167 Crewai Jobs - Page 6

Title- AI Engineer Experience - 4 to 6 Years Location - Noida Sec 126 Note-We are looking for a candidate who can Join within July and or having maximum 1 month of Notice period Skills Python Agentic Frameworks AI Models LangGraph Agentic workflows Vector Database AI/ML LLM RAG CrewAI Project Highlights We are seeking a Senior Agentic AI Engineer to lead the design and development of an enterprise-grade Agentic AI framework. This framework will empower various business units across the organization to build intelligent, autonomous agents capable of handling complex workflows, using tools, interacting with APIs, and making decisions with minimal human intervention.This role requires deep expertise in architecting scalable, modular AI systems using LLMs, tool integration, memory systems, and agent orchestration. This role is ideal for someone who has hands-on experience with agentic AI frameworks like LangGraph, CrewAI and AutoGPT and a deep understanding of enterprise software engineering and system architecture. Roles and Responsibilites Architect and build a reusable, secure, and scalable Agentic AI framework that can be adopted by multiple teams across the enterprise. Define standards, patterns, and abstractions for building intelligent agents using LLMs and other foundational models. Leverage and extend frameworks like LangGraph, CrewAI, or Semantic Kernel to enable advanced agentic capabilities such as (Long-horizon task planning, Tool calling (APIs, databases, RPA, internal services), Memory persistence and retrieval (via vector stores or knowledge graphs),Autonomous decision-making and reflection,Multi-agent orchestration and collaboration, Human-in-the-loop workflows,Monitoring and observability,Governance and compliance,Security and access control,Performance optimization and cost management Collaborate with cross-functional teams to ensure the framework aligns with enterprise-grade expectations. Integrate the framework with existing enterprise platforms. Conduct research and stay updated on the latest advancements in Agentic AI, LLMs, and related technologies. Mentor and guide junior engineers in best practices for building agentic systems. Coach and guide engineering teams in adopting and extending the framework. Requirements 4 -6 years of software engineering or AI/ML experience, with 2+ years in agentic or LLM-based system design. Hands-on experience with LangGraph, CrewAI, AutoGPT, BabyAGI, or similar frameworks. Proficiency in Python and common AI/LLM libraries (LangChain, OpenAI API, Hugging Face, etc.). Strong grasp of agent lifecycle, orchestration, and autonomy patterns (e.g., ReAct, Plan-and-Execute, hierarchical agents). Experience in integrating LLM agents with tools (APIs, search, file systems, databases) and managing secure, compliant interaction models. Solid understanding of vector stores , memory architectures, and context management strategies. Experience deploying agentic frameworks in large-scale enterprise environments (finance, telecom, healthcare, etc.) and Knowledge of LangGraph's stateful workflows will be a plus. Benefits Of Working With Us Opportunities to work in Latest Technologies. Challenging / Complex Development Projects. Working with Large clients. Strong Development & Delivery Processes. Focus on Learning and Development. Send your profile to: nikita.gautam@innovationm.com or you can connect over linkedin:- https://www.linkedin.com/in/gautamnikita/

Responsibilities: * Design, develop, test & maintain software solutions using Python, React, AWS, Kubernetes, LLMs, Langchain & Crewai. * Collaborate with cross-functional teams on project delivery & technical strategy.

Must Have Good Experience With GenAI + Autogen + CrewAI + WrenAI Hiring for Technical Architect Role Company have offices in all the mentioned Locations For the Quickest Response Whatsapp on 8287377768 Ayush ( Don't call )

The Technical Lead will focus on the development, implementation, and engineering of GenAI applications using the latest LLMs and frameworks. This role requires hands-on expertise in Python programming, cloud platforms, and advanced AI techniques, along with additional skills in front-end technologies, data modernization, and API integration. The Technical Lead will be responsible for building applications from the ground up, ensuring robust, scalable, and efficient solutions. Key Responsibilities: Application Development: Build GenAI applications from scratch using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends. Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications. OCR and Document Intelligence: Develop solutions for Optical Character Recognition (OCR) and document intelligence using cloud-based tools. API Integration: Use REST, SOAP, and other protocols to integrate APIs for data ingestion, processing, and output delivery. Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications. Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases. LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring. Responsible AI Practices: Ensure ethical AI practices are embedded in the development process. RAG and Modular RAG : Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures for enhanced model performance. Data Curation Automation : Build tools and pipelines for automated data curation and preprocessing. Technical Documentation : Create detailed technical documentation for developed applications and processes. Collaboration : Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship : Guide and mentor junior developers, fostering a culture of technical excellence and innovation. Required Skills : Python Programming : Deep expertise in Python for building GenAI applications and automation tools. Productionization of GenAI application beyond PoCs Using scale frameworks and tools such as Pylint,Pyritetc. LLM Frameworks : Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Front-End Technologies : Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development. Cloud Platforms : Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. Fine-Tuning Techniques : Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps : Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI : Expertise in implementing ethical AI practices and ensuring compliance with regulations. RAG and Modular RAG : Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures. Data Modernization : Expertise in modernizing and transforming data for GenAI applications. OCR and Document Intelligence : Proficiency in OCR and document intelligence using cloud-based tools. API Integration : Experience with REST, SOAP, and other protocols for API integration. Data Curation : Expertise in building automated data curation and preprocessing pipelines. Technical Documentation : Ability to create clear and comprehensive technical documentation. Collaboration and Communication : Strong collaboration and communication skills to work effectively with cross-functional teams. Mentorship : Proven ability to mentor junior developers and foster a culture of technical excellence.

