8 Langfuse Jobs

You are a Senior AI Developer specializing in Backend and Autonomous Systems, with a strong focus on Large Language Models (LLMs). In this role, you will be a part of a team that creates autonomous AI systems for software enterprise management. These systems utilize LLM agents for various tasks such as idea generation, market research, software development, and customer acquisition. Your responsibilities will include developing automated systems for code generation and management, designing AI agents capable of understanding business states to facilitate decision-making, contributing to the development of new architectures and programming languages tailored for AI-driven development, and implementing systems for storing and analyzing business states to support AI decision-making processes. To excel in this role, you should hold a Bachelor's degree in Computer Science & Engineering, possess over 5 years of experience in software development with a focus on backend and AI technologies, demonstrate strong architectural and software design skills, be proficient in Python and FastAPI, have hands-on experience with AI code generation tools like Cursor, be familiar with AI agent systems such as AutoGen, possess a deep understanding of LLMs and their applications in software development, have experience in finetuning LLM models, and additional experience with LangFuse, LangChain, and AWS would be advantageous.,

We are hiring an experienced AI Engineer / ML Specialist with deep expertise in Large Language Models (LLMs), who can fine-tune, customize, and integrate state-of-the-art models like OpenAI GPT, Claude, LLaMA, Mistral, and Gemini into real-world business applications. The ideal candidate should have hands-on experience with foundation model customization, prompt engineering, retrieval-augmented generation (RAG), and deployment of AI assistants using public cloud AI platforms like Azure OpenAI, Amazon Bedrock, Google Vertex AI, or Anthropics Claude. Key Responsibilities: LLM Customization & Fine-Tuning Fine-tune popular open-source LLMs (e.g., LLaMA, Mistral, Falcon, Mixtral) using business/domain-specific data. Customize foundation models via instruction tuning, parameter-efficient fine-tuning (LoRA, QLoRA, PEFT), or prompt tuning. Evaluate and optimize the performance, factual accuracy, and tone of LLM responses. AI Assistant Development Build and integrate AI assistants/chatbots for internal tools or customer-facing applications. Design and implement Retrieval-Augmented Generation (RAG) pipelines using tools like LangChain, LlamaIndex, Haystack, or OpenAI Assistants API. Use embedding models, vector databases (e.g., Pinecone, FAISS, Weaviate, ChromaDB), and cloud AI services. Must have experience of finetuning, and maintaining microservices or LLM driven databases. Cloud Integration Deploy and manage LLM-based solutions on AWS Bedrock, Azure OpenAI, Google Vertex AI, Anthropic Claude, or OpenAI API. Optimize API usage, performance, latency, and cost. Secure integrations with identity/auth systems (OAuth2, API keys) and logging/monitoring. Evaluation, Guardrails & Compliance Implement guardrails, content moderation, and RLHF techniques to ensure safe and useful outputs. Benchmark models using human evaluation and standard metrics (e.g., BLEU, ROUGE, perplexity). Ensure compliance with privacy, IP, and data governance requirements. Collaboration & Documentation Work closely with product, engineering, and data teams to scope and build AI-based solutions. Document custom model behaviors, API usage patterns, prompts, and datasets. Stay up-to-date with the latest LLM research and tooling advancements. Required Skills & Qualifications: Bachelors or Masters in Computer Science, AI/ML, Data Science, or related fields. 3-6+ years of experience in AI/ML, with a focus on LLMs, NLP, and GenAI systems. Strong Python programming skills and experience with Hugging Face Transformers, LangChain, LlamaIndex. Hands-on with LLM APIs from OpenAI, Azure, AWS Bedrock, Google Vertex AI, Claude, Cohere, etc. Knowledge of PEFT techniques like LoRA, QLoRA, Prompt Tuning, Adapters. Familiarity with vector databases and document embedding pipelines. Experience deploying LLM-based apps using FastAPI, Flask, Docker, and cloud services. Preferred Skills: Experience with open-source LLMs: Mistral, LLaMA, GPT-J, Falcon, Vicuna, etc. Knowledge of AutoGPT, CrewAI, Agentic workflows, or multi-agent LLM orchestration. Experience with multi-turn conversation modeling, dialogue state tracking. Understanding of model quantization, distillation, or fine-tuning in low-resource environments. Familiarity with ethical AI practices, hallucination mitigation, and user alignment. Tools & Technologies: Category Tools & Platforms LLM Frameworks Hugging Face, Transformers, PEFT, LangChain, LlamaIndex, Haystack LLMs & APIs OpenAI (GPT-4, GPT-3.5), Claude, Mistral, LLaMA, Cohere, Gemini, Azure OpenAI Vector Databases FAISS, Pinecone, Weaviate, ChromaDB Serving & DevOps Docker, FastAPI, Flask, GitHub Actions, Kubernetes Deployment Platforms AWS Bedrock, Azure ML, GCP Vertex AI, Lambda, Streamlit Monitoring Prometheus, MLflow, Langfuse, Weights & Biases

