7 Peft Jobs

You should have 3 to 10 years of experience in AI development and be located in Coimbatore. Immediate joiners are preferred. A minimum of 2 years of experience in core Gen AI is required. As an AI Developer, your responsibilities will include designing, developing, and fine-tuning Large Language Models (LLMs) for various in-house applications. You will implement and optimize Retrieval-Augmented Generation (RAG) techniques to enhance AI response quality. Additionally, you will develop and deploy Agentic AI systems capable of autonomous decision-making and task execution. Building and managing data pipelines for processing, transforming, and feeding structured/unstructured data into AI models will be part of your role. It is essential to ensure scalability, performance, and security of AI-driven solutions in production environments. Collaboration with cross-functional teams, including data engineers, software developers, and product managers, is expected. You will conduct experiments and evaluations to improve AI system accuracy and efficiency while staying updated with the latest advancements in AI/ML research, open-source models, and industry best practices. You should have strong experience in LLM fine-tuning using frameworks like Hugging Face, DeepSpeed, or LoRA/PEFT. Hands-on experience with RAG architectures, including vector databases such as Pinecone, ChromaDB, Weaviate, OpenSearch, and FAISS, is required. Experience in building AI agents using LangChain, LangGraph, CrewAI, AutoGPT, or similar frameworks is preferred. Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow is necessary. Experience in Python web frameworks such as FastAPI, Django, or Flask is expected. You should also have experience in designing and managing data pipelines using tools like Apache Airflow, Kafka, or Spark. Knowledge of cloud platforms (AWS/GCP/Azure) and containerization technologies (Docker, Kubernetes) is essential. Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, Cohere, Llama, etc.) and their integration in applications is a plus. A strong understanding of vector search, embedding models, and hybrid retrieval techniques is required. Experience with optimizing inference and serving AI models in real-time production systems is beneficial. Experience with multi-modal AI (text, image, audio) and familiarity with privacy-preserving AI techniques and responsible AI frameworks are desirable. Understanding of MLOps best practices, including model versioning, monitoring, and deployment automation, is a plus. Skills required for this role include PyTorch, RAG architectures, OpenSearch, Weaviate, Docker, LLM fine-tuning, ChromaDB, Apache Airflow, LoRA, Python, hybrid retrieval techniques, Django, GCP, CrewAI, OpenAI, Hugging Face, Gen AI, Pinecone, FAISS, AWS, AutoGPT, embedding models, Flask, FastAPI, LLM APIs, DeepSpeed, vector search, PEFT, LangChain, Azure, Spark, Kubernetes, AI Gen, TensorFlow, real-time production systems, LangGraph, and Kafka.,

As an AI Specialist at our company based in Hyderabad, you will be responsible for training and fine-tuning LLMs such as LLaMA and Mistral to cater to company-specific use cases. You will play a vital role in customizing and optimizing model performance for seamless production deployment. Collaboration with internal teams for model integration and data pipelines will be a key aspect of your role. It is imperative that you stay abreast of the latest advancements in GenAI and LLM techniques to contribute effectively. To excel in this role, you must possess hands-on experience with LLMs and fine-tuning techniques. Your expertise in the specifics of vector database indexing will be highly beneficial. We are looking for someone with a robust background in advanced AI/ML techniques and database indexing, particularly in the context of production projects. Familiarity with technologies such as LoRA, QLoRA, RAG, and PEFT is desirable. Additionally, your knowledge of model evaluation, optimization, and GPU training will be crucial for success in this position.,

