20 Qdrant Jobs

As an AI/ML professional at Voxai, you will play a crucial role in architecting and implementing Large Language Model (LLM)-driven solutions that revolutionize customer experience in the contact center domain. Your responsibilities will include designing and optimizing Retrieval-Augmented Generation (RAG) pipelines, recommending AI/ML architectures, developing AI agents, experimenting with Agentic AI systems, and building MLOps pipelines. You will collaborate with cross-functional teams to deliver real-world AI applications, analyze contact center data to derive actionable insights, and mentor junior engineers. Your qualifications should include a strong foundation in Computer Science, proficiency in Statistics and Machine Learning, hands-on experience in deploying ML models, familiarity with LLMs and vector databases, and expertise in MLOps tools and cloud-native deployment. Additionally, excellent problem-solving, analytical, and communication skills are essential for this role. Ideally, you should hold a Masters or Ph.D. in Computer Science or a related field, have experience in enterprise software or customer experience platforms, and be able to work effectively in a fast-paced, collaborative environment. Join Voxai to be at the forefront of CX innovation and make a significant impact in the tech industry.,

As a Generative AI Architect with 5 to 10+ years of experience, you will be responsible for designing, developing, and deploying enterprise-grade GenAI solutions. Your role will require in-depth expertise in LLMs, RAG, MLOps, cloud platforms, and scalable AI architecture. You will architect and implement secure, scalable GenAI solutions using LLMs such as GPT, Claude, LLaMA, and Mistral. Additionally, you will build RAG pipelines with LangChain, LlamaIndex, FAISS, Weaviate, and ElasticSearch. Your responsibilities will also include leading prompt engineering, setting up evaluation frameworks for accuracy and safety, and developing reusable GenAI modules for function calling, summarization, document chat, and Q&A. Furthermore, you will deploy workloads on AWS Bedrock, Azure OpenAI, and GCP Vertex AI, ensuring monitoring and observability with Grafana, Prometheus, and OpenTelemetry. You will apply MLOps best practices such as CI/CD, model versioning, and rollback. Researching emerging trends like multi-agent systems, autonomous agents, and fine-tuning will also be part of your role, along with implementing data governance and compliance measures like PII masking, audit logs, and encryption. To be successful in this role, you should have 8+ years of experience in AI/ML, including 2-3 years specifically in LLMs/GenAI. Strong coding skills in Python with Hugging Face Transformers, LangChain, and OpenAI SDKs are essential. Expertise in Vector Databases like Pinecone, FAISS, Qdrant, and Weaviate is required, along with hands-on experience with cloud AI platforms such as AWS SageMaker/Bedrock, Azure OpenAI, and GCP Vertex AI. Experience in building RAG pipelines and chat-based applications, familiarity with agents and orchestration frameworks like LangGraph, AutoGen, and CrewAI, knowledge of MLOps stack including MLflow, Airflow, Docker, Kubernetes, and FastAPI, as well as understanding of prompt security and GenAI evaluation metrics like BERTScore, BLEU, and GPTScore are also important. Excellent communication and leadership skills for architecture discussions and mentoring are expected in this role.,

