571 Llm Jobs - Page 8

you a Full Stack Engineer passionate about AI, automation , and transforming the way businesses communicate? At InfotechLaunch.com , were on a mission to modernize and automate mundane tasks through smart AI Voice Agents and advanced full-stack applications. Were looking for builders and problem-solvers engineers who dont just code but innovate, optimize, and deliver with passion and precision. This is a full-time, fully remote position for someone who thrives in a fast-paced environment, enjoys direct interaction with clients, and is committed to delivering AI solutions that work every time. Your Role Architect AI Voice Systems Build intelligent AI Voice Agents using Retell , Vapi.ai , and integrate OpenAI GPT-4 , Google Vertex AI , and custom LLMs. Implement natural-sounding conversations , task automation, and real-time event handling. Full-Stack Development Develop scalable, secure, and performant applications with Node.js , Python , Flutter , and React . Build intuitive frontends and powerful backends that integrate seamlessly with AI. UI/UX expert with hands-on experience designing applications using Figma, Adobe XD, and Sketch. Prompt Engineering & LLM Integration Craft effective prompts, design agent logic, and fine-tune LLM-based workflows for real-world tasks. Cloud Infrastructure & DevOps Design and manage cloud infrastructure on AWS , GCP , or Azure , with CI/CD pipelines , Docker , and Kubernetes . Automate testing, deployment, and monitoring of production environments. API & CRM Integration with Make.com Create smart automation using Make.com , Cal.com , Zapier , and integrate with CRMs like HubSpot , Zoho , and Pipedrive . Confidently read and implement API docs, troubleshoot and optimize flows. Direct Client Collaboration Work closely with clients to build, test, and refine solutions. Gather feedback, improve models, and deliver impactful outcomes. What We Expect 89 hours/day of focused, committed remote work Excitement to work with real clients and improve real products A passion for clean code , clear communication , and continuous improvement Required Skills 5+ years of experience in Full Stack Development (Node.js, Python, Flutter, React/Next.js) 23 years working with AI APIs , LLMs , and conversational AI workflows Experience building AI Voice Agents using Retell, Vapi.ai, or similar Deep knowledge of Prompt Engineering and LLM optimization Proficiency in Flutter for mobile and React/Next.js for web Cloud DevOps: AWS/GCP/Azure, CI/CD pipelines, Docker/Kubernetes Strong background in Make.com , Zapier, Cal.com, and REST API integrations CRM Integration: HubSpot, Zoho, Pipedrive Strong debugging, version control (Git), and documentation skills Bonus Skills Voice tech: Whisper, Google Speech, ElevenLabs AI search: LangChain, Pinecone, Weaviate NLP tools: spaCy, HuggingFace Transformers Payment/Bookings: Stripe, Twilio, Firebase Auth What You Get Opportunity to innovate real-world AI workflows Work from anywhere , flexible yet committed Performance-based compensation after 1st month Direct access to clients, decision-makers, and leadership Be part of a fast-growing company using AI to change industries Mentorship and guidance to advance your career and technical depth How to Apply Send us your: Resume / LinkedIn GitHub / Portfolio Details on past AI Voice Agent, Make.com, LLM work Note on why youre excited to automate the future with us! If you align with our expectations and agree to the terms and conditions, please send a message on WhatsApp to +91 81097 31880 to discuss further. Skills : - AI Voice Agent, Full Stack Developer, Python, Node.js, Flutter, React, OpenAI, ChatGPT, Google Vertex AI, Conversational AI, Retell, Make.com, LLM, Prompt Engineering, Cloud Infrastructure, AWS, GCP, Azure, Docker, Kubernetes, CI/CD, Remote Work, AI Developer, API Integration, Digital Transformation, AI Applications, NLP, Speech Recognition, VOPI, AI Hiring, CRM Integrations

Key deliverables: Develop full-stack applications for investment management and trading/valuation support using React, Python (Flask, FastAPI), AWS, and LLM Write and maintain unit tests to ensure code quality Participate in daily scrum meetings with clients to align on deliverables Design and implement REST APIs and cloud-based services Role responsibilities: Collaborate with product and engineering teams to build scalable web applications Integrate LLM and Langchain technologies to enhance product capabilities Manage cloud deployment and DevOps activities for continuous integration and delivery Ensure code quality, documentation, and best development practices

