16 Langfuse Jobs

About Zocket At Zocket, we are pioneering the future of marketing by harnessing the power of Artificial Intelligence to transform how businesses automate and scale their workflows. Our mission is bold yet simple: to streamline decision-making, dramatically reduce manual effort, and empower marketers to achieve superior results with minimal human intervention . This vision is powered by our cutting-edge Agentic AI systems next-generation intelligent agents designed not just to assist, but to autonomously execute complex marketing tasks at scale. About the Role: AI/ML Engineer As an AI/ML Engineer at Zocket , you will play a pivotal role in designing, building, and optimizing intelligent systems that power our agentic marketing platform. Youll work on challenging problems at the intersection of applied machine learning, generative AI, and large-scale data systems turning research concepts into production-ready solutions. You will: Develop, train, and fine-tune ML models for tasks such as campaign optimization, audience prediction, and creative generation. Build and scale AI agents that can reason, plan, and autonomously execute marketing workflows. Work with frameworks like LangGraph, LlamaIndex, and Semantic Kernel , alongside modern ML libraries (PyTorch, TensorFlow). Architect scalable data pipelines, integrate vector databases (Pinecone, Weaviate, Chroma) , and experiment with RAG (Retrieval-Augmented Generation). Deploy models and agents in production environments using cloud platforms (AWS/GCP/Azure) and observability tools (LangSmith, Langfuse). Collaborate with cross-functional teams (Product, Design, Marketing) to translate business needs into AI-first solutions. In this role, youll be at the forefront of building production-grade AI systems that redefine digital marketing efficiency turning complex ML advancements into tangible business outcomes . Show more Show less

Designation :Python Developer Experience: 4+ Years Candidate/ work Location : Bangalore (Hybrid) Interview Process 1 st Round : Online coding test 2 nd round : technical interview with Panel (Virtual) 3 rd Round : Face to Face (Mandatory ) Job Summary Strong core programming knowledge of Python 3.x. Hands-on experience with Django, Flask and FastAPI frameworks. Experience in developing and consuming REST APIs. Solid understanding of ORM (e.g., Django ORM or SQLAlchemy). Proficiency in working with databases like PostgreSQL, MySQL, or MongoDB. Working knowledge on LLMs and Langfuse. Familiarity with HTML, CSS, and JavaScript is a plus. Experience with version control systems (Git/GitHub). Good understanding of unit testing and debugging tools Show more Show less

About the Role We are seeking a Machine Learning Ops Engineer to support and scale our ML/AI infrastructure. The role involves working with LangServe, LangFuse, Docker, and Kubernetes to deploy, monitor, and optimize ML models in production environments. Key Responsibilities • Assist in designing and managing ML model deployment pipelines . • Work with LangServe to deploy and serve ML/LLM applications. • Implement monitoring and logging using LangFuse for model performance tracking. • Containerize ML applications with Docker and deploy them on Kubernetes clusters . • Collaborate with Data Scientists to integrate ML models into production systems. • Support CI/CD pipelines for model updates and versioning. • Ensure system reliability, scalability, and automation for ML workloads. Required Skills • 24 years of experience in ML Ops • Hands-on experience with LangServe and LangFuse . • Good understanding of Docker (building images, managing containers). • Exposure to Kubernetes (deployment, scaling, Helm, monitoring). • Programming skills in Python (preferred) or Java . • Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn). • Understanding of CI/CD tools (GitHub Actions, Jenkins, etc.). • Basic knowledge of cloud platforms (AWS / GCP / Azure). Good to Have • Exposure to LangChain or RAG pipelines . • Knowledge of observability tools ( Prometheus, Grafana, ELK ). • Understanding of ML lifecycle management (MLflow, DVC). Educational Qualification • Bachelors degree in Computer Science, Data Science, AI/ML, or related field.