Senior AI Engineer ???? Mumbai | Hybrid About Quantanite Were a global CX and digital solutions partner that blends cutting-edge AI with the human touch. Headquartered in London and operating across 4 continents, our 2,000+ people help some of the worlds fastest-growing brands scale smarter, work faster, and deliver better serviceevery time. Were not your typical outsourcing company. At Quantanite, great service is built on two things: smart tech and smarter people. From proprietary AI platforms like MBIUS to our collaborative, people-first culture, we equip our teams with the freedom and tools to build game-changing solutions. If youre looking for a place where your AI engineering skills can shape real enterprise impact (not just POCs), youll feel at home here. The Role As a Senior AI Engineer , youll own the full lifecycle of AI solutionsfrom design to production. Youll be hands-on with multi-agent workflows, advanced knowledge retrieval, and LLM optimization to solve enterprise-scale problems. Expect to lead projects such as AI Copilots, Enterprise Search Systems, Summarization Engines, and Intelligent Automation Tools building robust, scalable solutions that move beyond prototypes into production. Youll also play a key role in mentoring junior engineers, setting engineering standards, and collaborating cross-functionally to ensure delivery at scale. What Youll Do Build and deploy agentic systems and multi-agent workflows using LangChain, LlamaIndex, OpenAI SDK (and beyond). Design RAG pipelines with vector DBs like FAISS, Pinecone, Weaviate, or Qdrant. Fine-tune and optimize LLMs (GPT-4, Claude, LLaMA, Mistral, Gemini) with LoRA/QLoRA, quantization, and other techniques. Implement guardrails, safe API usage, and PII protection for secure deployments. Monitor/evaluate agent performance with LangSmith, AgentOps , and similar tools. Ship real-world enterprise AI use casesCopilots, orchestration engines, intelligent search, and more. Mentor and review the work of junior AI engineers, spreading best practices. Work with product, data, and DevOps teams to bring AI solutions into production at scale. What Youll Bring Solid grounding in LLMs and GenAI (fine-tuning, prompt engineering, practical deployment). Proven track record with RAG pipelines and vector databases. Hands-on with agent frameworks (LangChain, LlamaIndex, etc.) and orchestration. Experience deploying/scaling models on clouds (AWS, Azure, HuggingFace). Strong coding ability in Python or similar. Familiarity with LLM Ops : monitoring, evaluation, cost optimisation. Delivered enterprise-grade AI solutions (not just demos). Experience mentoring engineers and building technical excellence in a team. Bonus: contributed to open-source LLM projects or worked on autonomous agent use cases (research copilots, orchestration systems). Qualifications B.Tech (required). 4.57 years of relevant experience. Strong knowledge of GenAI, agentic systems, and multi-agent architectures. Attributes: clear communicator, strong leader, highly tech-savvy. Preferred: exposure to BPO/KPO environments or systems experience in enterprise AI/agentic tools. Why Quantanite ???? Hybrid work model. ???? Ongoing training, mentorship & career growth. ???? Inclusive, people-first culture. ???? Health insurance & provident fund. Quantanite is an equal opportunity employer. We celebrate diversity and are committed to building an inclusive environment for all. ???? Ready to push agentic AI beyond theory into enterprise-scale impact Lets build the future together. Apply today to discover how we can build better, together. Show more Show less

As an AI Fullstack Engineer at Discvr.ai, you will be an integral part of our core team, contributing to the design, development, and deployment of key features on our innovative AI search and research platform. Your primary focus will be on integrating and leveraging AI capabilities to enhance user experience and build efficient web search functionalities. Your responsibilities will include designing and maintaining responsive user interfaces using React/Next.js, developing robust backend services and APIs with Python, and managing various data stores such as MongoDB, Redis, Neo4j, and Qdrant. You will closely collaborate with AI models, integrate third-party APIs, and ensure the scalability, reliability, and security of the platform throughout all phases of the software development lifecycle. To excel in this role, you should have 3-6 years of professional experience in fullstack development, proficiency in JavaScript/TypeScript, React, and Next.js for frontend development, and solid expertise in Python for backend development. Hands-on experience with NoSQL databases, caching systems, and exposure to vector and graph databases is essential. Moreover, a demonstrable understanding of AI/ML concepts, experience with integrating AI APIs, and strong problem-solving skills are key qualifications we are seeking. In this demanding yet rewarding position within an early-stage startup, you will have the opportunity to work directly on core product features, collaborate with cutting-edge AI technology, and grow rapidly in a dynamic environment. The role offers a hybrid work or fully remote arrangement for the right candidate, based out of Gurgaon, India. At Discvr.ai, we are committed to diversity and creating an inclusive environment for all employees. Join us in revolutionizing the way information is accessed and insights are delivered through our AI search engine platform.,

About the Role We are seeking a skilled Full-Stack AI Engineer to design, build, and deploy advanced AI/ML solutions. You will work on Retrieval-Augmented Generation (RAG), semantic search optimization, and production-scale ML systems. Core Skills RAG implementation Vector database expertise Semantic search optimization Building production ML systems Technical Requirements Experience with LangChain/LlamaIndex Hands-on with Pinecone/Weaviate/Qdrant Knowledge of OpenAI, Sentence Transformers Strong coding in Python, NumPy Familiarity with FAISS and prompt engineering Document chunking and Redis/caching Show more Show less