Key deliverables: Develop full-stack applications for investment management and trading/valuation support using React, Python (Flask, FastAPI), AWS, and LLM Write and maintain unit tests to ensure code quality Participate in daily scrum meetings with clients to align on deliverables Design and implement REST APIs and cloud-based services Role responsibilities: Collaborate with product and engineering teams to build scalable web applications Integrate LLM and Langchain technologies to enhance product capabilities Manage cloud deployment and DevOps activities for continuous integration and delivery Ensure code quality, documentation, and best development practices

Key deliverables: Develop investment management and trading/valuation support applications using Python, React, SQL, FastAPI, AWS, and LLM Write and maintain unit tests to ensure code quality Participate in daily scrum meetings with clients to track progress and requirements Implement and maintain REST APIs and UI components Role responsibilities: Design and develop scalable full-stack solutions for financial applications Collaborate with cross-functional teams for cloud deployment and DevOps integration Utilize LLM and Langchain technologies to enhance application capabilities Ensure adherence to best practices in coding, testing, and documentation

Key deliverables: Develop investment management and trading/valuation support applications using Python, React, SQL, FastAPI, AWS, and LLM Write and maintain unit tests to ensure code quality Participate in daily scrum meetings with clients to track progress and requirements Implement and maintain REST APIs and UI components Role responsibilities: Design and develop scalable full-stack solutions for financial applications Collaborate with cross-functional teams for cloud deployment and DevOps integration Utilize LLM and Langchain technologies to enhance application capabilities Ensure adherence to best practices in coding, testing, and documentation

Key deliverables: Develop investment management and trading/valuation support applications using Python, React, SQL, FastAPI, AWS, and LLM Write and maintain unit tests to ensure code quality Participate in daily scrum meetings with clients to track progress and requirements Implement and maintain REST APIs and UI components Role responsibilities: Design and develop scalable full-stack solutions for financial applications Collaborate with cross-functional teams for cloud deployment and DevOps integration Utilize LLM and Langchain technologies to enhance application capabilities Ensure adherence to best practices in coding, testing, and documentation

Position Overview We are seeking an innovative AI & Generative Language Models Manager to lead our organization's initiatives in large language models (LLMs) and generative AI. This role will drive the development and implementation of cutting-edge AI solutions that transform our business operations and create competitive advantages. The ideal candidate will combine deep technical expertise in generative AI with strong leadership skills to build and direct a high-performing team. Core Responsibilities Strategic Leadership Develop and execute our organization's vision and strategy for generative AI and LLM applications Lead a team of AI engineers, researchers, and ML specialists focused on generative AI solutions Identify high-impact business opportunities where LLMs can deliver significant value Establish roadmaps for AI implementation hat align with broader organizational goals Stay at the forefront of rapidly evolving LLM technologies and industry developments Technical Direction Oversee the design, development, and optimization of LLM-based applications Guide the selection, customization, and fine-tuning of language models for specific use cases Establish architecture and frameworks for integrating LLMs into existing products and services Implement best practices for prompt engineering, context handling, and model evaluation Ensure AI solutions balance performance, cost-efficiency, and ethical considerations Team Management Build and mentor a talented team of AI specialists with expertise in generative models Create a collaborative environment that fosters innovation and knowledge sharing Set clear objectives and performance expectations for team members Recruit, develop, and retain top AI talent in a competitive market Promote continuous learning and professional growth within the team Cross-Functional Collaboration Partner with business leaders to translate organizational needs into AI solutions Collaborate with product, engineering, and design teams to integrate AI capabilities Communicate complex AI concepts effectively to various stakeholders Work with legal and compliance teams to ensure responsible AI deployment Drive AI literacy and adoption across the organization Experience 10+ years of experience in artificial intelligence or machine learning 5+ years of hands-on experience with LLMs and generative AI technologies 5+ years in leadership roles managing technical teams Proven track record of successfully implementing AI solutions in production environments Experience with LLM fine-tuning, prompt engineering, and AI application development Technical Skills Deep understanding of LLM architectures, capabilities, and limitations Expertise in natural language processing and generative AI techniques Proficiency in Python and modern AI frameworks (PyTorch, TensorFlow, Hugging Face) Experience with MLOps and AI deployment infrastructures Knowledge of responsible AI practices, including bias mitigation and ethical AI use Education Qualification Master's degree or PhD in Computer Science, AI, Machine Learning, or related field Relevant certifications in AI, machine learning, or cloud technologies are a plus Leadership & Professional Skills Strong strategic thinking and business acumen Excellent communication and stakeholder management abilities Team leadership and mentoring capabilities Problem-solving mindset and ability to drive innovation Project management skills and experience with agile methodologies