The Implementation Technical Architect will be responsible for designing, developing, and deploying cutting-edge Generative AI (GenAI) solutions using the latest Large Language Models (LLMs) and frameworks. This role requires deep expertise in Python programming, cloud platforms (Azure, GCP, AWS), and advanced AI techniques such as fine-tuning, LLMOps, and Responsible AI. The architect will lead the development of scalable, secure, and efficient GenAI applications, ensuring alignment with business goals and technical requirements. Key Responsibilities: Design and Architecture: Create scalable and modular architecture for GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Development: Lead the development of Python-based GenAI applications, ensuring high-quality, maintainable, and efficient code. Data Curation Automation: Build tools and pipelines for automated data curation, preprocessing, and augmentation to support LLM training and fine-tuning. Cloud Integration: Design and implement solutions leveraging Azure, GCP, and AWS LLM ecosystems, ensuring seamless integration with existing cloud infrastructure. Fine-Tuning Expertise: Apply advanced fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLM performance for specific use cases. LLMOps Implementation: Establish and manage LLMOps pipelines for continuous integration, deployment, and monitoring of LLM-based applications. Responsible AI: Ensure ethical AI practices by implementing Responsible AI principles, including fairness, transparency, and accountability. RLHF and RAG: Implement Reinforcement Learning with Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG) techniques to enhance model performance. Modular RAG Design: Develop and optimize Modular RAG architectures for complex GenAI applications. Open-Source Collaboration: Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment. Front-End Integration: Collaborate with front-end developers to integrate GenAI capabilities into user-friendly interfaces. SDLC and DevSecOps: Implement secure software development lifecycle (SDLC) and DevSecOps practices tailored to LLM-based projects. Technical Documentation: Create detailed design artifacts, technical specifications, and architecture diagrams for complex projects. Stakeholder Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship and Leadership: Guide and mentor junior developers and engineers, fostering a culture of innovation and technical excellence. Required Skills: Python Programming: Deep expertise in Python for building GenAI applications and automation tools. LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Cloud Platforms: Extensive experience with Azure, GCP, and AWS LLM ecosystems and APIs. Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations. RLHF and RAG: Advanced skills in Reinforcement Learning with Human Feedback and Retrieval-Augmented Generation. Modular RAG: Deep understanding of Modular RAG architectures and their implementation. Hugging Face: Proficiency in using Hugging Face and similar open-source platforms for model development. Front-End Integration: Knowledge of front-end technologies to enable seamless integration of GenAI capabilities. SDLC and DevSecOps: Strong understanding of secure software development lifecycle and DevSecOps practices for LLMs. Data Curation: Expertise in building automated data curation and preprocessing pipelines. API Development: Experience in designing and implementing APIs for GenAI applications. Technical Documentation: Ability to create clear and comprehensive design artifacts and technical documentation. Leadership and Mentorship: Proven ability to lead teams, mentor junior developers, and drive technical innovation.

The Implementation Technical Architect will be responsible for designing, developing, and deploying cutting-edge Generative AI (GenAI) solutions using the latest Large Language Models (LLMs) and frameworks. This role requires deep expertise in Python programming, cloud platforms (Azure, GCP, AWS), and advanced AI techniques such as fine-tuning, LLMOps, and Responsible AI. The architect will lead the development of scalable, secure, and efficient GenAI applications, ensuring alignment with business goals and technical requirements. Key Responsibilities: Design and Architecture: Create scalable and modular architecture for GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Development: Lead the development of Python-based GenAI applications, ensuring high-quality, maintainable, and efficient code. Data Curation Automation: Build tools and pipelines for automated data curation, preprocessing, and augmentation to support LLM training and fine-tuning. Cloud Integration: Design and implement solutions leveraging Azure, GCP, and AWS LLM ecosystems, ensuring seamless integration with existing cloud infrastructure. Fine-Tuning Expertise: Apply advanced fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLM performance for specific use cases. LLMOps Implementation: Establish and manage LLMOps pipelines for continuous integration, deployment, and monitoring of LLM-based applications. Responsible AI: Ensure ethical AI practices by implementing Responsible AI principles, including fairness, transparency, and accountability. RLHF and RAG: Implement Reinforcement Learning with Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG) techniques to enhance model performance. Modular RAG Design: Develop and optimize Modular RAG architectures for complex GenAI applications. Open-Source Collaboration: Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment. Front-End Integration: Collaborate with front-end developers to integrate GenAI capabilities into user-friendly interfaces. SDLC and DevSecOps: Implement secure software development lifecycle (SDLC) and DevSecOps practices tailored to LLM-based projects. Technical Documentation: Create detailed design artifacts, technical specifications, and architecture diagrams for complex projects. Stakeholder Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship and Leadership: Guide and mentor junior developers and engineers, fostering a culture of innovation and technical excellence. Required Skills: Python Programming: Deep expertise in Python for building GenAI applications and automation tools. LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Cloud Platforms: Extensive experience with Azure, GCP, and AWS LLM ecosystems and APIs. Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations. RLHF and RAG: Advanced skills in Reinforcement Learning with Human Feedback and Retrieval-Augmented Generation. Modular RAG: Deep understanding of Modular RAG architectures and their implementation. Hugging Face: Proficiency in using Hugging Face and similar open-source platforms for model development. Front-End Integration: Knowledge of front-end technologies to enable seamless integration of GenAI capabilities. SDLC and DevSecOps: Strong understanding of secure software development lifecycle and DevSecOps practices for LLMs. Data Curation: Expertise in building automated data curation and preprocessing pipelines. API Development: Experience in designing and implementing APIs for GenAI applications. Technical Documentation: Ability to create clear and comprehensive design artifacts and technical documentation. Leadership and Mentorship: Proven ability to lead teams, mentor junior developers, and drive technical innovation.

