167 Crewai Jobs - Page 7

We are seeking a skilled, motivated, and quick-learner Full Stack Developer to join our team working on cutting-edge Gen AI development work. The successful candidate will be responsible for developing innovative applications and solutions using including frontend and backend. While the solutions will often utilize Retrieval Augmented Generation (RAG), Agentic frameworks the role will not be limited to this, and will involve various AI technologies. Develop and maintain web applications using Angular, NDBX frameworks, and other modern technologies. Design and implement databases in Postgres DB, apply & implement ingestion and retrieval pipelines using pgvector, neo4j, ensuring efficient and secure data practices. Use different generative AI models & frameworks such as LangChain, Haystack, LlamIndex etc for chucking, embeddings, chat completions, integration with different data sources etc. Familiarity and experience with different agentic frameworks and technique like Langgraph, AutoGen, CrewAI, tool using techniques like MCP (Model Context Protocol). Use Azure & AWS cloud platforms in implementation to stay relevant to company AI guidelines requirements. Usage of OpenAPI standards, API first approach to develop APIs for communication between different software components. Collaborate with the team members to integrate various GenAI capabilities into the applications, including but not limited to RAG. Write clean, maintainable, and efficient code that adheres to company standards. Conduct testing to identify and fix bugs or vulnerabilities. Use collaboration and versioning tools such as GitHub for effective team working and code management. Stay updated with emerging technologies and apply them into operations and activities. Show a strong desire for continuous learning and the ability to quickly adapt and implement new technologies. Bachelor's degree in Computer Science, Information Technology, or a related field with 6+ years of working experience. Proven experience as a Full Stack Developer or similar role in designing, developing and deploying end to end applications. Knowledge of multiple front-end languages and libraries (e.g. HTML/ CSS, JavaScript, XML, jQuery). Experience with Angular and NDBX frameworks. Good experience with database technology such as Postgres DB, vector databases. Experience developing APIs following the OpenAPI standards. Understanding & experience in various generative AI models on cloud platforms such as Azure/ AWS, including Retrieval Augmented Generation, Prompt engineering, Agentic RAG, Agentic frameworks, Model context protocols etc. Experience with collaboration and versioning tools such as GitHub Experience with docker images, containers to package up an application with all the parts it needs, such as libraries and other dependencies, and ship it all out as one package. Functional Demonstrated responsiveness and accuracy Strong collaboration and teamwork skills Behaviours Take Ownership & Accountability Assertiveness & Persuasiveness Great attention to detail and problem-solving skills. Self-motivated with a keen interest in learning and implementing new technologies.

Job Description & Responsibilities Identify valuable data sources and automate collection processes Undertake preprocessing of structured and unstructured data Analyze large amounts of information to discover trends and patterns Build predictive models and machine-learning algorithms Combine models through ensemble modeling Present information using data visualization techniques Propose solutions and strategies to business challenges Collaborate with engineering and product development teams. Qualification, Skills & Experience Good hands-on experience in Advanced Python Programming Experiences in scalable Microservice Architecture and Development Good understanding of API Development, Messaging Queues and Asynchronous Task Management Basic DevOp skills and working with Docker Experience with LangChain, LangGraph, AutoGen or CrewAI are a big plus Ability to co-work in a small team with different locations Ability to learn and understand a new Python framework