Job Title: AI/ML Architect GenAI, LLMs & Enterprise Automation Location: Bangalore Experience: 8+ years (including 4+ years in AI/ML architecture on cloud platforms) Role Summary We are seeking an experienced AI/ML Architect to define and lead the design, development, and scaling of GenAI-driven solutions across our learning and enterprise platforms. This is a senior technical leadership role where you will work closely with the CTO and product leadership to architect intelligent systems powered by LLMs, RAG pipelines, and multi-agent orchestration. You will own the AI solution architecture end-to-endfrom model selection and training frameworks to infrastructure, automation, and observability. The ideal candidate will have deep expertise in GenAI systems and a strong grasp of production-grade deployment practices across the stack. Must-Have Skills AI/ML solution architecture experience with production-grade systems Strong background in LLM fine-tuning (SFT, LoRA, PEFT) and RAG frameworks Experience with vector databases (FAISS, Pinecone) and embedding generation Proficiency in LangChain, LangGraph , LangFlow, and prompt engineering Deep cloud experience (AWS: Bedrock, ECS, Lambda, S3, IAM) Infra automation using Terraform, CI/CD via GitHub Actions or CodePipeline Backend API architecture using FastAPI or Node.js Monitoring & observability using Langfuse, LangWatch, OpenTelemetry Python, Bash scripting, and low-code/no-code tools (e.g., n8n) Bonus Skills Hands-on with multi-agent orchestration frameworks (CrewAI, AutoGen) Experience integrating AI/chatbots into web, mobile, or LMS platforms Familiarity with enterprise security, data governance, and compliance frameworks Exposure to real-time analytics and event-driven architecture Youll Be Responsible For Defining the AI/ML architecture strategy and roadmap Leading design and development of GenAI-powered products and services Architecting scalable, modular, and automated AI systems Driving experimentation with new models, APIs, and frameworks Ensuring robust integration between model, infra, and app layers Providing technical guidance and mentorship to engineering teams Enabling production-grade performance, monitoring, and governance

You will be responsible for building curated enterprise-grade solutions for GenAI application deployment at a production scale for clients. This role demands a solid understanding and hands-on skills in GenAI application deployment, encompassing development and engineering skills. You will need to possess expertise in data ingestion, selecting the appropriate LLMs, implementing simple and advanced RAG, guardrails, prompt engineering for optimization, traceability, security, LLM evaluation, observability, and deployment at scale on cloud or on-premise. It is essential for candidates to demonstrate knowledge of agentic AI frameworks due to the rapid evolution of this space. Strong background in ML with engineering skills is highly preferred for the LLMOps role. You should have 3 - 5 years of experience working on ML projects, involving business requirement gathering, model development, training, deployment at scale, and monitoring model performance for production use cases. Proficiency in Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, AgentOps (optional) is crucial. Experience with proprietary and open-source large language models, LLM fine-tuning, creating distilled models from hosted LLMs, and building data pipelines for model training is required. You should also have experience in model performance tuning, RAG, guardrails, prompt engineering, evaluation, and observability. Prior experience in GenAI application deployment on cloud and on-premises at scale for production, creating CI/CD pipelines, working with Kubernetes, and deploying AI services on at least one cloud platform such as AWS, GCP, or Azure is necessary. Proficiency in creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen, and light-weight UI development using streamlit or chainlit (optional) is beneficial. Desired experience with open-source tools for ML development, deployment, observability, and integration is an added advantage. A background in DevOps and MLOps will be a plus. You should be familiar with collaborative code versioning tools like GitHub/GitLab and possess excellent communication and presentation skills. A degree in Computer Science, related technical field, or equivalent is required. If you are someone who thrives in a dynamic environment and enjoys collaborating with enthusiastic individuals, this opportunity is perfect for you.,