The Technical Lead will focus on the development, implementation, and engineering of GenAI applications using the latest LLMs and frameworks. This role requires hands-on expertise in Python programming, cloud platforms, and advanced AI techniques, along with additional skills in front-end technologies, data modernization, and API integration. The Technical Lead will be responsible for building applications from the ground up, ensuring robust, scalable, and efficient solutions. Key Responsibilities: Application Development: Build GenAI applications from scratch using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends. Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications. OCR and Document Intelligence: Develop solutions for Optical Character Recognition (OCR) and document intelligence using cloud-based tools. API Integration: Use REST, SOAP, and other protocols to integrate APIs for data ingestion, processing, and output delivery. Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications. Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases. LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring. Responsible AI Practices: Ensure ethical AI practices are embedded in the development process. RAG and Modular RAG : Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures for enhanced model performance. Data Curation Automation : Build tools and pipelines for automated data curation and preprocessing. Technical Documentation : Create detailed technical documentation for developed applications and processes. Collaboration : Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship : Guide and mentor junior developers, fostering a culture of technical excellence and innovation. Required Skills : Python Programming : Deep expertise in Python for building GenAI applications and automation tools. Productionization of GenAI application beyond PoCs Using scale frameworks and tools such as Pylint,Pyritetc. LLM Frameworks : Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Front-End Technologies : Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development. Cloud Platforms : Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. Fine-Tuning Techniques : Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps : Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI : Expertise in implementing ethical AI practices and ensuring compliance with regulations. RAG and Modular RAG : Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures. Data Modernization : Expertise in modernizing and transforming data for GenAI applications. OCR and Document Intelligence : Proficiency in OCR and document intelligence using cloud-based tools. API Integration : Experience with REST, SOAP, and other protocols for API integration. Data Curation : Expertise in building automated data curation and preprocessing pipelines. Technical Documentation : Ability to create clear and comprehensive technical documentation. Collaboration and Communication : Strong collaboration and communication skills to work effectively with cross-functional teams. Mentorship : Proven ability to mentor junior developers and foster a culture of technical excellence.

Location: Hyderabad, India | Employment Type: Full-Time Experience Level: 2+Years Company: Covasant Contact Person: Ranjith Reddy 9703455109 | ranjith.palle@covasant.cm | linkedin.com/in/ranjith-r-75a766227 Build the Future of AI with Covasant At Covasant , we don't just work with AI we engineer the next era of it. We're hiring mid-level to senior developers and AI leads to help us build next-generation agentic AI systems that are intelligent, collaborative, and scalable. This is your chance to go beyond prompt engineering and shape the architecture of autonomous, multi-agent AI solutions using tools like LangGraph, AutoGen, CrewAI , and more. If youve got the skills and curiosity to work on what the AI world will be talking about next year , we want to hear from you. Your Role Design and develop multi-agent LLM systems using LangGraph, AutoGen, or CrewAI. Build and deploy MCP servers , LLM gateways , and design Agent-to-Agent collaboration flows. Fine-tune language models for verticals like healthcare, manufacturing, or finance. Architect retrieval-augmented generation (RAG) systems with vector stores like FAISS, Pinecone, or Weaviate. Integrate tools like LangSmith , GuardrailsAI , and knowledge graphs to ensure trust, safety, and observability. Collaborate cross-functionally with product, data science, and engineering teams. What You Bring 2+ years in software development, with strong Python skills. Proven expertise in one or more: LangGraph , AutoGen , CrewAI . Deep understanding of Agent-based AI , LLM orchestration , and RAG pipelines . Experience fine-tuning LLMs and applying prompt engineering and domain adaptation . Familiarity with tools like LangSmith , PromptGuard , or Guardrails frameworks. Bonus If You Have Cloud experience (AWS, Azure, GCP) Familiarity with Docker, Kubernetes Exposure to multi-modal models (LLaMA, Mistral, Falcon) Frontend tech: React, Angular, or Vue CI/CD, MLOps, or LLMOps knowledge Important We’re currently hiring mid-level, senior, and lead professionals with hands-on experience in AI/ML projects . These openings are not for freshers or professionals with less than 2 years of experience — but we are planning something exciting for early-career AI talent soon! Why Join Covasant Work on real-world agentic AI systems ahead of industry trends Collaborative and innovation-first work culture Competitive pay, benefits & performance incentives Hybrid/flexible work setup A chance to lead and influence the next chapter in AI Let’s Connect If this excites you — whether or not you're actively job hunting — don’t miss the chance to explore this game-changing opportunity. Ranjith Reddy – 9703455109 ranjith.palle@covasant.cm Connect with me on LinkedIn – I’d love to stay in touch, even if this isn’t the right time. Apply now or just start a conversation. The future of AI doesn’t wait — and neither should you.