As a Generative AI Lead, you will be responsible for spearheading the design, development, and implementation of cutting-edge GenAI solutions within enterprise-grade applications. Your role will encompass leveraging your expertise in Large Language Models (LLMs), prompt engineering, and scalable AI system architecture, coupled with hands-on experience in MLOps, cloud technologies, and data engineering. Your primary responsibilities will include designing and deploying scalable and secure GenAI solutions utilizing LLMs such as GPT, Claude, LLaMA, or Mistral. You will lead the architecture of Retrieval-Augmented Generation (RAG) pipelines using tools like LangChain, LlamaIndex, Weaviate, FAISS, or ElasticSearch. Additionally, you will be involved in prompt engineering, evaluation frameworks, and collaborating with cross-functional teams to integrate GenAI into existing workflows and applications. Moreover, you will develop reusable GenAI modules for various functions like summarization, Q&A bots, and document chat, while leveraging cloud-native platforms such as AWS Bedrock, Azure OpenAI, and Vertex AI for deployment and optimization. You will ensure robust monitoring and observability across GenAI deployments and apply MLOps practices for CI/CD, model versioning, validation, and research into emerging GenAI trends. To be successful in this role, you must possess at least 8 years of overall AI/ML experience, with a focus of at least 3 years on LLMs/GenAI. Strong programming skills in Python and proficiency in cloud platforms like AWS, Azure, and GCP are essential. You should also have experience in designing and deploying RAG pipelines, summarization engines, and chat-based applications, along with familiarity with MLOps tools and evaluation metrics for GenAI systems. Preferred qualifications include experience with fine-tuning open-source LLMs, knowledge of multi-modal AI, familiarity with domain-specific LLMs, and a track record of published work or contributions in the GenAI field. In summary, as a Generative AI Lead, you will play a pivotal role in driving innovation and excellence in the development and deployment of advanced GenAI solutions, making a significant impact on enterprise applications and workflows.,

You have 5+ years of backend or full-stack development experience, including a minimum of 3 years specializing in Generative and Agentic AI. Your expertise lies in creating APIs utilizing REST, GraphQL, and gRPC, emphasizing on performance, versioning, and security. Proficiency in Python is required, with additional knowledge in TypeScript/Node.js, Go, or Java. You should possess a deep understanding of LLM integration and orchestration (OpenAI, Claude, Gemini, Mistral, LLaMA, etc.). Moreover, you must have hands-on experience with frameworks like LangChain, LlamaIndex, CrewAI, and Autogen. Familiarity with vector search, semantic memory, and retrieval-based augmentation tools such as FAISS or Qdrant is preferred. A solid grasp of cloud infrastructure (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes) is also essential for this role.,