Key Responsibilities:- Design and implement sophisticated AI applications leveraging state-of-the-art LLM technologies Develop efficient solutions for LLM integration, fine-tuning, and deployment Optimize model performance, latency, and resource utilization Build and maintain robust data pipelines for training and inference Implement advanced prompt engineering techniques and retrieval-augmented generation (RAG) Develop evaluation frameworks to measure AI system performance and output quality Collaborate with cross-functional teams to understand requirements and deliver solutions Mentor junior developers and share AI/LLM knowledge across the organization Participate in code reviews and ensure adherence to best practices Role Requirement :- 5+ years of software development experience with at least 3 years focused on AI/ML technologies Strong experience working with transformer-based models and LLM APIs Proficiency in Python and relevant AI/ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience with vector databases and semantic search technologies Solid understanding of prompt engineering, RAG, and fine-tuning techniques Familiarity with cloud platforms (AWS, Azure, GCP) for AI model deployment Strong problem-solving skills and attention to detail. Experience with LLM optimization techniques like quantization and distillation Knowledge of AI evaluation metrics and benchmarking methodologies Understanding of multimodal AI systems (text, image, audio) Experience with containerization and orchestration tools (Docker, Kubernetes) Contributions to open-source AI projects or research publications Familiarity with AI ethics and responsible AI development Qualifications:- Bachelors degree in computer science, AI, Machine Learning, or related field Master's in AI, B,Tech,MCA, or related field

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.

AnAI innovator with 79 years of experiencein NLP and Machine Learning, who thrives on buildingproduction-grade GenAI solutionsthat matter.Youll architect and deploy LLM-powered applications, support MLOps initiatives, and mentor the next generation of AI talent. Experience inpharma or life sciencesis a plus! Key Responsibilities: 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.

AnAI innovator with 79 years of experiencein NLP and Machine Learning, who thrives on buildingproduction-grade GenAI solutionsthat matter.Youll architect and deploy LLM-powered applications, support MLOps initiatives, and mentor the next generation of AI talent. Experience inpharma or life sciencesis a plus! Key Responsibilities: 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.

AnAI innovator with 79 years of experiencein NLP and Machine Learning, who thrives on buildingproduction-grade GenAI solutionsthat matter.Youll architect and deploy LLM-powered applications, support MLOps initiatives, and mentor the next generation of AI talent. Experience inpharma or life sciencesis a plus! Key Responsibilities: 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.

Python coding, Machine Learning, AI, CI/CD platform, LLM, Big data Develop and evaluate prompts for AI agents, chatbots, and other capabilities. Test and analyze outputs from AI by experimenting with different

Role & responsibilities Python, ML, NLP (LDA, embeddings, RAG), AI techniques, LLM-based matching (e.g., GPT/embeddings), timeseries forecasting Django is essential Experience with Databricks, Azure ML Stack, OpenAI API, Spark, and fuzzy matching would be a plus. Builds and deploys ML pipelines (incl. MLOps, API endpoints, CI/CD) Works with Lang chain, Azure Synapse, Kubernetes, and modern ML frameworks

Prompt Engineer Experience - 3 to 6 years Location - Chennai Timing - General shift Notice period - Less than 15 days Contract period - 6 months JD: Role Overview: We are looking for a talented and creative Prompt Engineer to join our AI team. In this role, you will design, develop, and refine prompts for large language models (LLMs) to solve business problems, improve product functionality, and optimize user experiences. Your work will directly impact how our products interact with users and leverage cutting-edge AI models. Responsibilities: Design and iterate on effective prompts to drive high-quality responses from LLMs (e.g., GPT-4, Claude, Gemini, etc.). Collaborate with AI researchers, data scientists, and product managers to develop prompt strategies aligned with business goals. Evaluate LLM performance, analyze outputs, and optimize prompts based on performance metrics. Conduct A/B testing of prompts and maintain a library of prompt templates. Stay current on developments in generative AI, NLP, and prompt engineering best practices. Develop documentation, tools, and internal guides to help teams effectively use LLMs. Ensure ethical and responsible use of AI systems, identifying and mitigating model bias or inappropriate outputs. Qualifications: Required: Bachelor's degree in Computer Science, Linguistics, Cognitive Science, or a related field. Strong understanding of LLMs and natural language processing (NLP). Hands-on experience with prompt design for ChatGPT, Claude, Gemini, or similar models. Excellent written communication and analytical skills. Familiarity with programming/scripting (Python preferred) and basic data handling. Preferred: Masters degree or higher in a relevant field. Experience with fine-tuning or RLHF (Reinforcement Learning with Human Feedback). Exposure to prompt chaining, function calling, and multi-model orchestration. Experience using tools such as LangChain, LlamaIndex, or vector databases like Pinecone or Weaviate. Sincerely, Varsha L TS