Role Overview: We are looking for a visionary Enterprise IT Architect to lead the transformation of our IT organization into an Agentic AI-driven powerhouse. This individual will architect modern, scalable, AI-native IT platforms that optimize operations, drive automation, and showcase how next-gen IT can innovate at scale. You will work across infrastructure, applications, data, security, and operations to infuse agentic capabilities into the fabric of enterprise IT. Here is how, through this exciting role, you will contribute to BMC's and your own success: Architect AI-native IT Systems : Design modern enterprise architectures that embed autonomous AI agents across IT functions including operations, service management, monitoring, and security. Enable Agentic AI Use Cases : Translate IT goals into agentic AI use cases across ticket triage, auto-remediation, provisioning, governance, and user support. Drive Platform Modernization : Lead the modernization of legacy IT platforms to be cloud-native, API-first, and AI-ready. Champion Automation & Observability : Implement intelligent automation, real-time observability, and self-healing capabilities using LLMs, agents, and RPA. Collaborate Cross-functionally : Partner with engineering, security, infrastructure, and product teams to ensure seamless integration of AI agents with enterprise systems. Evaluate and Adopt Technologies : Assess AI toolkits, orchestration frameworks, and multi-agent systems for enterprise applicability. Strategic Roadmapping : Contribute to the roadmap for AI transformation in IT aligned with business outcomes and goals Security and Governance : Ensure that AI-driven architectures meet enterprise-grade standards for risk, compliance, and governance. To ensure youre set up for success, you will bring the following skillset & experience: Mandatory Skills : 10+ years of experience in enterprise IT architecture, with 3+ years in AI Proven experience architecting solutions involving AI agents, cloud platforms (AWS, Azure, GCP). Deep understanding of AI orchestration frameworks, LLMs, agent stacks (e.g., AutoGen, LangChain, CrewAI), and observability tools. Experience designing IT solutions with resilience, security, and scale in mind. Strong knowledge of ITIL, SRE principles, DevOps, and enterprise systems (ERP, identity, asset management, etc.). Passion for innovation, continuous improvement, and being customer-zero for AI-driven IT. Preferred Skills : Familiarity with agent simulation environments and feedback loops. Exposure to enterprise use of generative AI, vector databases, and cloud-native ML pipelines. Strategic mindset with the ability to balance innovation with risk, cost, and governance.

Responsibilities: * Develop AI/ML models using Python, Agentic AI, CrewAI, Generative AI tools. * Collaborate with cross-functional teams on project delivery. * Optimize model performance through data analysis and iteration. Flexi working Provident fund

Should have exp with Agen c frameworks like Crew AI, Lang, graph, Swarm AI etc. Must have contributed significantly to the development of at least 2 Gen AI projects. Must have worked with mul ple Gen AI models including GPT, Claude, Qwen, Lama etc.

Project description We're seeking a strong and creative Software Engineer eager to solve challenging problems of scale and work on cutting edge technologies. In this project, you will have the opportunity to write code that will impact thousands of users every month. You'll implement your critical thinking and technical skills to develop cutting edge software, and you'll have the opportunity to interact with teams across disciplines. In Luxoft, our culture is one that strives on solving difficult problems focusing on product engineering based on hypothesis testing to empower people to come up with ideas. In this new adventure, you will have the opportunity to collaborate with a world-class team in the field of Insurance by building a holistic solution, interacting with multidisciplinary teams. Responsibilities Build scripts that can handle parallel and batch processing and ensure execution with best performance parameters. Fine Tune existing scripts for performance Manage the end-to-end development using Python Should be able to bring best standards and practices in python coding from previous experience. Skills Must have Minimum 6+ years of relevant professional experience on core Python development and CrewAI(open source multiagent orchestration framework) mandatory Automate multiagent workflows using CrewAI Should be hands-on at scripting, programming especially in Python Should be capable of designing the program independently e.g. Python wrappers around other programs / scripts Should have been working on Agile DevOps culture [ADO ] Kanban and sprints. Should be senior enough for matured interactions internally and with Clients Nice to have Knowledge of Open Telemetry , SDK, APIs and client server models Acquaintance with IBM Z mainframe systems and relevant components like Omnibus, MQ, CICS, DB2, IMS Exposure to Monitoring cum observability domain Other Languages English: C2 Proficient Location - Pune,Bangalore,Hyderabad,Chennai,Noida

What you'll do Greetings from Data Security Council of India...!! The Data Security Council of India (DSCI) is a not-for-profit, industry body for data protection in India, setup by nasscom committed to making cyberspace safe, secure, and trustworthy by establishing cybersecurity best practices, standards, and initiatives in cyber security and privacy. DSCI engages with governments, regulators, industry sectors, and think tanks on policy advocacy, thought leadership, capacity building, and outreach initiatives. For more information, visit: www.dsci.in. We are seeking a dynamic and technically proficient AI/ML Engineer to support our AI/ML R&D initiatives in cybersecurity and take ownership of TechSagar.in a knowledge repository for India's emerging technology capabilities. The ideal candidate will possess hands-on experience in generative AI, emerging technologies, and product management. This is a hybrid role combining deep technical development with stakeholder engagement and platform evangelism. Role & responsibilities : AI/ML & Cybersecurity Innovation Support R&D efforts to prototype generative AI models for real-time threat detection and cybersecurity. Design, develop, and deploy machine learning models tailored to cyber threat intelligence and anomaly detection. Research and implement novel AI approaches, including multi-agent and reasoning-based systems. Develop distributed security monitoring frameworks using tools like AutoGen , CrewAI , etc. Build LLM-powered threat analysis tools using LangChain , LlamaIndex , and integrate with enterprise infrastructure. Apply MLOps best practices for model deployment, performance monitoring, and continuous integration. Optimize vector stores (Qdrant, FAISS, Pinecone, etc.) for RAG-based systems. Create synthetic datasets for AI training and model evaluation. Use Pydantic for data validation within AI pipelines. TechSagar Product Responsibilities Manage and evolve the TechSagar.in platformenhancing features, ensuring data integrity, and driving usage. Liaise with tech partners, government bodies, startups, and academia to enrich platform content. Strategize and execute industry engagement plans to market TechSagar and establish its relevance. Represent TechSagar in external forums, conferences, and industry meetings. Collect user feedback, define product roadmap, and ensure alignment with AI/ML advancements. Required Qualifications: Bachelors or Masters degree in Computer Science, Artificial Intelligence, or related field. 12 years of hands-on experience in AI/ML model development and deployment. Strong programming expertise in Python . Familiarity with LangChain , LlamaIndex , and large language models (LLMs). Experience in applying AI to cybersecurity or vulnerability analysis. Good understanding of machine learning algorithms, data pipelines, and model evaluation. Excellent communication skills for technical and stakeholder engagement Preferred Skills: Exposure to generative AI , LLMs, and chain-of-thought reasoning techniques. Working knowledge of MLOps tools such as MLflow , Docker , etc. Familiarity with FastAPI or Flask for API development. Ability to preprocess, clean, and analyze large datasets efficiently. Experience in integrating AI tools with legacy or existing security systems. Technologies & Frameworks: LLM Frameworks: LangChain, LlamaIndex Multi-agent Systems: AutoGen, CrewAI Vector Databases: FAISS, Pinecone, Qdrant, Elasticsearch, AstraDB MLOps Tools: MLflow, Docker Programming & APIs: Python, FastAPI/Flask Data Validation: Pydantic Why Join Us? Be at the forefront of AI innovation in cybersecurity and national technology initiatives. Lead and shape a strategic tech product (TechSagar) with national impact. Collaborate with thought leaders in the AI, cybersecurity, and emerging tech ecosystem.