Job Summary: AI Engineer Product Integration (LLMs & ML Systems) We are seeking a hands-on AI Engineer with proven experience in building, fine-tuning, and deploying Large Language Models (LLMs) and Machine Learning (ML) systems in production. This is not a research role were looking for someone who can turn models into scalable features, deeply integrated within our Optimiser CRM platform. Youll lead initiatives that turn AI into real value: automating workflows, enriching CRM data, and enhancing decision- making across our client base. This role requires strong engineering fundamentals, model fluency, and the ability to build reliable AI solutions in a multi-tenant environment. Key Responsibilities: AI/ML Development Design and fine-tune LLMs and ML models for specific CRM workflows. Build and optimize data pipelines for training, inference, and evaluation. Implement retrieval-augmented generation (RAG) and context injection strategies. Production Integration Package and deploy models into scalable production systems. Develop APIs and microservices for AI-powered features. Monitor model behavior in production; use telemetry to drive continuous improvement. Architecture & Compliance Ensure AI features adhere to multi-tenant data isolation and privacy standards. Collaborate with product and backend teams to align AI systems with Optimisers architecture. Implement access control, audit logging, and performance monitoring for AI services. Qualifications 2+ years of experience shipping ML/LLM features in a production environment. Strong Python development skills and hands-on experience with ML/LLM libraries (e.g., PyTorch, TensorFlow, LangChain, Hugging Face). Practical understanding of vector search, embeddings, and retrieval systems (e.g., Pinecone, FAISS, Weaviate). Experience deploying models as part of SaaS applications, especially in privacy-sensitive environments. Clear understanding of the ML lifecycle: from development to monitoring and iteration. Preferred (Not Mandatory) Exposure to prompt engineering and model evaluation techniques. Experience with fine-tuning LLMs or working with tools like LoRA, PEFT, or RAG pipelines. Familiarity with agent-based orchestration (e.g., LangChain agents, semantic routers). Understanding of MLOps (model versioning, deployment automation, rollback strategies). Location: Location: Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad

Job Description Summary role description: Hiring for a Solution Architect for an InsurTech platform provider, Life and Health Insurance. Company description: Our client is a VC-funded InsurTech platform company, providing software platforms for Life Insurance and Health Insurance companies across the globe. Leveraging their domain expertise, regulatory knowledge and technology experience, they architect innovative products and disrupt the Insurance value chain from Customer Acquisition to Engagement. Their products serve customers across the APAC region. Role details: Title / Designation : Solutions Architect Location: Pune/Mumbai Work Mode: Work from office Role & responsibilities: Define and evolve AI/ML architecture roadmap for FWA, IDP, and Agentic AI frameworks. Lead technical presentations and solution design sessions with customers. Design scalable architectures for multi-agent systems and autonomous decision-making. Drive innovation by evaluating emerging AI/ML technologies, especially AI agents. Architect cloud-native platforms supporting the complete AI/ML lifecycle. Provide technical leadership across product development and customer implementation. Collaborate with data scientists, engineers, and business stakeholders. Stay at the forefront of AI/ML innovations, particularly autonomous agents and LLMs. Establish and enforce technical standards and architectural guidelines. Candidate requirements: 10+ years in software architecture/system design in insurance domain, with 5+ years in AI/ML systems/platforms. Proven experience delivering large-scale AI/ML solutions, preferably with autonomous agents. Experience with cloud-native architectures (AWS, Azure, GCP), containerization (Docker, Kubernetes), and microservices. Deep expertise in AI/ML system architecture (model serving, MLOps/LLMOps pipelines, distributed computing). Strong understanding of Agentic AI, multi-agent systems, and LLMs (including LoRA, PEFT fine-tuning). Bachelor's or Master's in CS, SE, Data Science, or related technical field. Exceptional technical leadership and communication skills. Selection process: Interview with Senior Solution Architect Interview with CTO HR Discussion Check Your Resume for Match Upload your resume and our tool will compare it to the requirements for this job like recruiters do.