We are seeking a proficient AI/ML Engineer responsible for testing, deploying, and hosting various Large Language Models (LLMs). The role involves monitoring deployed agentic services, ensuring their optimal performance, and staying abreast of the latest advancements in AI technologies. Key Responsibilities: LLM Deployment & Hosting Evaluate, test, and deploy diverse LLMs on cloud-based and on-premise infrastructures. Optimize model performance for scalability and efficiency. Implement secure API endpoints and model-serving pipelines. Strong CI/CD skills and knowledge of CI/CD tools like Jenkins, GitHub Actions, CircleCI etc. Agentic AI Services Deploy and maintain AI agents using frameworks such as CrewAI, Agnos(Phi Data), AutoGen etc. Integrate LLMs into business workflows and automation tools. Design, monitor, and enhance agentic services for real-world applications. Monitoring & Optimization Utilize advanced observability tools to monitor model performance, latency, and cost efficiency. Implement tools like AgentOps, OpenLIT, Langfuse, and Langtrace for comprehensive monitoring and debugging of LLM applications. Develop logging, tracing, and alerting systems for deployed models and AI agents. Conduct A/B testing and gather user feedback to refine AI behavior. Address model drift and retrain models as necessary. Best Practices & Research Stay updated with the latest advancements in AI, LLMs, and agentic systems. Implement best practices for prompt engineering, reinforcement learning from human feedback (RLHF), and fine-tuning methodologies. Optimize compute costs and infrastructure usage for AI applications. Collaborate with researchers and ML engineers to integrate state-of-the-art AI techniques. Strong knowledge on version control tools like GitHub Qualifications & Skills: Proficiency with LLM frameworks such as Hugging Face Transformers, OpenAI API, or Meta AI models. Strong programming skills in Python and experience with deep learning libraries (PyTorch, TensorFlow, JAX). Experience with cloud platforms (AWS, Azure, GCP) and model deployment tools (Docker, Kubernetes, FastAPI, Ray Serve). Familiarity with vector databases (FAISS, Pinecone, Weaviate) and retrieval-augmented generation (RAG) techniques. Hands-on experience with monitoring tools such as AgentOps, OpenLIT, Langfuse, and Langtrace. Understanding of prompt engineering, LLM fine-tuning, and agent-based automation. Excellent problem-solving skills and the ability to work in a dynamic AI research and deployment team. Preferred Qualifications: Experience in reinforcement learning, fine-tuning LLMs, or training custom models. Knowledge of security best practices for AI applications. Contributions to open-source AI/ML projects or research publications in the field.

Job description Job description Job Summary: We are looking for a results-driven and innovative Data Scientist with 46 years of experience in data analysis, machine learning, and product optimization. The ideal candidate will have a strong foundation in Python, SQL, and cloud services, along with practical exposure to GenAI, LLMs, and MLOps frameworks. You will be responsible for building scalable data pipelines, developing machine learning models, and solving real-world business problems with data-driven solutions. Key Responsibilities: Design and deploy LLM-powered solutions (e.g., RAG, LangChain, Vector DB) to enhance business processes. Build and fine-tune traditional ML models (Random Forest, Decision Trees) for predictive analytics. Optimize LLM performance using LoRA fine-tuning and post-training quantization. Develop and deploy containerized AI applications using Docker and FastAPI. Collaborate with agents like CrewAI and LangSmith to automate document processing and data extraction workflows. Implement Python & SQL-based ETL pipelines for real-time data ingestion. Design dashboards and KPI monitoring tools using Metabase to enable data-driven decision-making. Create data consumption triggers and automate reporting for international stakeholders. Moderate large-scale live virtual data science classes and provide operational support. Required Skills: Programming: Python, SQL, FastAPI ML & AI: Random Forest, Decision Trees, Clustering, PCA, DL, NLP, Transformers, Gen AI Frameworks/Tools: LangChain, MLflow, Kubeflow, CrewAI, LangSmith DevOps: Docker, Git Databases: MySQL, PostgreSQL, MongoDB Cloud: AWS (S3, EC2) Visualization: Metabase Other: Experience with LLM fine-tuning and quantization Role & responsibilitiesRole & responsibilities Preferred candidate profile

Role: AI/ML Engineer Has 4+ years of work experience , with at least 2+ years directly working on Generative AI and Agentic systems Knows how to use Large Language Models (LLMs) like GPT for real applications (not just research or theory) Has worked on multi-agent systems or autonomous AI agents using tools like CrewAI, AutoGen, or similar frameworks Can write smart prompts ( prompt engineering ), build apps with LLMs, and knows how to do RAG (Retrieval-Augmented Generation) Has done fine-tuning of models and built custom AI workflows Codes well in Python Has used ML frameworks like TensorFlow or PyTorch Familiar with tools like LangChain , and cloud/data platforms like AWS or Databricks Location: Bangalore/ Mumbai Shift Timing: 12:00 PM 9:00 PM Notice Period: 30 days or less Call Anumeha @ +91 6376649769/ Alfiya @ +91 8787064649

