras, and TensorFlow. Experience in delivering data science projects leveraging cloud infrastructure. Familiarity with cloud technology such as AWS / Azure and knowledge of AWS tools such as S3, EMR, EC2, Redshift, and Glue; viz tools like Tableau and Power BI. Relevant experience in Feature Engineering, Feature Selection, and Model Validation on Big Data. Knowledge of self-service analytics platforms such as Dataiku/ KNIME/ Alteryx will be an added advantage. ML Ops Engineering: 3-5 years of experience with MLOps Frameworks like Kubeflow, MLFlow, Data Robot, Airflow, etc., experience with Docker and Kubernetes, OpenShift. Prior experience in end-to-end automated ecosystems including, but not limited to, building data pipelines, developing & deploying scalable models, orchestration, scheduling, automation, and ML operations. Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure, or GCP). Programming languages like Python, Go, Ruby, or Bash, a good understanding of Linux, knowledge of frameworks such as Keras, PyTorch, TensorFlow, etc. Ability to understand tools used by data scientists and experience with software development and test automation. Good understanding of advanced AI/ML algorithms & their applications. Gen AI: Minimum of 4-6 years develop, test, and deploy Python based applications on Azure/AWS platforms. Must have basic knowledge on concepts of Generative AI / LLMs / GPT . Deep understanding of architecture and work experience on Web Technologies. Python, SQL hands-on experience . Expertise in any popular python web frameworks e.g. flask, Django etc. Familiarity with frontend technologies like HTML, JavaScript, REACT. Be an Individual Contributor in the Analytics and Development team and solve real-world problems using cutting-edge capabilities and emerging technologies based on LLM/GenAI/GPT. Can interact with client on GenAI related capabilities and use cases.

Job Title: Senior Data Engineer Databricks, Azure & Mosaic AI Role Summary: We are seeking a Senior Data Engineer with extensive expertise in Data & Analytics platform modernization using Databricks, Azure, and Mosaic AI. This role will focus on designing and optimizing cloud-based data architectures, leveraging AI-driven automation to enhance data pipelines, governance, and processing at scale. Key Responsibilities: Architect & modernize Data & Analytics platforms using Databricks on Azure. Design and optimize Lakehouse architectures integrating Azure Data Lake, Databricks Delta Lake, and Synapse Analytics. Implement Mosaic AI for AI-driven automation, predictive analytics, and intelligent data engineering solutions. Lead the migration of legacy data platforms to a modern cloud-native Data & AI ecosystem. Develop high-performance ETL pipelines, integrating Databricks with Azure services such as Data Factory, Synapse, and Purview. Utilize MLflow & Mosaic AI for AI-enhanced data processing and decision-making. Establish data governance, security, lineage tracking, and metadata management across modern data platforms. Work collaboratively with business leaders, data scientists, and engineers to drive innovation. Stay at the forefront of emerging trends in AI-powered data engineering and modernization strategies. Required Skills & Qualifications: Experience in Data Engineering, Cloud Platforms, and AI-driven automation. Expertise in Databricks (Apache Spark, Delta Lake, MLflow) and Azure (Data Lake, Synapse, ADF, Purview). Strong experience with Mosaic AI for AI-powered data engineering and automation. Advanced proficiency in SQL, Python, and Scala for big data processing. Experience in modernizing Data & Analytics platforms, migrating from on-prem to cloud. Knowledge of Data Lineage, Observability, and AI-driven Data Governance frameworks. Familiarity with Vector Databases & Retrieval-Augmented Generation (RAG) architectures for AI-powered data analytics. Strong leadership, problem-solving, and stakeholder management skills. Preferred Skills: Experience with Knowledge Graphs (Neo4J, TigerGraph) for data structuring. Exposure to Kubernetes, Terraform, and CI/CD for scalable cloud deployments. Background in streaming technologies (Kafka, Spark Streaming, Kinesis).