???? We&aposre Hiring: Artificial Intelligence Consultant! ???? We&aposre seeking a highly motivated and technically adept Artificial Intelligence Consultant to join our growing Artificial Intelligence and Business Transformation practice. This role is ideal for a strategic thinker with a strong blend of leadership, business consulting acumen, and technical expertise in Python, LLMs, Retrieval-Augmented Generation (RAG), and agentic systems. Experience Required: Minimum 6+ Years Location: Remote/ Work From Home Job Type: Contract to hire (1 Year /Renewable Contract) Notice Period: Immediate to 15 Days Max Mode of Interview: Virtual Roles And Responsibilities AI Engagements: Independently manage end-to-end delivery of AI-led transformation projects across industries, ensuring value realization and high client satisfaction. Strategic Consulting & Roadmapping: Identify key enterprise challenges and translate them into AI solution opportunities, crafting transformation roadmaps that leverage RAG, LLMs, and intelligent agent frameworks. LLM/RAG Solution Design & Implementation: Architect and deliver cutting-edge AI systems using Python, LangChain, LlamaIndex, OpenAI function calling, semantic search, and vector store integrations (FAISS, Qdrant, Pinecone, ChromaDB). Agentic Systems: Design and deploy multi-step agent workflows using frameworks like CrewAI, LangGraph, AutoGen or ReAct, optimizing tool-augmented reasoning pipelines. Client Engagement & Advisory: Build lasting client relationships as a trusted AI advisor, delivering technical insight and strategic direction on generative AI initiatives. Hands-on Prototyping: Rapidly prototype PoCs using Python and modern ML/LLM stacks to demonstrate feasibility and business impact. Thought Leadership: Conduct market research, stay updated with the latest in GenAI and RAG/Agentic systems, and contribute to whitepapers, blogs, and new offerings. Essential Skills Education : Bachelor&aposs or Masters in Computer Science, AI, Engineering, or related field. Experience : Minimum 6 years of experience in consulting or technology roles, with at least 3 years focused on AI & ML solutions. Leadership Quality: Proven track record in leading cross-functional teams and delivering enterprise-grade AI projects with tangible business impact. Business Consulting Mindset: Strong problem-solving, stakeholder communication, and business analysis skills to bridge technical and business domains. Python & AI Proficiency: Advanced proficiency in Python and popular AI/ML libraries (e.g., scikit-learn, PyTorch, TensorFlow, spaCy, NLTK). Solid understanding of NLP, embeddings, semantic search, and transformer models. LLM Ecosystem Fluency: Experience with OpenAI, Cohere, Hugging Face models; prompt engineering; tool/function calling; and structured task orchestration. Independent Contributor: Ability to own initiatives end-to-end, take decisions independently, and operate in fast-paced environments. Preferred Skills Cloud Platform Expertise: Strong familiarity with Microsoft Azure (preferred), AWS, or GCP including compute instances, storage, managed services, and serverless/cloud-native deployment models. Programming Paradigms: Hands-on experience with both functional and object-oriented programming in AI system design. Hugging Face Ecosystem: Proficiency in using Hugging Face Transformers, Datasets, and Model Hub. Vector Store Experience: Hands-on experience with FAISS, Qdrant, Pinecone, ChromaDB. LangChain Expertise: Strong proficiency in LangChain for agentic task orchestration and RAG pipelines. MLOps & Deployment: CI/CD for ML pipelines, MLOps tools (MLflow, Azure ML), containerization (Docker/Kubernetes). Cloud & Service Architecture: Knowledge of microservices, scaling strategies, inter-service communication. Programming Languages: Proficiency in Python and C# for enterprise-grade AI solution development. Show more Show less

You will be part of a team that is building an Agentic AI Platform aimed at enabling enterprises to solve real business problems using Agentic AI workflows. The platform covers a wide range of areas from utility operations to legal document review. The ultimate goal is to empower AI agents to think, act, and deliver quickly, securely, and locally. As an Agentic AI Engineer, you will have the opportunity to work with cutting-edge frameworks such as Lang Chain, CrewAI, Lang Graph, Google ADK, and more. Your role will involve translating real enterprise challenges into intelligent multi-agent workflows. Your responsibilities will include building and deploying AI agents using open-source agentic frameworks, integrating models from sources like OpenAI, Mistral, Gemini, Llama, Claude, among others, and utilizing tools such as Retrieval-Augmented Generation (RAG), knowledge graphs, and vector stores. Collaboration with product managers and domain experts to address real problems in various enterprise domains like utilities, legal, marketing, and supply chain will be a key aspect of your role. Additionally, you will play a significant role in continuously testing and refining agent behavior and contributing to the enhancement of the proprietary DataInsightAI platform. To excel in this role, you should possess at least 1+ years of hands-on experience in implementing enterprise-level Gen AI projects that have been successfully deployed. Strong Python skills, familiarity with LLMs, agentic AI workflows, RAG, vector databases, and the ability to simplify complex problems are essential. A curiosity-driven mindset, a fast learner, and hands-on coding abilities are also crucial for success in this role. While not mandatory, experience in multi-agent architectures, graph databases like Neo4j, deployment on cloud platforms such as Azure/AWS/GCP, and familiarity with LangGraph or Google ADK are considered advantageous. Joining our team will offer you the opportunity to work on cutting-edge agentic AI projects daily, be part of a small team with significant ownership, ship solutions rapidly, and tackle real enterprise challenges. Moreover, you will be supported by InTimeTec, a company with a strong AI/ML engineering culture.,