Role Overview: We are seeking an Agentic AI Engineer with 2 to 5 years of experience to design, build, and optimize AI agents that can autonomously plan, reason, and execute tasks. The role involves working on AI frameworks, integrating APIs, and developing intelligent systems capable of decision-making and interaction. Key Responsibilities: Develop and deploy autonomous AI agents using LLMs and agentic AI frameworks. Work with LangChain, AutoGPT, CrewAI, OpenAI APIs or similar tools. Integrate AI systems with external APIs, databases, and enterprise tools. Optimize agent workflows for task planning, reasoning, and execution . Collaborate with cross-functional teams to build production-grade AI solutions . Monitor and improve performance, reliability, and scalability of AI systems. Required Skills & Experience: 25 years of experience in AI/ML, software development, or related fields . Strong programming skills in Python/JavaScript . Hands-on experience with LLMs (OpenAI, Anthropic, Cohere, etc.) . Familiarity with LangChain, LlamaIndex, or other agent frameworks . Knowledge of REST APIs, GraphQL, cloud platforms (AWS, GCP, Azure) . Problem-solving mindset with ability to design autonomous workflows . Good to Have: Exposure to Reinforcement Learning (RL), RAG (Retrieval Augmented Generation) . Experience with Vector Databases (Pinecone, Weaviate, FAISS) . Prior experience building chatbots, copilots, or workflow automationRole & responsibilities Preferred candidate profile

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.

- Senior AI/ML Engineer | 8 yrs | Bangalore - Prompt Engineering, LLMs (temp, top_p) - LangChain, LangGraph, CrewAI - RAG, LLM evaluation & observability - Python, Cloud deployment (AWS/GCP/Azure) Required Candidate profile - Prompt Engineering - LLMs & hyperparameters - LangChain/LangGraph/CrewAI - RAG - LLM evaluation - Python - Cloud deployment

Job Overview: We are looking for a highly skilled AI & Agentic Developer to design, build, and deploy intelligent autonomous systems. This role involves developing AI-powered agents that can perceive, reason, and act independently, leveraging LLMs, reinforcement learning, and multi-agent systems. You will work on cutting-edge AI frameworks like LangChain, AutoGen, CrewAI, and MLOps pipelines for scalable deployment. Key Responsibilities: AI & Machine Learning Development: Develop, train, and fine-tune machine learning (ML) and deep learning (DL) models. Implement LLMs, NLP, reinforcement learning (RL), and computer vision (CV) models. Optimize retrieval-augmented generation (RAG) systems with vector databases (FAISS, Pinecone, Chroma). Work on multimodal AI systems integrating text, image, and audio processing. Agentic AI Systems Development: Build autonomous AI agents that can make decisions, learn from feedback, and interact with environments. Implement multi-agent frameworks using LangChain, CrewAI, AutoGen, BabyAGI, MetaGPT. Integrate AI agents with APIs, databases, and enterprise applications for real-world deployment. Software Engineering & MLOps: Design and deploy AI models using Docker, Kubernetes, and CI/CD pipelines. Implement MLOps workflows for continuous training, monitoring, and model retraining. Optimize AI solutions for cloud platforms (AWS, Azure, GCP) and edge computing. AI Governance & Security: Implement AI safety measures, including adversarial defense and bias mitigation. Ensure compliance with AI regulations (GDPR, HIPAA, SOC 2) and ethical AI principles. Secure AI agents against prompt injection, model poisoning, and adversarial attacks. Required Skills & Qualifications: Technical Skills: Strong programming skills in Python, Java, or R. Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-Learn). Experience with LLMs & NLP (Hugging Face, OpenAI API, BERT, GPT, Claude). Proficiency in multi-agent frameworks (LangChain, CrewAI, AutoGen). Knowledge of reinforcement learning (PPO, A3C, DDPG, SAC). Experience with vector databases (FAISS, Pinecone, Chroma) and knowledge graphs. Strong foundation in MLOps, cloud AI services (AWS, Azure AI, Google Vertex AI). Soft Skills: Strong analytical and problem-solving skills. Ability to work in a fast-paced, cross-functional team. Excellent communication and documentation skills.

