26 Autogen Jobs

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 )

In this role, you will: Participate in low to moderately complex initiatives and projects associated with the technology domain, including installation, upgrades, and deployment efforts Identify opportunities for service quality and availability improvements within the technology domain environment Design, code, test, debug, and document for low to moderately complex projects and programs associated with technology domain, including upgrades and deployments Review and analyze technical assignments or challenges that are related to low to medium risk deliverables and that require research, evaluation, and selection of alternative technology domains Present recommendations for resolving issues or may escalate issues as needed to meet established service level agreements Exercise some independent judgment while also developing understanding of given technology domain in reference to security and compliance requirements Provide information to technology colleagues, internal partners, and stakeholders Required Qualifications: 2+ years of software engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: Strong proficiency in Python/Java and LLM orchestration frameworks (LangChain, LangGraph) Basic Knowledge of model context protocols, RAG architectures, and embedding techniques Experience with model evaluation frameworks and metrics for LLM performance Proficiency in frontend development with React.js for AI applications Experience with UI/UX design patterns specific to AI interfaces Experience with vector databases and efficient retrieval methods Knowledge of prompt engineering techniques and best practices Experience with containerization and microservices architecture Strong understanding of semantic search and document retrieval systems Working knowledge of both structured and unstructured data processing Experience with version control using GitHub and CI/CD pipelines Experience working with globally distributed teams in Agile scrums Experience with Google ADK (preferred but not mandatory) AutoGen, and OpenAI APIs Strong verbal and written communication skills Job Expectations Understanding of enterprise use cases for Generative AI Knowledge of responsible AI practices and ethical considerations Ability to optimize AI solutions for performance and cost Well versed in MLOps concepts for LLM applications Staying current with rapidly evolving Gen AI technologies and best practices Experience implementing security best practices for AI applications

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.

***** GenAI Develope r***** ***** Immediate Joine r***** ***** Pune Hinjewadi ***** We are seeking experienced GenAI Developers to join our team in Pune. As a key member of our AI innovation group, you will be responsible for designing, architecting, and implementing scalable Generative AI solutions leveraging AWS services. You will lead the transformation of existing LLM-based architectures into modern, cloud-native solutions and collaborate with cross-functional teams to deliver cutting-edge AI applications. Key Responsibilities: Design and architect scalable GenAI solutions using AWS Bedrock and other AWS services. Lead the transformation of homegrown LLM-based architecture into a managed cloud-native solution. Provide architectural guidance across components: RAG pipelines, LLM orchestration, vector DB integration, inference workflows, and scalable endpoints. Collaborate closely with GenAI developers, MLOps, and data engineering teams to ensure alignment on implementation. Evaluate, integrate, and benchmark frameworks such as LangChain, Autogen, Haystack, or LlamaIndex. Ensure infrastructure and solutions are secure, scalable, and cost-optimized on AWS. Act as a technical SME and hands-on contributor for architecture reviews and POCs. Must-Have Skills: Deep expertise with AWS Bedrock, S3, Lambda, SageMaker, API Gateway, DynamoDB/Redshift, etc. Proven experience architecting LLM applications with RAG, embeddings, prompt engineering. Hands-on understanding of frameworks like LangChain, LlamaIndex, or Autogen. Knowledge of LLMs like Anthropic Claude, Mistral, Falcon, or custom models. Strong understanding of API design, containerization (Docker), and serverless architecture. Experience leading cloud-native transformations. Preferred: Experience with CI/CD, DevOps integration for ML/AI pipelines. Exposure to AWS, Azure/GCP in GenAI (bonus).