You will be responsible for developing and maintaining high-performance server-side applications in Python following SOLID design principles. You will design, build, and optimize low-latency, scalable applications and integrate user-facing elements with server-side logic via RESTful APIs. Maintaining ETL and Data pipelines, implementing secure data handling protocols, and managing authentication and authorization across systems will be crucial aspects of your role. Additionally, you will ensure security measures and setup efficient deployment practices using Docker and Kubernetes. Leveraging caching solutions for enhanced performance and scalability will also be part of your responsibilities. To excel in this role, you should have strong experience in Python and proficiency in at least one Python web framework such as FastAPI or Flask. Familiarity with ORM libraries, asynchronous programming, event-driven architecture, and messaging tools like Apache Kafka or RabbitMQ is required. Experience with NoSQL and Vector databases, Docker, Kubernetes, and caching tools like Redis will be beneficial. Additionally, you should possess strong unit testing and debugging skills and the ability to utilize Monitoring and Logging frameworks effectively. You should have a minimum of 1.5 years of professional experience in backend development roles with Python. Your expertise in setting up efficient deployment practices, handling data securely, and optimizing application performance will be essential for success in this position.,

You are seeking a hands-on backend expert to elevate your FastAPI-based platform to the next level by developing production-grade model-inference services, agentic AI workflows, and seamless integration with third-party LLMs and NLP tooling. In this role, you will be responsible for various key areas: 1. Core Backend Enhancements: - Building APIs - Strengthening security with OAuth2/JWT, rate-limiting, SecretManager, and enhancing observability through structured logging and tracing - Adding CI/CD, test automation, health checks, and SLO dashboards 2. Awesome UI Interfaces: - Developing UI interfaces using React.js/Next.js, Redact/Context, and various CSS frameworks like Tailwind, MUI, Custom-CSS, and Shadcn 3. LLM & Agentic Services: - Designing micro/mini-services to host and route to platforms such as OpenAI, Anthropic, local HF models, embeddings & RAG pipelines - Implementing autonomous/recursive agents that orchestrate multi-step chains for Tools, Memory, and Planning 4. Model-Inference Infrastructure: - Setting up GPU/CPU inference servers behind an API gateway - Optimizing throughput with techniques like batching, streaming, quantization, and caching using tools like Redis and pgvector 5. NLP & Data Services: - Managing the NLP stack with Transformers for classification, extraction, and embedding generation - Building data pipelines to combine aggregated business metrics with model telemetry for analytics You will be working with a tech stack that includes Python, FastAPI, Starlette, Pydantic, Async SQLAlchemy, Postgres, Docker, Kubernetes, AWS/GCP, Redis, RabbitMQ, Celery, Prometheus, Grafana, OpenTelemetry, and more. Experience in building production Python REST APIs, SQL schema design in Postgres, async patterns & concurrency, UI application development, RAG, LLM/embedding workflows, cloud container orchestration, and CI/CD pipelines is essential for this role. Additionally, experience with streaming protocols, NGINX Ingress, SaaS security hardening, data privacy, event-sourced data models, and other related technologies would be advantageous. This role offers the opportunity to work on evolving products, tackle real challenges, and lead the scaling of AI services while working closely with the founder to shape the future of the platform. If you are looking for meaningful ownership and the chance to solve forward-looking problems, this role could be the right fit for you.,