Were seeking a forward-thinking Gen AI Engineer to design, implement, and optimize cutting-edge agentic AI solutions using frameworks such as Crew.ai , LangChain , and LangGraph . You will work at the intersection of LLMs , Retrieval-Augmented Generation (RAG) systems, and NLP , enabling impactful AI applications across diverse domains. Key Responsibilities Design and deploy autonomous AI agents and multi-agent systems using LLMs such as GPT-4o, Claude, and LLaMA, leveraging Crew.ai, LangChain (and LangGraph). Own the AI solution lifecycle , including data acquisition, model experimentation, fine-tuning, deployment, and production monitoring. Develop scalable AI backends and pipelines using Python with frameworks like PyTorch or TensorFlow, deploying via REST APIs (FastAPI) on cloud platforms like AWS or Azure. Implement RAG-based systems by integrating open-source LLMs (via Hugging Face or Ollama) with vector databases (e.g., Pinecone, ChromaDB) and structured data stores (SQL/NoSQL). Collaborate with cross-functional teams to ensure reliable, maintainable, and impactful AI solutions. Required Skills & Experience 2 to 5 years of experience in AI/ML, NLP, Generative AI, with a focus on agentic AI systems . Strong proficiency in Python and hands-on experience building RAG systems and deploying open-source LLMs. Experience developing AI-driven backends and services with a strong understanding of scalability and performance. Familiarity with vector search technologies, database integration, and cloud-native architectures. Excellent communication and collaboration skills; ability to work effectively in a team environment. Education B.Tech/B.E. or M.Tech/M.E. in Computer Science, AI/ML, or a related field. Role: Gen AI Engineer Designation: AI Developer Experience: 2-5 Years (AI/ML, NLP, Generative AI): Location: Pune

Key Responsibilities: Agentic AI solution implementation leveraging Azure ecosystem, open-source RPA tools and scripting solutions where needed. Automation Solution Development : Create, deploy, and optimize automation workflows and BOTs using Agentic AI frameworks like Autogen. Leverage AI-driven processes to enable smart automation in business operations. Hands-on Cloud Integration : Design and implement automation solutions that integrate seamlessly with Microsoft Azure services (such as Azure Functions , Logic Apps , Azure Storage ) and other cloud-based tools to improve scalability and performance. AI and Python Libraries : Utilize Agentic AI frameworks for enhancing automation processes, and use Python libraries like Playwright for web automation, browser interaction, and testing. Implement machine learning models or AI algorithms where applicable to automate decision-making and data processing tasks. Scripting & Customization : Develop custom scripts in languages such as Python , PowerShell , JavaScript , and others to extend the functionality of RPA solutions, optimize workflows, and troubleshoot complex scenarios. Stakeholder Engagement : Collaborate with business analysts, IT teams, and key stakeholders to gather requirements, align automation goals with business priorities, and ensure automation solutions meet the desired business outcomes. Process Optimization : Continuously monitor, analyze, and optimize existing automation systems to enhance performance, reduce processing times, and ensure robustness. Identify opportunities to further automate manual processes across departments. Technical Leadership & Mentorship : Provide leadership, mentorship, and training to Agentic AI developers and junior engineers. Share best practices and foster a collaborative environment to ensure high standards of automation development.

We are seeking a skilled and innovative AI Agent Developer to design, build, and maintain autonomous AI agents capable of performing complex tasks using large language models (LLMs), APIs, and structured environments. The ideal candidate will have a strong foundation in software engineering, experience with AI frameworks, and a passion for building intelligent, adaptive systems. Immediate joiners will be preferred. Key Responsibilities: • Design, develop, and deploy AI agents capable of autonomous decision-making and task execution. • Integrate agents with LLMs (e.g., OpenAI GPT, Claude, Mistral) and external APIs for dynamic reasoning and action. • Implement memory, planning, and tool-use capabilities in agent architectures. • Collaborate with data scientists and backend engineers to optimize performance and scalability. • Monitor and evaluate agent behavior, adapting systems based on real-world feedback. • Contribute to research and innovation on agent frameworks and human-AI interaction models. Required Skills & Qualifications: • Bachelors or Masters degree in Computer Science, Artificial Intelligence, or related field. • 5+ years of experience with Python, AI/ML frameworks (LangChain, AutoGen, etc.), or agent-based modeling. • Experience with RESTful APIs, cloud platforms (AWS, Azure, GCP), and vector databases (e.g., Pinecone, Weaviate). • Understanding of reinforcement learning, planning algorithms, and prompt engineering. • Excellent problem-solving and communication skills. Preferred Qualifications: • Experience building agents in environments like ReAct, AutoGPT, BabyAGI, or custom frameworks. • Background in cognitive architectures, multi-agent systems, or human-in-the-loop systems. • Exposure to ethical considerations and safety in autonomous AI systems. Interested candidates share resume at meenakshi.middha@alcortech.com with CCTC,ECTC and notice period details.