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

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

Senior Backend Engineer (AI Agent Orchestration) Experience: 3 - 5 Years Exp Salary : Upto USD 1,851 / month Preferred Notice Period : Within 30 Days Shift : 2:00PM to 11:00PM IST Opportunity Type: Remote Placement Type: Contractual Contract Duration: Full-Time, 03 Months (*Note: This is a requirement for one of Uplers' Clients) Must have skills required : Fast API, LLM, Python, RestAPI Good to have skills : React Js BatlabsAI (One of Uplers' Clients) is Looking for: Senior Backend Engineer (AI Agent Orchestration) 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 Job Title: Senior Backend Engineer (AI Agent Orchestration) - Contract Location: Remote About Batlabs AI: At Batlabs AI, we're at the forefront of applying cutting-edge artificial intelligence to solve complex, real-world challenges in the [mention a slightly broader field like "scientific R&D" or "industrial process optimization" if "materials manufacturing" is too specific for IP reasons] space. We're an early-stage startup fueled by innovation, a passion for tackling difficult problems, and a commitment to making a significant impact. If you're excited by the prospect of building intelligent systems from the ground up and shaping the future of AI applications, Batlabs AI is the place for you. The Opportunity: We are seeking a highly skilled and motivated Backend Engineer to join us on a contract basis. You will play a pivotal role in architecting and developing the core backend infrastructure for our AI-powered platform. This isn't just another backend role; you'll be instrumental in building an "army of AI agents" sophisticated, specialized AI systems that will form the backbone of our product. You'll work with state-of-the-art technologies like Large Language Models (e.g., Gemini), Langchain, and secure code execution environments to bring our vision to life. What You'll Do: Design, develop, and deploy robust and scalable backend services using Python and FastAPI. Architect and implement AI agent-based workflows using Langchain, defining agent roles, tools, and orchestration logic. Integrate Large Language Models (like Gemini) for code generation, data analysis, and complex reasoning. Develop and integrate tools for AI agents, potentially involving secure code execution sandboxes (like E2B or Docker). Build and maintain APIs for frontend-backend communication and potential third-party integrations. Collaborate closely with a small, agile team to define requirements, design solutions, and iterate quickly. Champion best practices in software development, including security, testing, and maintainability. Embrace a culture of continuous learning, staying updated with advancements in AI, LLMs, and backend technologies. What We're Looking For: Essential Skills & Experience: Proven experience in backend development using Python. Strong experience with web frameworks, particularly FastAPI. Solid understanding of API design principles (RESTful, etc.). A proactive, problem-solving mindset with a willingness to tackle challenging and often ambiguous problems. Demonstrated ability to learn quickly and adapt to new technologies and concepts. Preferred & Highly Valued Experience: Hands-on experience with Langchain for developing AI agent workflows or similar agent-based systems. Experience integrating and working with Large Language Models (LLMs) like Gemini, GPT, or similar. Familiarity with developing systems that involve LLM-generated code and its secure execution (e.g., using Docker, E2B, or other sandboxing technologies). Understanding of chatbot development principles and conversational AI. Experience in a startup environment or working on novel AI applications. Bonus Points: Familiarity with frontend technologies like ReactJS. Experience with cloud platforms (e.g., GCP, AWS, Azure) and deploying AI/ML models. Our Culture & What We Value: Building an inclusive, collaborative, and high-performing culture. Humility: Embrace learning and are open to growth. Open to Feedback: Constructive criticism is welcomed for improvement. Accountability: Encourage ownership of work and responsibility for outcomes. Collaborative Problem Solving: Focus on "us versus the problem." Agile Mindset: Managers and team members work together collaboratively. Transparency: Open environment where information is accessible. Approachability: Promote direct communication and understanding of company goals. Deeply committed to building a diverse team and an inclusive environment. Why Join Us? Impact: Play a foundational role in an early-stage AI startup. Challenge: Work on intellectually stimulating problems at the cutting edge of AI and software engineering. Growth: Unparalleled opportunity for learning and professional development. Collaboration: Be part of a supportive, humble, and collaborative team. Flexibility: Enjoy the benefits of a remote work environment. Interested? If you're a passionate backend engineer ready to dive into complex AI challenges and contribute to a groundbreaking platform, we'd love 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: Batlabs AI is redefining how engineers understand complex materials data using LLMs built for science 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!