About Us We are a cutting-edge AI innovation company developing intelligent agents that transform the way businesses operate. Our mission is to push the boundaries of what's possible with large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration. We work with top-tier clients across industries to deliver agentic systems that solve real-world business problems with speed, scale, and intelligence. Role Overview Were looking for an AI Agent Engineer to design, build, and refine advanced autonomous agents that operate across complex environments. Youll work at the intersection of technical architecture, AI-driven solution consulting, and system designcollaborating closely with business leaders, developers, and product teams to create production-ready, intelligent systems. This is an ideal role for engineers who are passionate about the future of AI, thrive on experimentation, and want to shape the next generation of automation through LLMs and multi-agent workflows. Key Responsibilities Core Engineering Architect and implement end-to-end AI agents using LangGraph, AutoGen, CrewAI, and other multi-agent frameworks (e.g., MCP, A2A). Design, iterate, and optimize prompts to support reliable and accurate agent performance in business-critical use cases. Integrate leading LLMs (GPT-4, Claude, Gemini, open-source alternatives) into real-time workflows and internal systems. Implement RAG architectures using vector databases such as Qdrant, Chroma, or Weaviate to ground agent responses in relevant context. Connect agents to APIs, external tools, document stores, and operational platforms to support intelligent decision-making. Collaboration & Consulting Translate client and internal requirements into AI-first system designs and architecture. Consult with product managers, sales engineers, and data teams to align AI solutions with business priorities and feasibility. Support implementation through technical documentation, design reviews, and hands-on problem-solving. Innovation & Enablement Lead test-and-learn initiatives and proof-of-concepts to validate agent performance and business value. Stay current on rapid developments in the LLM and multi-agent ecosystem and drive adoption of new capabilities. Contribute to the internal AI platform with tools, patterns, and reusable components to accelerate development. Provide training and support to technical and non-technical stakeholders to drive adoption and governance. Qualifications Must-Have: 3+ years of experience in AI/ML engineering or intelligent system development. Strong Python programming skills, with hands-on experience in prompt engineering and LLM workflows. Experience with frameworks such as LangGraph, CrewAI, AutoGen, LangChain, or similar agent development tools. Proficiency in implementing RAG architectures and working with vector databases (e.g., Qdrant, Chroma, Weaviate). Integration experience with APIs, databases, and frontend or workflow tools. Demonstrated success in consulting, technical sales, or AI solution architecture. Awareness of AI safety, compliance, and responsible development practices. Nice-to-Have: Familiarity with orchestration tools like n8n, Replit, or low-code AI automation platforms. Experience in enterprise domains such as insurance, healthcare, legal tech, or customer service. Exposure to multi-modal systems (text + vision) or knowledge graphs. MLOps or AI infrastructure experience in cloud environments (AWS, Azure, GCP Youll Thrive In This Role If You: Are energized by building systems that operate autonomously and adaptively in real-world scenarios. Can quickly move from ideation to implementation with a test-and-learn mindset. Stay at the forefront of LLM advancements and understand how to apply them to business problems. Communicate effectively across disciplines and help bridge product, engineering, and customer value. Thrive in a fast-paced, experimental environment that balances deep technical rigor with user impact. Why Join Us Work on frontier problems in AI agent design and autonomous systems. Collaborate with top-tier clients and industry-leading experts. Flexible work culture built around autonomy, innovation, and continuous learning. Competitive compensation and opportunities for high-impact contributions.

Role & Responsibility Develop and maintain microservice architecture and API management solutions using REST and gRPC for seamless deployment of AI solutions. • Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization. • Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models. • Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements. • Familiarity with tools like Terraform, CloudFormation, and Pulumi for efficient infrastructure management. • Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub Actions, Jenkins) to ensure streamlined development workflows. • Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments. • Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. • Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development. • Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. • Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs. • Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling. • Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions. • Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function. • Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment. Technical Skills: • Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow) • Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques • Experience with big data processing using Spark for large-scale data analytics • Version control and experiment tracking using Git and MLflow • Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. • DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. • LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. • MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. • Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems. • LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. • General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. • Experience in creating LLD for the provided architecture. • Experience working in microservices based architecture.

Roles and Responsibilities Develop and maintain Microservice architecture and API management solutions using REST and gRPC for seamless deployment of AI solutions. Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization. Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models. Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements. Familiarity with tools like Terraform, CloudFormation, and Pulumi for efficient infrastructure management. Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub Actions, Jenkins) to ensure streamlined development workflows. Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments. Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development. Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs. Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling. Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions. Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function. Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment. Technical Skills: Advanced proficiency in Python . Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques Experience with big data processing using Spark for large-scale data analytics Version control and experiment tracking using Git and MLflow Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems. LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. Experience in creating LLD for the provided architecture. Experience working in microservices based architecture. Domain Expertise: Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation Expertise in feature engineering, embedding optimization, and dimensionality reduction Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing Experience with RAG systems, vector databases, and semantic search implementation Proficiency in LLM optimization techniques including quantization and knowledge distillation Understanding of MLOps practices for model deployment and monitoring Professional Competencies: Strong analytical thinking with ability to solve complex ML challenges Excellent communication skills for presenting technical findings to diverse audiences Experience translating business requirements into data science solutions Project management skills for coordinating ML experiments and deployments Strong collaboration abilities for working with cross-functional teams Dedication to staying current with latest ML research and best practices Ability to mentor and share knowledge with team members

Infra as Code, CI/CD, system admin, coding, monitoring, security, and cross-team communication. Skills: Docker, K8s, ArgoCD, Ansible, Jenkins, AWS, Linux/macOS, Prometheus, DBs (SQL/NoSQL), Python, Git. Add GitHub/GitLab link in resume.