Role & responsibilities We are seeking an exceptional Data Scientist with specialized expertise in developing multi-agent AI systems. In this role, you will design, implement, and optimize complex AI ecosystems where multiple intelligent agents collaborate to solve sophisticated problems. You will leverage your deep understanding of generative AI, retrieval-augmented generation (RAG), and prompt engineering to create cutting-edge solutions that push the boundaries of artificial intelligence. Key Responsibilities Design and develop generative AI-based multi-agent systems that can collaborate, communicate, and coordinate to achieve complex objectives Architect and implement RAG-based chatbot solutions that effectively leverage knowledge bases and external data sources Create sophisticated prompt engineering strategies to optimize AI agent behavior and inter-agent communication Build, train, and fine-tune generative AI models for various applications within multi-agent systems Develop robust evaluation frameworks to measure and improve multi-agent system performance Implement efficient knowledge sharing mechanisms between AI agents Write clean, efficient, and well-documented Python code for production-ready AI systems Collaborate with cross-functional teams to integrate multi-agent systems into broader product ecosystems Stay at the forefront of AI research and incorporate state-of-the-art techniques into our solutions Preferred candidate profile Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, or related field 4+ years of professional experience in data science or machine learning engineering Extensive experience with Python programming and related data science/ML libraries Demonstrated expertise in developing and deploying generative AI models (e.g., LLMs, diffusion models) Proven experience building RAG-based systems and implementing vector databases Strong background in prompt engineering for large language models Experience designing and implementing generative AI-based multi-agent architectures Excellent problem-solving skills and ability to optimize complex AI systems Preferred Qualifications Experience with LangChain, AutoGPT, CrewAI, or similar frameworks for building agent-based systems Familiarity with orchestration tools for managing complex AI workflows Knowledge of agent communication protocols and collaborative problem-solving frameworks Experience with distributed systems and cloud computing platforms (AWS, GCP, Azure) Contributions to open-source AI projects or research publications in relevant fields Experience with knowledge graphs and semantic reasoning systems Familiarity with MLOps practices and deployment of AI systems at scale

We are seeking a highly skilled and motivated Senior Data Scientist with 56 years of experience to drive the development of intelligent AI systems. This role requires extensive hands-on experience with Large Language Models (LLMs) , strong background in Agentic AI , Machine Learning , and Python programming . You will work on designing autonomous agents, building scalable ML pipelines, and integrating advanced LLM-powered solutions into real-world products. Key Responsibilities: Architect and implement agentic AI systems that use LLMs for autonomous reasoning, planning, and multi-step task execution. Lead the development, fine-tuning, evaluation, and deployment of Large Language Models (LLMs) using frameworks like Hugging Face Transformers, LangChain, LLM orchestration tools, and vector databases. Develop ML models using supervised, unsupervised, and reinforcement learning techniques, and integrate them into production environments. Design and build end-to-end machine learning pipelines, including data ingestion, feature engineering, training, and deployment. Optimize model performance and latency in real-world applications and implement model monitoring and retraining strategies. Collaborate with cross-functional teams including product, engineering, and business to translate AI capabilities into product features. Mentor junior data scientists and contribute to the team's technical excellence and innovation. Required Skills & Experience: 56 years of professional and relevant experience in data science, AI, or machine learning roles. Proven hands-on experience with LLMs, including fine-tuning, prompt engineering, and RAG (Retrieval-Augmented Generation) pipelines. Deep expertise in Python and ML libraries such as scikit-learn, PyTorch, TensorFlow, and transformers. Strong understanding of agentic AI principles, autonomous agents, and task orchestration. Experience with cloud platforms (AWS, GCP, or Azure) and scalable infrastructure for deploying AI models. Exposure to API development, ML model serving, and integration with real-time systems. Excellent communication and collaboration skills in cross-functional environments. Educational Qualification: Bachelor of Technology (B.Tech) in Computer Science, Data Science, Artificial Intelligence, or a related field from a recognized institution. Preferred Qualifications: Contributions to open-source AI/ML projects or research publications related to LLMs or agent-based systems. Familiarity with LangChain, AutoGPT, CrewAI, or other agent orchestration frameworks. Experience with vector databases (e.g., FAISS, Pinecone) and knowledge of semantic search. Understanding of multi-agent collaboration, goal decomposition, and planning architectures.