*****GenAI/Architect***** *****Immediate Joiners Preferred**** *****Pune Hinjewadi Phase1***** *****4 days a week***** We are seeking an experienced **GenAI Lead / Architect** to design and implement scalable Generative AI solutions leveraging AWS services. The ideal candidate will lead the transformation of existing LLM-based architectures into efficient, cloud-native platforms, ensuring robust, secure, and cost-effective deployments. Key Responsibilities: Design and architect scalable GenAI solutions using AWS Bedrock and other AWS services. Lead the transformation of homegrown LLM-based architecture into a managed cloud-native solution . Provide architectural guidance across components: RAG pipelines, LLM orchestration, vector DB integration, inference workflows, and scalable endpoints. Collaborate closely with GenAI developers, MLOps, and data engineering teams to ensure alignment on implementation. Evaluate, integrate, and benchmark frameworks such as LangChain, Autogen, Haystack, or LlamaIndex . Ensure infrastructure and solutions are secure, scalable, and cost-optimized on AWS. Act as a technical SME and hands-on contributor for architecture reviews and POCs. Must-Have Skills: Proven experience architecting LLM applications with RAG, embeddings, prompt engineering. Hands-on understanding of frameworks like LangChain , LlamaIndex , or Autogen . Knowledge of LLMs like Anthropic Claude, Mistral, Falcon, or custom models . Strong understanding of API design, containerization (Docker), and serverless architecture. Expertise with AWS Bedrock , S3, Lambda, SageMaker, API Gateway, DynamoDB/Redshift, etc. Experience leading cloud-native transformations. Preferred: AWS Certified Solutions Architect. Experience with CI/CD, DevOps integration for ML/AI pipelines. Exposure to Azure/GCP in GenAI (bonus).

About DigiLantern DigiLantern is a forward-thinking technology services company focused on delivering AI-powered digital transformation solutions . Our services include custom web and mobile application development , Salesforce and CRM integrations , AI agents and automation , SaaS product development , and e-commerce solutions . We help clients accelerate growth, enhance customer engagement, and optimize operations through intelligent and scalable technology. Role Overview We are seeking a highly skilled Technical Architect to lead solution architecture and system design across diverse digital and AI-first projects. You will be responsible for building scalable platforms with a strong focus on Agent AI frameworks , Retrieval-Augmented Generation (RAG) pipelines, and modern cloud-native infrastructure. This is a key leadership role that blends hands-on technical design with strategic planning and client engagement. Key Responsibilities Architect robust, scalable, and secure cloud-based solutions for enterprise-grade applications Design and integrate Agent AI workflows using frameworks like LangChain, AutoGen, or Semantic Kernel Build RAG pipelines for knowledge retrieval from vector databases (e.g., FAISS, Pinecone, Weaviate) Collaborate with product managers, developers, and clients to translate requirements into technical blueprints Lead architecture reviews, code quality standards, and technical governance across projects Guide engineering teams on architectural best practices, DevOps, and performance optimization Create technical documentation, architecture diagrams, and project-level design assets Stay current with advancements in AI/ML, cloud infrastructure, and modern development stacks Required Qualifications 10+ years of experience in software development and system architecture Strong backend expertise in Python, Java, Node.js and relevant frameworks (Django, Spring Boot, Express.js) Hands-on experience with cloud platforms : AWS (preferred), GCP, Azure Proven experience with containerization and orchestration : Docker, Kubernetes Deep understanding of Agent AI architectures and LLM orchestration (LangChain, AutoGen, CrewAI) Experience building or integrating RAG-based solutions using vector databases and embedding models Strong knowledge of APIs (REST, GraphQL), security protocols (OAuth2, JWT), and DevSecOps principles Excellent communication and problem-solving skills Bachelor's or Masters degree in Computer Science, Engineering, or a related discipline Preferred Experience Designing and deploying AI agents or automation tools with real-time decision-making capabilities Working with OpenAI APIs, Hugging Face, or custom LLMs Experience in regulated industries such as healthcare, finance, or nonprofit Collaborating with global, cross-functional engineering teams in agile environments Exposure to compliance standards such as HIPAA, GDPR Technology Stack AI & Data Agent AI: LangChain, AutoGen, CrewAI, Semantic Kernel, OpenAI APIs RAG: FAISS, Pinecone, Weaviate, ChromaDB, Haystack ML Tools: TensorFlow, PyTorch, Scikit-learn Databases: PostgreSQL, MongoDB, Redis, MySQL Analytics: Google Analytics, Power BI, Tableau Backend Python, Java, Node.js, PHP Django, Flask, Spring Boot, Express.js Frontend JavaScript / TypeScript React, Angular, Vue.js Material-UI, TailwindCSS Cloud & DevOps AWS, GCP, Azure Jenkins, GitHub Actions, Terraform, Ansible CI/CD with GitLab, CircleCI Security & Integration OAuth2, JWT, SAML SSL/TLS, AES encryption API tools: Swagger, Postman, MuleSoft Why Join DigiLantern? Architect real-world, high-impact solutions with cutting-edge AI technologies Be a key technical leader in a fast-growing digital innovation firm Opportunity to shape future-ready platforms across industries Competitive compensation, collaborative culture, and high ownership Thanks & Regards Pankhuri Agarwal Assistant Manager-HR pankhuri.agarwal@digilantern.co.in 9821486056 www.digilantern.com