As a Senior / Lead Data Scientist specializing in Generative AI and NLP, you will be at the forefront of AI innovation. Your role will involve designing and deploying sophisticated models, including large language models (LLMs), to solve complex business problems. You will work closely with cross-functional teams to create scalable, data-driven solutions that bring AI-driven creativity and intelligence to life across various industries. Generative AI & NLP Development : Design, develop, and deploy advanced applications and solutions using Generative AI models (e.g., GPT, LLaMA, Mistral) and NLP algorithms to solve business challenges and unlock new opportunities for our clients. Model Customization & Fine-Tuning : Apply state-of-the-art techniques like LoRA, PEFT, and fine-tuning of large language models to adapt solutions to specific use cases, ensuring high relevance and impact. Innovative Problem Solving: Leverage advanced AI methodologies to tackle real-world business problems, providing creative and scalable AI-powered solutions that drive measurable results. • Data-Driven Insights: Conduct deep analysis of large datasets, uncovering insights and trends that guide decisionmaking, improve operational efficiencies, and fuel innovation. Cross-Functional Collaboration: Work closely with Consulting, Engineering, and other teams to integrate AI solutions into broader business strategies, ensuring the seamless deployment of AI-powered applications. • Client Engagement: Collaborate with clients to understand their unique business needs, provide tailored AI solutions, and educate them on the potential of Generative AI to drive business transformation. Role & responsibilities Generative AI & NLP Expertise: Extensive experience in developing and deploying Generative AI applications and NLP frameworks, with hands-on knowledge of LLM fine-tuning, model customization, and AI-powered automation. Hands-On Data Science Experience: 6+ years of experience in data science, with a proven ability to build and operationalize machine learning and NLP models in real-world environments. AI Innovation: Deep knowledge of the latest developments in Generative AI and NLP, with a passion for experimenting with cutting-edge research and incorporating it into practical solutions. Problem-Solving Mindset: Strong analytical skills and a solution-oriented approach to applying data science techniques to complex business problems. Communication Skills: Exceptional ability to translate technical AI concepts into business insights and recommendations for non-technical stakeholders.

Job Title: AI/ML and Chatbot Lead Experience Level: 10+ Years (Lead/Architect level) Location: Remote Employment Type: Full-time No. of Positions: 1 Job Overview: We are seeking a visionary and hands-on AI/ML and Chatbot Lead to spearhead the design, development, and deployment of enterprise-wide Conversational and Generative AI solutions. This role will establish and scale our AI Lab function, define chatbot and multimodal AI strategies, and deliver intelligent automation solutions that enhance user engagement and operational efficiency. Key Responsibilities Define and lead the enterprise-wide strategy for Conversational AI, Multimodal AI, and Large Language Models (LLMs). Build an AI/Chatbot Lab , creating a roadmap and driving innovations across in-app, generative, and conversational AI. Architect scalable AI/ML systems including presentation, orchestration, AI, and data layers. Collaborate with business stakeholders to assess needs, conduct ROI analyses, and deliver impactful AI use cases. Identify and implement agentic AI capabilities and SaaS optimization opportunities. Deliver POCs, pilots, and MVPs owning the design, development, and deployment lifecycle. Lead, mentor, and scale a high-performing team of AI/ML engineers and chatbot developers . Build multi-turn, memory-aware conversations using frameworks like LangChain or Semantic Kernel . Integrate bots with platforms like Salesforce, NetSuite, Slack , and custom applications via APIs/webhooks. Implement and monitor chatbot KPIs using tools like Kibana , Grafana , and custom dashboards. Champion ethical AI , governance, and data privacy/security best practices. Must-Have Skills 10+ years in AI/ML; demonstrable success in chatbot, conversational AI , and generative AI implementations. Experience building and operationalizing an AI/Chatbot architecture framework used enterprise-wide. Expertise in: Python , LangChain, ElasticSearch, NLP (spaCy, NLTK, Hugging Face) LLMs (e.g., GPT, BERT), RAG, prompt engineering Chatbot platforms (Azure OpenAI, MS Bot Framework), CLU, CQA AI solution deployment and monitoring at scale Familiarity with: Machine learning algorithms, deep learning, reinforcement learning NLP techniques for NLU/NLG Cloud platforms ( AWS, Azure, GCP ), Docker , Kubernetes Vector DBs (Pinecone, Weaviate, Qdrant) Semantic search, knowledge graphs, intelligent document processing Strong grasp of AI governance , documentation, and compliance standards Excellent team leadership, communication, and documentation skills Good-to-Have Skills Experience with Glean , Perplexity.ai , Rasa , XGBoost Familiarity with Salesforce , NetSuite , and business domains like Customer Success Knowledge of RPA tools like UiPath and its AI Center Role & responsibilities Interested candidate can call at 7087707007