About the Role: We are seeking a highly motivated and technically proficient AI Agent Development Engineer to join our advanced AI team. In this role, you will design, build, and deploy intelligent, autonomous AI agents that leverage Generative AI , Reinforcement Learning , and Natural Language Processing (NLP) to perform complex, dynamic tasks across diverse domains. You will work at the forefront of AI agent architecture , integrating reasoning, memory, tool usage, and multi-step decision-making capabilities using state-of-the-art libraries and frameworks. Key Responsibilities: Design, develop, and deploy autonomous AI agents for real-world task automation and decision-making and orchestration. Integrate and fine-tune LLMs (Large Language Models) for goal-oriented, tool-using agents. Implement memory-augmented reasoning, retrieval-augmented generation (RAG), and multi-agent coordination. Work with vector databases to store and retrieve contextual memory and knowledge. Optimize agent performance using Reinforcement Learning frameworks and human feedback. Collaborate with cross-functional teams to integrate AI agents into applications and services. Monitor, test, and maintain AI pipelines in production environments. Required Skills & Experience: Strong programming skills in Python (C# or R is a plus). Proven experience in building and deploying AI agents or LLM-based applications . Hands-on expertise in: AI Agent Frameworks: LangChain, AutoGPT, BabyAGI, CrewAI, AgentGPT LLMs & Generative AI: OpenAI (GPT-3.5/4), Hugging Face Transformers, Anthropic Claude, Cohere Vector Search & Memory: Pinecone, FAISS, ChromaDB, Weaviate Reinforcement Learning: OpenAI Gym, Stable-Baselines3, Ray RLlib NLP & Language Tools: spaCy, NLTK, TextBlob Modeling & Deployment: TensorFlow, PyTorch, Keras, Scikit-learn, MLflow APIs & UI Frameworks: Flask, FastAPI, Streamlit, Gradio DevOps: Docker, Git, CI/CD workflows, Kubernetes, Terraform Preferred Qualifications: Experience with agent orchestration tools like LangGraph , ReAct, or Toolformer. Exposure to real-time or asynchronous multi-agent systems. Familiarity with prompt engineering, context management, and RAG pipelines. Contributions to open-source agent/LLM projects or research publications in AI/ML. Why Join Us? Be part of an AI-first product company solving real-world automation and decision-making challenges. Work with a team of AI innovators and contribute to cutting-edge research and applications.

About the Role: We are seeking a highly motivated and technically proficient AI Agent Development Engineer to join our advanced AI team. In this role, you will design, build, and deploy intelligent, autonomous AI agents that leverage Generative AI , Reinforcement Learning , and Natural Language Processing (NLP) to perform complex, dynamic tasks across diverse domains. You will work at the forefront of AI agent architecture , integrating reasoning, memory, tool usage, and multi-step decision-making capabilities using state-of-the-art libraries and frameworks. Key Responsibilities: Design, develop, and deploy autonomous AI agents for real-world task automation and decision-making and orchestration. Integrate and fine-tune LLMs (Large Language Models) for goal-oriented, tool-using agents. Implement memory-augmented reasoning, retrieval-augmented generation (RAG), and multi-agent coordination. Work with vector databases to store and retrieve contextual memory and knowledge. Optimize agent performance using Reinforcement Learning frameworks and human feedback. Collaborate with cross-functional teams to integrate AI agents into applications and services. Monitor, test, and maintain AI pipelines in production environments. Required Skills & Experience: Strong programming skills in Python (C# or R is a plus). Proven experience in building and deploying AI agents or LLM-based applications . Hands-on expertise in: AI Agent Frameworks: LangChain, AutoGPT, BabyAGI, CrewAI, AgentGPT LLMs & Generative AI: OpenAI (GPT-3.5/4), Hugging Face Transformers, Anthropic Claude, Cohere Vector Search & Memory: Pinecone, FAISS, ChromaDB, Weaviate Reinforcement Learning: OpenAI Gym, Stable-Baselines3, Ray RLlib NLP & Language Tools: spaCy, NLTK, TextBlob Modeling & Deployment: TensorFlow, PyTorch, Keras, Scikit-learn, MLflow APIs & UI Frameworks: Flask, FastAPI, Streamlit, Gradio DevOps: Docker, Git, CI/CD workflows, Kubernetes, Terraform Preferred Qualifications: Experience with agent orchestration tools like LangGraph , ReAct, or Toolformer. Exposure to real-time or asynchronous multi-agent systems. Familiarity with prompt engineering, context management, and RAG pipelines. Contributions to open-source agent/LLM projects or research publications in AI/ML. Why Join Us? Be part of an AI-first product company solving real-world automation and decision-making challenges. Work with a team of AI innovators and contribute to cutting-edge research and applications.