Senior Business Analytics Engineer (AI & Data Analytics) Looking for an experienced Business Analytics Engineer (7-8 years) with strong expertise in Python, SQL, and Tableau to support our Sales & Marketing teams. The ideal candidate will have a proven ability to leverage and build AI-driven data models for research and structured database prompts, driving business insights and efficiency. In This Role, You Will: Develop AI-powered analytics models to extract insights using research structured as well as non-structured databases for Sales & Marketing Build predictive models and automation solutions to enhance lead generation, pipeline forecasting, and campaign effectiveness Design and optimize SQL queries for data extraction, transformation, and reporting Create interactive dashboards and reports in Tableau to support decision-making Collaborate with stakeholders across Sales, Marketing, and Business Analytics to translate business needs into data-driven solutions Implement AI-driven research methodologies to enhance data exploration and prompt-based insights Ensure data accuracy, governance, and efficiency in analytics workflows Heres What You Need: 79 years of experience in Business Analytics & Gen AI Proficiency in Python (including AI & Data Analytics libraries) Strong SQL skills for data extraction and transformation across large datasets Hands-on experience with Tableau for reporting and visualization Experience in building and optimizing Gen AI models for business analytics Ability to work with structured databases and AI-driven prompt generation for research Strong analytical mindset with problem-solving abilities Experience in a SaaS environment supporting Sales & Marketing is a plus

Payoll Company- Anlage Infotech Client- NTT Data Role & responsibilities Experience (Relevant) in Years 4 to 7 years Top 3 skills which is mandatory Python frameworks such DL, ML, Flask,FastAPI Generative AI models Proficient in python and common libraries such as numpy, pandas, matplotlib, and scikit-learn. Shift Timings 12 to 10 or 10 to 08 No. of Position 6 No of Interview Rounds 2 Technical skills 5 to 6 years Experience in developing Python frameworks such DL, ML, Fast API, Flask At least 3 years of experience in developing generative AI models using python and relevant frameworks. Strong knowledge of machine learning, deep learning, and generative AI concepts and algorithms. Proficient in python and common libraries such as numpy, pandas, matplotlib, and scikit-learn. Familiar with version control, testing, debugging, and deployment tools. Excellent communication and problem-solving skills. Curious and eager to learn new technologies and domains. Note- Immediate Joiners Only, Serving notice period, 15 Days or Less

To leverage expertise in data architecture and management to design, implement, and optimize a robust data warehousing platform for the pharmaceutical industry. The goal is to ensure seamless integration of diverse data sources, maintain high standards of data quality and governance, and enable advanced analytics through the definition and management of semantic and common data layers. Utilizing Axtria DataMAx and generative AI technologies, the aim is to accelerate business insights and support regulatory compliance, ultimately enhancing decision-making and operational efficiency. Key Responsibilities Data Modeling: Design logical and physical data models to ensure efficient data storage and retrieval. ETL Processes: Develop and optimize ETL processes to accurately and efficiently move data from various sources into the data warehouse. Infrastructure Design: Plan and implement the technical infrastructure, including hardware, software, and network components. Data Governance: Ensure compliance with regulatory standards and implement data governance policies to maintain data quality and security. Performance Optimization: Continuously monitor and improve the performance of the data warehouse to handle large volumes of data and complex queries. Semantic Layer Definition: Define and manage the semantic layer architecture and technology stack to manage the lifecycle of semantic constructs including consumption into downstream systems. Common Data Layer Management: Integrate data from multiple sources into a centralized repository, ensuring consistency and accessibility. Deep expertise in architecting enterprise-grade software systems that are performant, scalable, resilient, and manageable. Architecting GenAI based systems is an added plus. Advanced Analytics: Enable advanced analytics and machine learning to identify patterns in genomic data, optimize clinical trials, and personalize medication. Generative AI: Should have worked with production-ready use case for GenAI based data. Stakeholder Engagement: Work closely with business stakeholders to understand their data needs and translate them into technical solutions. Cross-Functional Collaboration: Collaborate with IT, data scientists, and business analysts to ensure the data warehouse supports various analytical and operational needs. Qualifications Proven experience in data architecture and data warehousing , preferably in the pharmaceutical industry. Strong knowledge of data modeling, ETL processes, and infrastructure design . Experience with data governance and regulatory compliance in the life sciences sector. Proficiency in using Axtria DataMAx or similar data management products . Excellent analytical and problem-solving skills . Strong communication and collaboration skills . Preferred Skills Familiarity with advanced analytics and machine learning techniques . Experience in managing semantic and common data layers . Knowledge of FDA guidelines, HIPAA regulations, and other relevant regulatory standards . Experience with generative AI technologies and their application in data warehousing.