Key Responsibilities: Design and development of AI-driven applications using frameworks such as LangChain, LangGraph, CrewAI, AutoGPT, and Autogen. Build and maintain RESTful APIs, GraphQL endpoints, and gRPC services to support scalable AI features. Support the development of intelligent agents and multi-agent collaboration mechanisms. Implement reasoning techniques including ReAct, Chain-of-Thought (CoT), and Tree-of-Thought (ToT) as part of AI workflows. Contribute to retrieval-augmented generation (RAG) pipelines using vector stores like Weaviate, Pinecone, FAISS, or ChromaDB. Integrate tool-use APIs, function calling, MCP, and external workflows via tools like Zapier or N8N. Work with orchestration tools such as Airflow, Ray, or Temporal to manage agent workflows and data pipelines. Collaborate with AI/ML engineers, product managers, and other developers in building intelligent, production-grade applications. Required Qualifications: 3-5 years of backend or full-stack development experience, with working on Generative or Agentic AI solutions. Proficiency in Python (preferred), with working knowledge of TypeScript/Node.js, Go, or Java. Hands-on experience developing REST APIs, GraphQL endpoints, and/or gRPC-based services. Familiarity with integrating LLMs like GPT-4, Claude, Gemini, Mistral, or LLaMA. Exposure to frameworks such as LangChain, LlamaIndex, or CrewAI. Basic understanding of vector databases and semantic search tools (e.g., FAISS, Pinecone). Experience working in cloud environments (AWS, GCP, Azure) and with containerization tools (Docker, Kubernetes).

Responsibilities Job Responsibilities: Architect and Design scalable and efficient AI solutions, leveraging technologies such as LangChain, Agentic AI, RAG, Event driven architecture using Kafka etc. Collaborate with cross-functional teams to identify business needs and develop tailored solutions Provide technical leadership and guidance to junior team members Stay up-to-date with the latest advancements in AI, machine learning, and data science, and apply this knowledge to improve our solutions Communicate complex technical concepts to non-technical stakeholders and team members Troubleshoot and resolve technical issues, and provide support to ensure high system uptime and performance Develop and maintain technical documentation, and ensure that all solutions are well-documented and easily maintainable Technical and Professional Requirements: Preferred Qualifications: Experience with Agentic Frameworks such LangGraph, AutoGen, CrewAI Experience with cloud-based technologies, such as AWS or Azure Familiarity with containerization using Docker, and orchestration using Kubernetes Familiarity with agile development methodologies, such as Scrum or Kanban Experience with AI-related tools and frameworks, such as TensorFlow or PyTorch Knowledge of data engineering, data warehousing, and data governance Experience with agile development methodologies, such as Scrum or Kanban Certification in data science, machine learning, or a related field Experience with leadership and mentoring, with a proven track record of guiding junior team members and helping them grow in their careers Strong business acumen, with the ability to understand business needs and develop solutions that drive business growth and improvement. Preferred Skills: Technology->Artificial Intelligence->Artificial Intelligence - ALL Technology->Machine Learning->Generative AI Technology->Machine Learning->AI/ML Solution Architecture and Design->generative ai Technology->Machine Learning->Python Additional Responsibilities: Required Qualifications: B.E/B.Tech/M.E/M.Tech/MCA degree in Computer Science, Information Technology, or a related field At least 8 years of experience in software development, with at least 2 years of experience in Generative AI Proficiency in LangChain, Python, Generative AI, Agentic AI, Kafka, and Advanced Prompt Engineering Techniques Strong understanding of software architecture, design patterns, and principles Excellent problem-solving skills, with the ability to analyze complex technical problems and develop creative solutions Strong communication and teamwork skills, with the ability to collaborate with cross-functional teams and communicate technical concepts to non-technical stakeholders Tech Skill: LangChain, Python, Fast/Flask API, Gen AI, Agentic AI, Advanced Prompt Engineering, Machine Learning, SQL, Kafka Soft Skill: Communication, Team Work, Problem Solving Educational Requirements Bachelor of Engineering Service Line Information Systems Location of posting is subject to business requirements

Job Description: We are seeking a highly skilled and passionate AI/ML Engineer with strong expertise in Generative AI and Large Language Models (LLMs) . The ideal candidate will have hands-on experience in building, fine-tuning, and deploying agentic AI systems using modern GenAI frameworks. You will work on cutting-edge projects involving prompt engineering , RAG pipelines , and memory architectures such as vector databases. Responsibilities: Design and implement AI/ML solutions using modern LLM architectures and agentic AI concepts . Build and optimize intelligent agents using frameworks such as LangChain, AutoGen, CrewAI , or Semantic Kernel . Develop and fine-tune generative AI models with Transformers , HuggingFace , OpenAI API , etc. Implement and enhance Retrieval-Augmented Generation (RAG) pipelines and memory systems like vector databases (e.g., FAISS, Pinecone). Write high-performance Python code to support experimentation, model integration, and API interactions. Collaborate cross-functionally with product, design, and engineering teams in an agile development environment. Deploy AI solutions on cloud platforms (AWS, Azure, or GCP) with a focus on scalability and performance. Stay updated with the latest advancements in the AI/ML/GenAI space. Required Experience: 3 to 8 years of experience in AI/ML , with at least 1 year in Generative AI / LLM-based projects . Proven expertise in Python programming and related libraries for ML/GenAI. Hands-on experience with one or more GenAI frameworks (LangChain, AutoGen, etc.). Solid understanding of prompt engineering , RAG , vector DBs , and agent-based systems . Cloud deployment experience (AWS, Azure, or GCP) is a must. Strong analytical and problem-solving skills.