About this role: Wells Fargo is seeking a Principal AI Engineer. In this role, you will: Act as an advisor to leadership to develop or influence applications, network, information security, database, operating systems, or web technologies for highly complex business and technical needs across multiple groups Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering solutions that are long-term, large-scale and require vision, creativity, innovation, advanced analytical and inductive thinking Translate advanced technology experience, an in-depth knowledge of the organizations tactical and strategic business objectives, the enterprise technological environment, the organization structure, and strategic technological opportunities and requirements into technical engineering solutions Provide vision, direction and expertise to leadership on implementing innovative and significant business solutions Maintain knowledge of industry best practices and new technologies and recommends innovations that enhance operations or provide a competitive advantage to the organization Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership Required Qualifications: 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: This is an individual contributor role (Principal AI Engineer) which is at the level of Executive Director Own the end-to-end AI/ML lifecycledesign, build, deploy, and maintain scalable agent solutions for Technology Analytics. Embed Generative AI and Agentic AI into day-to-day business processes to lift efficiency and employee productivity. Execute and refine the AI roadmap, keeping every project tightly aligned with enterprise strategy and measurable KPIs. Design autonomous AI agents capable of real-time decision-making and action with minimal human oversight. Enforce best-in-class practices for model scalability, reliability, security, and ongoing maintenance. Lead R&D on emerging AI tech, scouting fresh use cases to keep Wells Fargo at the innovation forefront. Drive cross-division rollout of AI solutions, track impact, and deliver tangible cost- and time-savings. Serve as an AI thought leadermentor teams, champion best practices, and translate complex concepts for senior stakeholders. 7 years of Analytics, AI or Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education. 3+ years of experience in Generative AI tech like Retrieval-Augmented Generation (RAG) design end-to-end pipelines (Vector DBs, chunking, embedding, retrieval, context injection) to ground LLM responses on enterprise knowledge. Strong software development skills, particularly in Python, with experience working with AI frameworks (any one of Langchain, Langgraph, CrewAI, Autogen, Google ADK ) and tools in cloud environments; Knowledge of ML, NLP, Information Retrieval, Recommender Systems and LLMs. Must havedeployed role-based agent ( Multi Agent Orchestration ) teams with one of the listed frameworks Experience with container & orchestration technologies (Docker, Kubernetes etc) and cloud platforms (AWS, GCP or Azure); Experience with training, fine-tuning, and applying large language models (LLMs) for agentic AI application Deployment & MLOps - containerize inference endpoints with Docker/K8s, scale on AWS/GCP GPU instances, and automate CI/CD, canary releases, and rollback strategies. 5+ years of experience with NLP Tech/ NLP pipelines (NLTK, spaCy, Hugging Face, BERT/GPT) 5+ years of hands-on experience with Microsoft Power Platform (Power Apps, Power Automate, Power BI, Dataverse), including solution architecture, governance, and deployment. Working knowledge of enterprise tools like MS Copilot Studio and AgentSpace for Agentic AI, with a strong understanding of prompt engineering, grounding, and extensibility. Familiarity with Data Connectors and API gateways that support seamless communication between systems. General Analytics Data Visualization using Tableau, PowerBI. Strong knowledge of SQL (Structured Databases) Deep subject matter expertise in AI technologies, including but not limited to Copilot Studio, Azure AI Foundry, Google Agent Space, Google Gemini, Microsoft 365, and M365 Copilot. Traditional Data Science - classical ML and statistical modelinglinear/logistic regression, time-series ARIMA/Prophet, decision trees, random forests, gradient-boosting (XGBoost/LightGBM), SVMs, k-means and hierarchical clustering, association-rule mining (Apriori), and A/B testing with hypothesis-testing frameworks Ability to orchestrate processes across integrated systems to enable robust workflows and seamless operations. Ability to collaborate with cross-functional teams to align technical solutions with business goals and end-user needs. A strong focus on cybersecurity and risk management throughout the software development lifecycle. Passion for designing solutions that prioritize end-user experience and usability. Strong problem-solving skills and attention to detail in complex system design. Proficiency in designing and developing multi-agent systems where multiple AI agents collaborate to achieve complex tasks 7 Aug 2025 To request a medical accommodation during the application or interview process, visit . Wells Fargo maintains a drug free workplace. Please see our to learn more.

