1069 Llm Jobs - Page 38

We are seeking a Principal Machine Learning Engineer with deep expertise in Natural Language Processing (NLP), Large Language Models (LLMs), and advanced deep learning techniques. This role demands a visionary leader who can architect, scale, and deliver large-scale machine learning systems with a focus on business outcomes, high reliability, and cutting-edge innovation. You will lead the strategic development of ML systems, optimizing for both performance and business impact, while architecting large-scale, production-grade AI solutions. Key Responsibilities: End-to-End LLM System Architecture: Design and lead the development of advanced LLM-based systems, using models such as LLaMA, Mistral, and Azure OpenAI. Architect these solutions to support business objectives at scale, focusing on optimizing performance, resource utilization, and reliability across diverse environments. Scaling AI Solutions: Lead efforts to scale machine learning solutions across enterprise-wide applications. Establish methodologies to manage increasingly complex data pipelines, model lifecycle management, and deployable architectures, ensuring fault tolerance and minimal downtime. High-Impact ML Deployment: Drive large-scale deployment of LLMs and other ML models in cloud and hybrid environments (AWS, GCP, Azure). Build highly scalable, secure, and reliable AI infrastructure, ensuring systems meet stringent SLAs and production quality standards. Deep Learning System Optimization: Architect and optimize deep learning systems, enhancing performance using techniques such as distributed training, model pruning, quantization, and hyperparameter tuning. Drive the development of tools and frameworks that improve deployment speed and system efficiency. Business Alignment: Lead the alignment of ML solutions with business strategies, focusing on maximizing ROI from AI investments. Translate business problems into technical solutions, ensuring that AI systems deliver measurable value. AI Innovation Leadership: Spearhead the exploration of new ML models, techniques, and platforms, driving the next generation of AI applications. Lead advanced research in multimodal LLMs, self-supervised learning, and model interpretability to create cutting-edge business solutions. Cross-Functional Leadership & Collaboration: Mentor and guide AI engineers and machine learning scientists, fostering a culture of collaboration and technical excellence. Build strong relationships with product teams, data engineering, and operations to deliver seamless AI solutions across the enterprise. System Reliability and Optimization: Ensure high system availability by implementing monitoring, logging, and alerting strategies. Architect scalable and highly available systems capable of processing large volumes of data in real time. ML Governance and Compliance: Oversee the governance of AI systems, ensuring ethical deployment and compliance with data privacy regulations. Implement processes to ensure model auditability, explainability, and bias mitigation. Technical Expertise: Advanced NLP & LLMs: Deep expertise in building, fine-tuning, and deploying large language models such as the LLaMA and Mistral families, including advanced knowledge of techniques like reinforcement learning, model distillation, and transfer learning. Distributed Systems & Parallelization: Expert knowledge of distributed systems and parallelized model training, enabling large-scale deployment and training of models across multi-cloud infrastructures. Deep Learning Frameworks: Advanced experience with PyTorch, TensorFlow, and other ML libraries. Strong software engineering and system design skills, including proficiency with FastAPI, test-driven development (TDD), and CI/CD pipelines. Cloud and Infrastructure Expertise: Experience architecting fault-tolerant, distributed machine learning systems across multi-cloud platforms. Expertise in Kubernetes, Docker, and other containerization technologies for large-scale deployments. Must-Have Skills Excellent communication skills to articulate complex technical details to non-technical stakeholders. Excellent team player and ability to guide, nurture other members of the team In-depth understanding of architecting scalable and reliable solutions Willing to work from Office Nice to haves: Multimodal and Vision Models: Experience in deploying multimodal AI solutions that integrate text, images, video, and audio into cohesive applications for cross-domain problems. AI in Production: Proven track record of managing large-scale ML systems in production, optimizing for uptime, performance, and cost-efficiency across global infrastructures. Experience: 8+ years with a Bachelors degree, 6+ years (Master s), or 4+ years (PhD) preferably in Computer science/Data Engineering

AI/ML Engineer We are looking for versatile and experienced technical additions to our development team in Gandhinagar. The position offers an extensive amount of ownership and influence over our development process as we scale the team. We re looking for people who enjoy solving meaningful problems and love seeing the things they build in the hands of real users. Responsibilities: Design and develop architecture for ML models to be implemented in dockerized containers with CI & CD Develop the models for various use cases related to search, text classification and entity recognition Develop search engine for various use cases with automated pipeline of indexing Dataset processing for the training of the pre-trained models and for new models Come up with innovative ideas to improvise the product API development for all the model related functions like dataset preparation, training, deployment, retraining, etc. Expectations: Hands-on experience with Unix systems Hands-on experience with transformer-based models Working knowledge of Elastic search Experience working with generative models like LLM Has developed models for various use cases of search, text classification, and entity recognition Hands-on Experience with CI & CD frameworks like Jenkins. and working knowledge in Maven. Hands-on Experience in building docker images, docker deployments, private repository configuration, container configurations, container orchestration using Kubernetes or Docker Swarm Experience with unit testing frameworks and integration with CI technologies Expert in NLP, NER, and NLU Hands-on experience with BERT model Skilled in Fast API, Flask, Django, MariaDB, MongoDB Experience with various AI frameworks and tools like Pytorch, Spacy, Tensorflow, RasaNLU, SKLearn, etc Has used Naive-Bayes, KNN, CART, Linear Regression in various models. Working knowledge of Azure Cognitive Services like Azure OCR, Azure entity recognition, etc.

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