Job Title : Senior Software Engineer. Exp: 5 Years to 10 Years Location : Remote. About the Company: Galileo is the leading platform for Gen AI evaluation and observability, with a mission to democratize building safe, reliable and robust applications in the new era of AI powered software development. Our foundation is built on pioneering the early technology behind the world's most ubiquitous AI applications including Apple's Siri and Google Speech. We firmly believe that AI developers require meticulously crafted, research-driven tools to create trustworthy and high-quality generative AI applications that will revolutionize our work and lifestyle. Galileo addresses the complexities inherent in implementing, evaluating, and monitoring GenAI applications, optimizing the development process for both individual developers and teams by offering a comprehensive platform that spans the full AI development lifecycle. Galileo bridges critical gaps, significantly enhancing developers' ability to refine and deploy reliable and precise GenAI applications. Since its inception, Galileo has rapidly gained traction, serving Fortune 100 banks, Fortune 50 telecom companies, as well as AI teams at prominent organizations such as Reddit and Headspace Health, among dozens of others. Galileo has AI research at its core, with the founders coming from Google and Uber where they solved challenging AI/ML problems in the Speech, Evaluation and ML Infra domains. It is now a Series B business backed by tier 1 investors including Battery Ventures, Scale Venture Partners, and Databricks Ventures, with $68M in total funding. We are headquartered in San Francisco with locations such as New York and Bangalore, India forming our areas of future growth. Responsibilities Understand GenAI at a sufficient level of curiosity and usage (beyond simple applications such as using ChatGPT such as understanding and usage of Lang Chain, Lang Graph, Crew AI, OpenAI APIs etc. Be able to quickly understand our product hosted at https://app.galileo.ai/ Have 5 years of experience working on Product Engineering teams building service oriented or general micro services-based applications using Python Can work in a fast-paced environment and can get ideas quickly off the ground with minimal support (ideally has experience working in a mix of large enterprises and startup experience). Skill Requirements Programming languages: Python and Typescript Basic understanding of LLM (OpenAI/Gemini/) API and SDK Basic understanding of AI frameworks like Langchain, CrewAI etc. Has some exposure and understanding of Observability and Reliability (such as familiarity with OTEL, understanding of SLOs) Has some degree of familiarity with Cloud environments such as Azure, GCP and AWS If you are passionate about applying your experience in the AI/ML field to an emerging and advancing field of Generative AI, this is a great opportunity for you. You will get to work on a ground breaking product poised to revolutionize the way companies approach LLM Evaluations. Join our team at Galileo and help us build the future of Evaluations in Generative AI.

Job Title: AI Developer Supply Chain Management Location: Permanent Remote Job Type: Full-time Team: Product AI Engineering We are looking for a talented LLM Developer to join our AI Engineering team and contribute to the development of the SCM Expert AI Agent the core brain behind AI capabilities like demand forecasting, anomaly detection, ETA prediction, and route optimization in the FarmToPlate (F2P) platform. In this role, you will focus on integrating and optimizing LLM-based agentic architectures using frameworks like LangChain, CrewAI, and RAG pipelines. Youll work closely with traditional ML developers and backend engineers to deploy intelligent, explainable, and real-time decision agents for supply chain management. Responsibilities: Design, build, and optimize LLM-based agents using LangChain, CrewAI, or custom wrappers. Implement RAG pipelines with vector stores (e.g., FAISS, ChromaDB) and embedding models. Develop LLM-powered tools for demand forecasting, query understanding, anomaly explanations, and summarization. Integrate agents with internal and external data sources using tools like MongoDB, Sklearn datasets, or Browsing Agents. Implement prompt engineering, agent memory, and context-chaining for more dynamic agent behavior. Collaborate with data science and product teams to translate SCM logic into agentic workflows. Expose models and agents through APIs using FastAPI, including role-based access if needed. Optimize runtime performance with quantization, PEFT (parameter-efficient finetuning), and model caching. Track experiments, versions, and deployment artifacts using tools like MLflow, SageMaker, or custom registries. Requirements: 3-5 years of experience working on Python-based AI systems. Strong understanding of LLMs, Transformers, Prompt Engineering, and conversational AI. Hands-on experience with LangChain, CrewAI, OpenAI/GPT APIs, or Hugging Face Transformers. Familiarity with RAG concepts, vector databases (FAISS, Chroma), and embedding techniques. Good knowledge of API frameworks (FastAPI/Flask) and working with JSON schema-driven inputs. Basic understanding of traditional ML models and workflows (e.g., regression, classification, anomaly detection). Comfortable integrating external data tools and APIs into LLM pipelines. Bonus Points For Experience building Agentic RAG systems that combine logic, tools, memory, and models. Knowledge of Open Source LLM tuning using PEFT, LoRA, bitsandbytes, Unsloth, etc. Familiarity with SageMaker, EKS, or deploying custom models to the cloud. Experience in supply chain/logistics, or working with ERP/SCM structured data. Built monitoring dashboards using Evidently.ai or similar model observability tools. Why Join Us Be part of a domain-driven AI platform blending supply chain expertise with modern agentic LLM architectures. Work on real-world use cases that impact farm-to-plate food systems at scale. Collaborate with a fast-moving, cross-functional team that values innovation, ownership, and outcome. Learn and grow in a modular, full-stack AI environment where your ideas become deployed APIs. Enjoy flexibility in remote, experimentation, and continuous learning.

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.

Required Skills 7+ years of professional Python development experience Hands-on experience with at least one agent framework (CrewAI, LangChain, LangGraph, or equivalent) Strong understanding of LLM capabilities, limitations, and prompt engineering Experience with RESTful API design and async Python (FastAPI preferred) Knowledge of vector databases and RAG (Retrieval Augmented Generation) systems Familiarity with financial or tax domains Demonstrated leadership in technical decision-making Excellent written and verbal communication skills Nice to Have Experience with AWS services, particularly Lambda and EKS Background in financial services or tax preparation software Knowledge of security and compliance requirements for financial data Previous startup experience, particularly in MVP development