Genpact (NYSE: G) is a global professional services and solutions firm delivering outcomes that shape the future. Our 125,000+ people across 30+ countries are driven by our innate curiosity, entrepreneurial agility, and desire to create lasting value for clients . Powered by our purpose - the relentless pursuit of a world that works better for people - we serve and transform leading enterprises, including the Fortune Global 500, with our deep business and industry knowledge, digital operations services, and expertise in data, technology, and AI. Inviting applications for the role of Principal Consultant , Generative AI + Agentic AI ! In this role, we are seeking a highly skilled and experienced AI Engineer with hands-on expertise in Generative AI, AWS Bedrock, Azure Open AI, Document Intelligence tools, Huggingface , Agentic AI, and Advanced Data Pipelines . It%27s platforms semantic kernel, autogen , langchain . Responsibilities Architect and implement advanced generative AI models, focusing on applications such as image and text generation, image and document extraction, RAG, Prompt engineering, and synthetic data generation. Advanced Prompt Engineering techniques. Stay at the forefront of AI/ML and generative AI advancements, driving the adoption of new technologies and best practices within the team. Develop, fine-tune, and optimize generative AI models, ensuring they meet performance, scalability, and business objectives . Firm grasp on tools to compare outputs of each model and compare against each other. Apply solid data science principles to enhance AI/ML models, using statistical analysis and machine learning techniques to improve model accuracy and efficiency. Work closely with data engineers, software developers, and product managers to integrate generative AI models into products and services, aligning with business objectives . Ensure that AI models are designed and deployed with ethical considerations, adhering to industry standards and regulatory requirements. Qualifications we seek in you! Minimum Q ualifications / Skills B achelor&rsquos or M aster&rsquos degree in computer science, AI, Machine Learning, Data Science, or a related field, with 5+ years of experience in AI/ML, particularly with generative AI models. Strong expertise in developing and deploying generative AI models. Proficiency in Python and experience with AI development libraries such as TensorFlow and PyTorch . Experience with Azure OpenAI, Open Source LLMs, and LangChain . Solid experience in applying data science principles, including statistical analysis, data preprocessing, and machine learning, to enhance AI/ML models. Preferred Q ualifications / Skills Familiarity with advanced techniques such as Retrieval-Augmented Generation (RAG), fine-tuning, prompt engineering, and zero-shot learning. Familiarity with NLP or computer vision related to generative AI applications. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. For more information, visit Follow us on X , Facebook, LinkedIn, and YouTube. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training .

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.

Job Position (Title): Agentic AI Experience Required: 3 - 9 Years No of Position: 4 Job Type: FTE Location: Hyderabad Required Skills 3 to 7 years of hands-on experience in AI/ML, with at least 12 years specifically in Agentic AI or autonomous agents. Strong proficiency in Python and familiarity with AI libraries such as Hugging Face Transformers, LangChain, and OpenAI APIs. Experience with tools like AutoGen , CrewAI , and LangGraph . Deep understanding of RAG pipelines , LLM fine-tuning , and embedding-based retrieval systems . Hands-on experience with vector databases like FAISS, Pinecone, or similar. Solid foundation in machine learning, NLP, and data structures. Excellent communication skills and ability to work in a collaborative environment Role and Responsibilities Design, build, and deploy intelligent autonomous agents using Agentic AI frameworks like AutoGen , CrewAI , and LangGraph . Implement RAG (Retrieval-Augmented Generation) pipelines to enhance response accuracy and contextual relevance. Work with vector databases (e.g., FAISS, Pinecone, Weaviate) for knowledge storage and semantic search. Fine-tune large language models (LLMs) for specific tasks using tools like LoRA, PEFT, or full model training. Collaborate with product, research, and engineering teams to understand user needs and create agent-based workflows. Integrate APIs and services to create complex multi-agent systems capable of task delegation and coordination. Continuously evaluate and improve system performance, scalability, and reliability. Preferred Qualifications Prior experience deploying LLM-based agents in production. Knowledge of prompt engineering and agent orchestration. Contributions to open-source AI/agent frameworks.