We are seeking a highly skilled AI/ML Engineer with expertise in Python/Java to design, develop, and deploy scalable AI and machine learning solutions. The ideal candidate will have strong problem-solving skills, hands-on experience with ML frameworks, and the ability to work closely with cross-functional teams to translate business problems into data-driven solutions. Location : Bangalore Experience Required: 2-8 years Key Responsibilities •Design, build, and deploy machine learning models and AI-driven solutions for complex business problems. •Collaborate with data scientists, product managers, and software engineers to integrate ML solutions into production systems. •Perform data preprocessing, feature engineering, and model optimization to improve accuracy and efficiency. •Develop and maintain scalable APIs and microservices for ML model serving using Python or Java . •Implement Lanserve for serving ML/AI models and manage monitoring/tracing using Langfuse . •Containerize and orchestrate ML applications using Docker and Kubernetes for scalable deployments. •Research, evaluate, and implement cutting-edge ML algorithms and deep learning techniques . •Work with cloud platforms (AWS/Azure/GCP) for ML pipelines and deployment. •Maintain clear documentation, reusable code, and best practices in ML engineering. Required Skills & Qualifications • 2-8 years of hands-on experience in AI/ML engineering, with strong coding proficiency in Python and/or Java . •Solid understanding of machine learning algorithms, deep learning, NLP, and computer vision concepts. •Experience with ML/DL frameworks such as TensorFlow, PyTorch, Keras, or Scikit-learn . •Strong knowledge of data structures, algorithms, and software engineering principles . •Hands-on experience with Lanserve (model serving) and Langfuse (observability/monitoring for LLMs/AI systems) . •Proficiency with containerization (Docker) and orchestration (Kubernetes) . •Exposure to big data technologies (Spark, Hadoop, Kafka) is a plus. •Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, CI/CD for ML) . •Experience working on cloud platforms (AWS SageMaker, Azure ML, or GCP AI Platform) . •Strong problem-solving and analytical skills with the ability to translate business requirements into ML solutions. Good to Have •Knowledge of LLMs (Large Language Models) and Generative AI . •Experience with vector databases, embeddings, and retrieval-augmented generation (RAG) . •Contribution to open-source ML/AI projects . Education •Bachelors or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field .

Title : AI QA Automation Engineer Location : Remote Job Summary: We are seeking an AI Quality Engineer with a strong automation skillset to ensure the robustness, performance, and reliability of our AI systems and services. The ideal candidate is tech-savvy, proactive, and passionate about quality at every stepfrom initial design through deployment and ongoing monitoring. You will play a key role in building and maintaining a highly automated testing infrastructure to support fast, reliable model and pipeline delivery as the company scales. Key Responsibilities Testing Expertise: Conduct comprehensive testing across all layers, including server load, integration points, and output quality. Apply Test Driven Development (TDD) principlesanticipate, design, and define all necessary tests before the start of feature development. Identify what needs to be tested and proactively communicate requirements before build phases. Automation-First Approach: Develop, maintain, and extend a fully automated testing suite that covers unit, integration, performance, and end-to-end testing. Emphasize automation to minimize manual intervention and maximize test coverage, reliability, and repeatability. DevOps & CI/CD Integration: Collaborate closely with DevOps to ensure all tests (including those for model deployment and data pipelines) are tightly integrated with modern CI/CD workflows. Streamline rapid yet safe releases through automation and timely feedback. Automated Testing Frameworks: Extensive hands-on experience with frameworks such as Pytest (Python testing), Playwright (end- to-end browser testing), Postman (API testing), and Langfuse (LLM output tracking/testing). Implement and maintain robust API contract testing to ensure reliable interactions between services. Manual & LLM Testing: Execute manual test cases with strong attention to detail, especially for evaluating Large Language Model (LLM) output quality. Flag issues such as hallucinations, factual inaccuracies, or unexpected edge case responses. Continuously update manual testing strategies to adapt to evolving model behaviors and business requirements. Monitoring, Observability & Post-Deploy Quality: ? Configure, deploy, and interpret dashboards from monitoring tools like Prometheus, Grafana, and CloudWatch. ? Track model health, pipeline performance, error rates, and system anomalies after deployment. ? Proactively investigate and triage quality issues uncovered in production. Core Abilities and Technical Skills: Deep practical knowledge in test automation, performance, and reliability engineering. In-depth experience integrating tests into CI/CD pipelines, especially for machine learning and AI model workflows. Hands-on proficiency in automated QA tools: Pytest, Playwright, Postman, Langfuse, and similar. Solid foundation in manual exploratory testing, particularly for complex and evolving outputs such as those from LLMs. Expertise in monitoring, APM, and observability tools (e.g., Prometheus, Grafana, CloudWatch). Demonstrated strong problem-solving skillsanticipate, identify, and resolve issues early. Strong communication skills to clearly articulate requirements, quality risks, and advocate for automation-driven quality throughout the organization. Mindset: Automation-First: Relentless emphasis on driving automation over manual effort. Proactive: Anticipates issues and testing needs; does not wait to be told what to test. Quality Advocate: Champions testing best practices and designs processes to catch bugs before production. Curious & Continuous Learner: Seeks out new tools, stays current with testing frameworks and industry best practices. Collaborative: Partners effectively with product, engineering, and DevOps teams to deliver high- quality models and features at scale. Show more Show less

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

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. 36+ 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.