Senior Technical Architect Machine Learning Solutions We are looking for a Senior Technical Architect with deep expertise in Machine Learning (ML), Artificial Intelligence (AI) , and scalable ML system design . This role will focus on leading the end-to-end architecture of advanced ML-driven platforms, delivering impactful, production-grade AI solutions across the enterprise. Key Responsibilities Lead the architecture and design of enterprise-grade ML platforms , including data pipelines, model training pipelines, model inference services, and monitoring frameworks. Architect and optimize ML lifecycle management systems (MLOps) to support scalable, reproducible, and secure deployment of ML models in production. Design and implement retrieval-augmented generation (RAG) systems, vector databases , semantic search , and LLM orchestration frameworks (e.g., LangChain, Autogen). Define and enforce best practices in model development, versioning, CI/CD pipelines , model drift detection, retraining, and rollback mechanisms. Build robust pipelines for data ingestion, preprocessing, feature engineering , and model training at scale , using batch and real-time streaming architectures. Architect multi-modal ML solutions involving NLP, computer vision, time-series, or structured data use cases. Collaborate with data scientists, ML engineers, DevOps, and product teams to convert research prototypes into scalable production services . Implement observability for ML models including custom metrics, performance monitoring, and explainability (XAI) tooling. Evaluate and integrate third-party LLMs (e.g., OpenAI, Claude, Cohere) or open-source models (e.g., LLaMA, Mistral) as part of intelligent application design. Create architectural blueprints and reference implementations for LLM APIs, model hosting, fine-tuning, and embedding pipelines . Guide the selection of compute frameworks (GPUs, TPUs), model serving frameworks (e.g., TorchServe, Triton, BentoML) , and scalable inference strategies (batch, real-time, streaming). Drive AI governance and responsible AI practices including auditability, compliance, bias mitigation, and data protection. Stay up to date on the latest developments in ML frameworks, foundation models, model compression, distillation, and efficient inference . 14. Ability to coach and lead technical teams , fostering growth, knowledge sharing, and technical excellence in AI/ML domains. Experience managing the technical roadmap for AI-powered products , documentations ensuring timely delivery, performance optimization, and stakeholder alignment. Required Qualifications Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 8+ years of experience in software architecture , with 5+ years focused specifically on machine learning systems and 2 years in leading team. Proven expertise in designing and deploying ML systems at scale , across cloud and hybrid environments. Strong hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn). Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding models (e.g., SBERT, OpenAI, Cohere). Demonstrated proficiency in MLOps tools and platforms : MLflow, Kubeflow, SageMaker, Vertex AI, DataBricks, Airflow, etc. In-depth knowledge of cloud AI/ML services on AWS, Azure, or GCP – including certification(s) in one or more platforms. Experience with containerization and orchestration (Docker, Kubernetes) for model packaging and deployment. Ability to design LLM-based systems , including hybrid models (open-source + proprietary), fine-tuning strategies, and prompt engineering. Solid understanding of security, compliance , and AI risk management in ML deployments. Preferred Skills Experience with AutoML , hyperparameter tuning, model selection, and experiment tracking. Knowledge of LLM tuning techniques : LoRA, PEFT, quantization, distillation, and RLHF. Knowledge of privacy-preserving ML techniques , federated learning, and homomorphic encryption Familiarity with zero-shot, few-shot learning , and retrieval-enhanced inference pipelines. Contributions to open-source ML tools or libraries. Experience deploying AI copilots, agents, or assistants using orchestration frameworks.