Bangalore Senior Generative AI & Agentic AI Engineer Location: Kolkata/ Indore/ Pune / Bangaluru Type: Full-time About the Role We are looking for a highly skilled and innovative Generative AI & Agentic AI Engineer with strong conceptual understanding and hands-on implementation expertise. This role requires deep proficiency in Python development, API design, data handling with Pandas, and modular coding practices. The ideal candidate will have proven experience in building and deploying complex Retrieval-Augmented Generation (RAG) solutions and will work closely with cross-functional teams to design, develop, and implement AI-powered solutions. Key Responsibilities Design, develop, and implement advanced Generative AI and Agentic AI solutions. Lead complex RAG (Retrieval-Augmented Generation) implementations, ensuring optimal accuracy, scalability, and performance. Develop, maintain, and optimize Python-based applications following best coding practices and modular design principles. Create, manage, and integrate RESTful APIs for seamless data and AI service interactions. Work extensively with Pandas for large-scale data processing, transformation, and analysis. Collaborate with Data Science, ML, and Product teams to design AI-driven features. Continuously research emerging AI trends, tools, and best practices to keep solutions at the cutting edge. Participate in code reviews , provide mentorship, and maintain high development standards. Required Skills & Qualifications Strong conceptual and practical knowledge of Generative AI & Agentic AI architectures and frameworks. Proven experience implementing complex RAG solutions in production environments. Expert-level proficiency in Python (modular coding, best practices, and performance optimization). Experience in API design and development (RESTful, FastAPI, Flask, or similar). Hands-on experience with Pandas for data processing and analysis. Strong problem-solving and debugging skills. Excellent communication skills ability to clearly articulate technical concepts to both technical and non-technical stakeholders. Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related field. Preferred Skills Experience with LangChain, LangGraph, CrewAI , or similar AI orchestration frameworks. Knowledge of vector databases (e.g., Pinecone, Weaviate, Milvus, ChromaDB). Familiarity with LLM fine-tuning and prompt engineering . Experience deploying AI solutions to cloud platforms (AWS, Azure, GCP). Exposure to MLOps and production-grade AI deployment pipelines. Soft Skills Strong collaboration and teamwork mindset. Ability to work independently and manage multiple priorities. A passion for continuous learning and adapting to new technologies. Why Join Us Work on cutting-edge Generative AI and Agentic AI projects. Collaborate with a highly skilled and passionate team . Opportunities for continuous learning and growth . [Add company-specific perks/benefits here]