Tech stack required: Programming languages: Python Public Cloud: AzureFrameworks: Vector Databases such as Milvus, Qdrant/ ChromaDB, or usage of CosmosDB or MongoDB as Vector stores. Knowledge of AI Orchestration, AI evaluation and Observability Tools. Knowledge of Guardrails strategy for LLM. Knowledge on Arize or any other ML/LLM observability tool. Experience: Experience in building functional platforms using ML, CV, LLM platforms. Experience in evaluating and monitoring AI platforms in production Nice to have requirements to the candidate Excellent communication skills, both written and verbal. Strong problem-solving and critical-thinking abilities. Effective leadership and mentoring skills. Ability to collaborate with cross-functional teams and stakeholders. Strong attention to detail and a commitment to delivering high-quality solutions. Adaptability and willingness to learn new technologies. Time management and organizational skills to handle multiple projects and priorities.

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.

Senior Engineer (GenAI & Prompt Engineering) | Xebia We are looking for a highly experienced GenAI Engineer with deep expertise in Prompt Engineering, Retrieval-Augmented Generation (RAG), and Vector Search Systems to integrate GenAI into our engineering & DevOps ecosystem. This is a contractor role where you will design intelligent assistants & agents to augment CI/CD workflows, knowledge retrieval, incident handling, and developer productivity using LLMs, Python, and NLP pipelines . Key Responsibilities Design & optimize prompts for LLM workflows ensuring accuracy & relevance Build RAG pipelines using vector DBs (FAISS, Pinecone, Weaviate, Qdrant) Integrate LLMs into internal tools, CI/CD & observability dashboards Implement GenAI solutions using LangChain, LlamaIndex, Haystack, Hugging Face etc. Fine-tune open-source/commercial LLMs (OpenAI, Claude, Cohere, Mistral) for domain use cases Collaborate with DevOps & platform teams to drive automation with GenAI Ensure data privacy, governance & ethical AI practices Required Skills 6-10+ years in engineering, with 2+ years hands-on in GenAI/LLMs/NLP Strong Python skills with experience in LangChain, Hugging Face, LlamaIndex Deep knowledge of vector databases & embeddings Proven experience designing & deploying RAG architectures Experience integrating LLM APIs (OpenAI, Azure OpenAI, Claude, etc.) Nice to Have Experience with multi-modal models / fine-tuning LLMs Familiarity with developer-facing GenAI use cases : infra-as-code review, changelog generation, log triage Exposure to Kubernetes, GitOps, DevSecOps Work Location & Mode Any Xebia Location : Chennai, Bangalore, Hyderabad, Pune, Gurugram, Bhopal, Jaipur Hybrid model 3 days/week from office Important Only Immediate Joiners or max 2 weeks notice period will be considered How to Apply Send your profile to vijay.s@xebia.com with the subject line: Application Senior Engineer (GenAI & Prompt Engineering) Please share the following details along with your CV: Full Name Total Experience Current CTC Expected CTC Current Location Preferred Xebia Location (from above) Notice Period / Last Working Day (if serving) Primary Skills LinkedIn Profile

Were looking for a skilled Generative AI Developer to build agentic AI solutions using Dify.ai or similar platforms (LangChain, LlamaIndex, Haystack). Youll design and implement intelligent, autonomous AI agents and workflows that solve real business problems. Key Responsibilities Develop agentic AI workflows with Dify.ai, LangChain, or LlamaIndex Build and integrate autonomous AI agents and RAG pipelines with vector databases (Milvus, Pinecone, Qdrant, FAISS, Weaviate) Integrate LLMs (GPT, Claude, LLaMA, Mistral, Gemini, etc.) into business solutions Extend platforms via APIs, plugins, and custom backend logic (Python) Collaborate with product and engineering teams to deliver scalable AI applications Deploy solutions with Docker (Kubernetes a plus) Required Skills 4 to 9 years in software engineering or AI/ML development Practical experience with agentic AI workflows (Dify.ai, LangChain, LlamaIndex, Haystack, etc.) Strong Python skills; experience with vector databases & RAG Familiarity with React or TypeScript (for UI/workflows) API integration and deployment with Docker