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. 36+ 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

Role & responsibilities Job Title: Senior Data Scientist Location: Bangalore Business & Team: BB Advanced Analytics and Artificial Intelligence COE Impact & contribution: As a Senior Data Scientist, you will be instrumental in pioneering GenAI and multi-agentic systems at scale within CommBank. You will architect, build, and operationalize advanced generative AI solutionsleveraging large language models (LLMs), collaborative agentic frameworks, and state-of-the-art toolchains. You will drive innovation, helping set the organizational strategy for advanced AI, multi-agent collaboration, and responsible next-gen model deployment. Roles & Responsibilities: • GenAI Solution Development: Lead end-to-end development, fine-tuning, and evaluation of state-of-the-art LLMs and multi-modal generative models (e.g., transformers, GANs, VAEs, Diffusion Models) tailored for financial domains. • Multi-Agentic System Engineering: Architect, implement, and optimize multi-agent systems, enabling swarms of AI agents (utilizing frameworks like Langchain, Langgraph, and MCP) to dynamically collaborate, chain, reason, critique, and autonomously execute tasks. • LLM-Backed Application Design: Develop robust, scalable GenAI-powered APIs and agent workflows using FastAPI, Semantic Kernel, and orchestration tools. Integrate observability and evaluation using Langfuse for tracing, analytics, and prompt/response feedback loops. • Guardrails & Responsible AI: Employ frameworks like Guardrails AI to enforce robust safety, compliance, and reliability in LLM deployments. Establish programmatic checks for prompt injections, hallucinations, and output boundaries. • Enterprise-Grade Deployment: Productionize and manage at-scale GenAI and agent systems with cloud infrastructure (GCP/AWS/Azure), utilizing model optimization (quantization, pruning, knowledge distillation) for latency/throughput tradeoffs. • Toolchain Innovation: Leverage and contribute to open source projects in the GenAI ecosystem (e.g., LangChain, LangGraph, Semantic Kernel, Langfuse, Huggingface, FastAPI). Continuously experiment with emerging frameworks and research. • Stakeholder Collaboration: Partner with product, engineering, and business teams to define high-impact use cases for GenAI and agentic automation; communicate actionable technical strategies and drive proof-of-value experiments into production. • Mentorship & Thought Leadership: Guide junior team members in best practices for GenAI, prompt engineering, agentic orchestration, responsible deployment, and continuous learning. Represent CommBank in the broader AI community through papers, patents, talks, and open- source. Essential Skills: • 10+ years of hands-on experience in Machine Learning, Deep Learning, or Generative AI domains, including practical expertise with LLMs, multi-agent frameworks, and prompt engineering. • Proficient in building and scaling multi-agent AI systems using LangChain, LangGraph, Semantic Kernel, MCP, or similar agentic orchestration tools. • Advanced experience developing and deploying GenAI APIs using FastAPI; operational familiarity with Langfuse for LLM evaluation, tracing, and error analytics. • Demonstrated ability to apply Guardrails to enforce model safety, explainability, and compliance in production environments. • Experience with transformer architectures (BERT/GPT, etc.), fine-tuning LLMs, and model optimization (distillation/quantization/pruning). • Strong software engineering background (Python), with experience in enterprise- grade codebases and cloud-native AI deployments. • Experience integrating open and commercial LLM APIs and building retrieval- augmented generation (RAG) pipelines. • Exposure to agent-based reinforcement learning, agent simulation, and swarm-based collaborative AI. • Familiarity with robust experimentation using tools like LangSmith, GitHub Copilot, and experiment tracking systems. • Proven track record of driving GenAI innovation and adoption in cross-functional teams. Education Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related technical discipline. Desirable: • Papers, patents, or open-source contributions to the GenAI/LLM/Agentic AI ecosystem. • Experience with financial services or regulated industries for secure and responsible deployment of AI.