Lead Software Engineer GenAI Development and Exploration Location: India Experience: 5–10 Years Primary Skillset: Generative AI Services, Machine Learning, Python, Cloud, MLOps, Prompt Engineering About the Role: We are seeking a visionary and hands-on Lead Software Engineer to drive Generative AI development and exploration initiatives within our technology innovation team. This role is ideal for someone passionate about applying GenAI to real-world enterprise challenges and who enjoys building proof-of-concepts, production-ready solutions, and scalable AI platforms. Key Responsibilities: • Lead the design and development of Generative AI applications , including LLM-based solutions, document intelligence, summarization, chatbot development, and intelligent automation. • Evaluate and integrate GenAI services and APIs (e.g., Vertex AI , OpenAI , Anthropic , HuggingFace , etc.) into existing or new platforms. • Build scalable backend services and data pipelines to support AI/ML workloads. • Explore and validate use cases across multiple business functions for GenAI application and automation opportunities. • Mentor junior engineers on GenAI design patterns, best practices, and engineering discipline. • Partner with data scientists, product teams, and business stakeholders to define requirements, build MVPs, and iterate toward enterprise-scale solutions. • Champion MLOps practices for GenAI model deployment, monitoring, and lifecycle management. • Contribute to technical research, architecture recommendations, and the long-term GenAI roadmap. Qualifications: • Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field. • 8+ years of experience in software engineering with 2–3 years working directly with AI/ML models or GenAI solutions . • Strong experience with Python , cloud platforms (especially GCP Vertex AI ), and ML frameworks. • Hands-on experience with LLMs , prompt engineering , fine-tuning, and embeddings. • Experience in building and integrating GenAI services in real-time or batch applications. • Solid understanding of machine learning lifecycle , data preprocessing , and deployment pipelines . • Familiarity with CI/CD pipelines , API development , and cloud-native architectures . • Excellent communication, collaboration, and stakeholder management skills.

Main Skills : AI Architect (Solutions/Technical) - LLM/Gen AI + Any Cloud + Python Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : Natural Language Processing (NLP) Roles & Responsibilities: - Expected to perform independently and become an SME. - Required active participation/contribution in team discussions. - Contribute in providing solutions to work related problems. - Develop applications and systems utilizing AI tools and Cloud AI services. - Apply GenAI models as part of the solution. - Implement deep learning and neural networks. - Create chatbots and work on image processing. - Collaborate with team members to provide innovative solutions. Professional & Technical Skills: - Must To Have Skills: Proficiency in Natural Language Processing (NLP). - Strong understanding of statistical analysis and machine learning algorithms. - Experience with data visualization tools such as Tableau or Power BI. - Hands-on implementing various machine learning algorithms such as linear regression, logistic regression, decision trees, and clustering algorithms. - Solid grasp of data munging techniques, including data cleaning, transformation, and normalization to ensure data quality and integrity. Additional Information: - The candidate should have a minimum of 2 years of experience in Natural Language Processing (NLP). Pls share cvs to parul@mounttalent.com

Position Summary: Associate Content Editors are responsible for detecting and correcting errors and editing and proofreading content. This role also involves fact-checking content and ensuring the use of proper spelling, grammar, and punctuation. Responsibilities: Proofread, fact check (when needed), and edit content. Read and evaluate written text for grammatical and spelling errors. Compare edited content against original copy to identify errors or omission. Rephrase written text to ensure content is consistent. Stay abreast with grammatical development as well as new terminologies in English. Edit according to the US English style, while ensuring proper usage of words and grammar. Contact SMEs or freelancers to clarify grammatical inconsistencies related to style and text choice. Ensure that the quality of content is maintained. Ensure that project is completed within set time constraints. Requirements: Strong writing/editing/proofreading skills and an excellent portfolio. An avid reader. Hands-on experience with MS Office, publishing tools, CMS. Strong communication skills, highly organized, an eye for detail along with critical thinking, prioritizing and multitasking skills. A team player with an ability to deliver content within deadlines while maintaining consistency of content. Familiar with Gen AI (Chat GPT prompts etc)