You will be responsible for building curated enterprise-grade solutions for GenAI application deployment at a production scale for clients. Your role will involve a solid understanding and hands-on skills for GenAI application deployment, which includes development and engineering tasks. This will include data ingestion, selecting suitable LLMs, implementing simple and advanced RAG, setting up guardrails, prompt engineering for optimization, ensuring traceability and security, evaluating LLMs, enabling observability, and deploying at scale on the cloud or on-premise. It is crucial that candidates also showcase knowledge on agentic AI frameworks, with a preference for those having a strong background in ML with engineering skills for the LLMOps role. The ideal candidate should possess 3 - 5 years of experience in working on ML projects, encompassing tasks such as business requirement gathering, model development, training, deployment at scale, and monitoring model performance for production use cases. Proficiency in Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, and optionally AgentOps is essential. Prior experience working with both proprietary and open-source large language models, fine-tuning LLMs, creating distilled models from hosted LLMs, building data pipelines for model training, and tuning model performance, RAG, guardrails, prompt engineering, evaluation, and observability is required. Moreover, candidates should have experience in GenAI application deployment on cloud and on-premises at scale for production, creating CI/CD pipelines, working with Kubernetes, deploying AI services on at least one cloud platform (AWS/GCP/Azure), creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen, and optionally developing lightweight UI using streamlit or chainlit. Desired experience with open-source tools for ML development, deployment, observability, and integration, as well as a background in DevOps and MLOps, will be advantageous. Proficiency in collaborative code versioning tools such as GitHub/GitLab, along with strong communication and presentation skills, is essential. A B.E/B.Tech/M.Tech in Computer Science or a related technical degree or equivalent qualification is required. If you are someone who enjoys challenging growth opportunities and thrives in a dynamic environment working alongside enthusiastic over-achievers, this role might be the perfect fit for you.,

Required Qualifications 8+ years of experience in DevOps, SRE, or similar roles, with at least 1 year specifically working with LLMs or AI systems in production Strong hands-on experience with AWS cloud services, particularly Bedrock, Lambda, SQS, API Gateway, OpenSearch, and CloudWatch Experience with infrastructure-as-code using Terraform, CloudFormation, or similar tools Proficiency in Python and experience building automation tooling and pipelines Familiarity with LangOps platforms such as Langfuse for LLM observability and evaluation Experience with CI/CD pipelines Knowledge of logging, monitoring, and alerting systems Understanding of security best practices for AI systems, including prompt injection mitigation techniques Excellent troubleshooting and problem-solving skills Strong communication skills and ability to work effectively with cross-functional teams Must be legally entitled to work in the country where the role is located Preferred Qualifications Experience with prompt engineering and testing tools like Promptfoo Familiarity with vector databases and retrieval-augmented generation (RAG) systems Knowledge of serverless architectures and event-driven systems Experience with AWS Guardrails for LLM security Background in data engineering or machine learning operations Understanding of financial systems and data security requirements in the finance industry Familiarity with implementing technical solutions to meet compliance requirements outlined in SOC2, ISAE 3402, and ISO 27001

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.

Senior Software Engineer/ LLM Ops Engineer External Description Description - External JD - What You Will Do Design, implement, and maintain LLM operations workflows using tools like Langfuse to monitor performance, track usage, and create feedback loops for continuous improvement Develop and maintain infrastructure-as-code for AI deployments using Terraform and AWS services (Lambda, SQS, API Gateway, OpenSearch, CloudWatch) Build and enhance monitoring, logging, and alerting systems to ensure optimal performance and reliability of our LLM infrastructure Collaborate with AI engineers to design and implement evaluation frameworks (including LLM-as-judge systems) to measure and improve model performance Manage prompt versioning, testing, and deployment pipelines through CI/CD and custom tooling Implement and maintain security guardrails for LLM interactions, ensuring compliance with best practices Create comprehensive documentation for LLM operations, including runbooks for production incidents Participate in on-call rotations to support mission-critical AI systems Drive innovation in LLM operations by researching and implementing best practices and emerging tools in the rapidly evolving GenAI space Deep understanding of prompt engineering strategies What You Will Bring To succeed in this role, you will need a combination of experience, technology skills, personal qualities, and education. Required Qualifications 3+ years of experience in DevOps, SRE, or similar roles, with at least 1 year specifically working with LLMs or AI systems in production Strong hands-on experience with AWS cloud services, particularly Bedrock, Lambda, SQS, API Gateway, OpenSearch, and CloudWatch Experience with infrastructure-as-code using Terraform, CloudFormation, or similar tools Proficiency in Python and experience building automation tooling and pipelines Familiarity with LangOps platforms such as Langfuse for LLM observability and evaluation Experience with CI/CD pipelines Knowledge of logging, monitoring, and alerting systems Understanding of security best practices for AI systems, including prompt injection mitigation techniques Excellent troubleshooting and problem-solving skills Strong communication skills and ability to work effectively with cross-functional teams Must be legally entitled to work in the country where the role is located Preferred Qualifications Experience with prompt engineering and testing tools like Promptfoo Familiarity with vector databases and retrieval-augmented generation (RAG) systems Knowledge of serverless architectures and event-driven systems Experience with AWS Guardrails for LLM security Background in data engineering or machine learning operations Understanding of financial systems and data security requirements in the finance industry Familiarity with implementing technical solutions to meet compliance requirements outlined in SOC2, ISAE 3402, and ISO 27001