Job Title : Agentic AI Backend Developer Years of Experience : 5 to 7 years Job Location : Chennai / Hyderabad Work Mode : Hybrid 3 days from Office Job Description: Role Overview: Seeking an experienced backend developer to architect and implement multi-agent AI solutions with a focus on Retrieval-Augmented Generation (RAG). This position will utilize frameworks like LangGraph and Lang Chain, leverage vector databases such as Pinecone and Weaviate, and deploy solutions within AWS environments including S3 and related services. Key Responsibilities: Design, implement, and optimize multi-agent RAG systems using LangGraph, LangChain, and similar agentic frameworks. Orchestrate and coordinate agent-based workflows, managing state and memory across agents for robust, scalable AI features. Integrate, tune, and maintain vector databases such as Pinecone and Weaviate for efficient semantic search and retrieval. Develop backend APIs and microservices in Python or Node.js, with seamless interactions with agentic pipelines and vector databases. Deploy solutions on AWS, leveraging services like Amazon S3, Lambda, EC2, and container orchestration tools. Collaborate with front-end and cross-functional product teams to deliver high-quality, reliable user experiences. Oversee software lifecycle, maintaining documentation, automated testing, CI/CD pipelines, and performance monitoring. Qualifications: Hands-on experience designing and deploying AI/ML and RAG applications with advanced agentic frameworks (LangGraph, Lang Chain, CrewAI or AutoGen). Strong software engineering background in Python or Node.js; familiarity with FastAPI a plus. Significant expertise working with vector databases, including Pinecone and Weaviate, for RAG architecture. Proven track record of deploying, operating, and optimizing applications on AWS (S3, Lambda, API Gateway, etc.). Familiar with DevOps and MLOps practices; skilled in working with cloud-native tools and services. Exposure to advanced agentic concepts and frameworks; familiarity with Google agentic tools is an advantage. Excellent communication and the ability to thrive within cross-functional teams. Regards, Sreejit.

GenAI Engineer Build and deploy conversational agents using LLMs and multi-agent systems. Design RAG pipelines and integrate with enterprise systems. Experience: ~3 years overall, 2+ years in AI/ML engineering. Must have : Experience with LangGraph, Agno, AutoGen or CrewAI.

We are seeking a passionate and skilled Agentic AI Developer with 2 to 5 years of experience in developing intelligent, autonomous systems using modern AI/ML technologies. In this role, you will design and deploy AI agents that demonstrate reasoning, adaptability, and goal-directed behaviorinteracting proactively with tools, APIs, and digital environments. Key Responsibilities Design, build, and deploy agentic AI systems capable of autonomous, goal-oriented decision-making. Develop AI agents using LLMs, reinforcement learning, memory modules, and multi-agent system architectures. Integrate AI models with external tools, APIs, databases, and software systems to facilitate real-world interactions. Collaborate with cross-functional teams including product managers and data scientists to convert business requirements into intelligent AI solutions. Implement memory and feedback mechanisms to create adaptive, context-aware agent behavior. Optimize performance, scalability, and reliability of AI systems in production environments. Required Skills & Qualifications 2 - 5 years of hands-on experience in AI/ML development and deployment. Proficiency in Python and relevant AI/ML libraries (e.g., LangChain, Hugging Face Transformers, TensorFlow, PyTorch, OpenAI APIs). Strong understanding of agentic AI concepts, including autonomous agents, memory, planning, and decision-making. Familiarity with LLM agent frameworks such as LangChain, AutoGPT, CrewAI, or OpenAgents. Experience with RESTful APIs, microservices architecture, and system integrations. Excellent problem-solving and analytical skills with the ability to think independently. Education Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related discipline. Preferred (Good to Have) Experience in fintech or enterprise-grade AI solutions. Familiarity with cloud platforms such as AWS, GCP, or Azure. Knowledge of agent simulation environments or multi-agent reinforcement learning (MARL).

Role & responsibilities Design, develop, and implement AI-based automation tools for SRE Incident Management. Good experience working with Open AI or other LLM models with understanding of prompting, tool calling, etc. Utilize LLMs to analyze logs and events for generating insights and automating processes. Work with data parsing, chunking, and embedding to enhance the understanding and applicability of data. Collaborate with cross-functional teams to integrate AI solutions into existing systems. Stay updated with the latest advancements in AI technologies, particularly relating to LLMs and agentic AI. Develop core logic, APIs, and use cases using Python. Leverage frameworks such as Lang chain, Crew ai, or other agentic AI frameworks for developing AI applications. Provide support and guidance to junior developers within the team. Preferred candidate profile 6-10 years of professional experience in backend development. Extensive experience with Python for developing backend solutions and AI use cases. Hands-on experience with Large Language Models (LLMs) and related technologies. Proficiency in data parsing, chunking, and embedding. Familiarity with agentic frameworks like Langchain or Crewai. Strong understanding of the latest technologies in LLMs and AI. Basic knowledge of Node.js is a plus. Strong problem-solving skills and ability to work in a fast-paced environment. Excellent communication and collaboration skills.