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.

ABOUT THE ROLE Role Description: We are seeking an accomplished and visionary Data Scientist/ GenAI developer to join Amgens Enterprise Data Management team. As part of MDM team, you will be responsible for design ing , develop ing , and deploy ing Generative AI and ML models to power data-driven decisions across business domains. This role is ideal for an AI practitioner who thrives in a collaborative environment and brings a strategic mindset to applying advanced AI techniques to solve real-world problems. To succeed in this role, the candidate must have strong AI/ML, Data Science , GenAI experience along with MDM knowledge, hence the candidates having only MDM experience are not eligible for this role. Candidate must have AI/ML, data science and GenAI experience on technologies like ( PySpark / PyTorch , TensorFlow, LLM , Autogen , Hugging FaceVectorDB , Embeddings, RAGs etc ), along with knowledge of MDM (Master Data Management) Roles & Responsibilities: Develop enterprise-level GenAI applications using LLM frameworks such as Langchain, Autogen, and Hugging Face. Design and develop intelligent pipelines using PySpark, TensorFlow, and PyTorch within Databricks and AWS environments. Implement embedding models andmanage VectorStores for retrieval-augmented generation (RAG) solutions. Integrate and leverage MDM platforms like Informatica and Reltio to supply high-quality structured data to ML systems. Utilize SQL and Python for data engineering, data wrangling, and pipeline automation. Build scalable APIs and services to serve GenAI models in production. Lead cross-functional collaboration with data scientists, engineers, and product teams to scope, design, and deploy AI-powered systems. Ensure model governance, version control, and auditability aligned with regulatory and compliance expectations. Basic Qualifications and Experience: Masters degree with 4 - 6 years of experience in Business, Engineering, IT or related field OR Bachelors degree with 6 - 9 years of experience in Business, Engineering, IT or related field OR Diploma with 10 - 12 years of experience in Business, Engineering, IT or related field Functional Skills: Must-Have Skills: 6+ years of experience working in AI/ML or Data Science roles, including designing and implementing GenAI solutions. Extensive hands-on experience with LLM frameworks and tools such as Langchain, Autogen, Hugging Face, OpenAI APIs, and embedding models. Strong programming background with Python, PySpark, and experience in building scalable solutions using TensorFlow, PyTorch, and SK-Learn. Proven track record of building and deploying AI/ML applications in cloud environments such as AWS. Expertise in developing APIs, automation pipelines, and serving GenAI models using frameworks like Django, FastAPI, and DataBricks. Solid experience integrating and managing MDM tools (Informatica/Reltio) and applying data governance best practices. Guide the team on development activities and lead the solution discussions Must have core technical capabilities in GenAI, Data Science space Good-to-Have Skills: Prior experience in Data Modeling, ETL development, and data profiling to support AI/ML workflows. Working knowledge of Life Sciences or Pharma industry standards and regulatory considerations. Proficiency in tools like JIRA and Confluence for Agile delivery and project collaboration. Familiarity with MongoDB, VectorStores, and modern architecture principles for scalable GenAI applications. Professional Certifications : Any ETL certification (e.g. Informatica) Any Data Analysis certification (SQL) Any cloud certification (AWS or AZURE) Data Science and ML Certification Soft Skills: Strong analytical abilities to assess and improve master data processes and solutions. Excellent verbal and written communication skills, with the ability to convey complex data concepts clearly to technical and non-technical stakeholders. Effective problem-solving skills to address data-related issues and implement scalable solutions. Ability to work effectively with global, virtual teams.

ABOUT THE ROLE Role Description: We are seeking an accomplished and visionary Data Scientist/ GenAI developer to join Amgens Enterprise Data Management team. As part of MDM team, you will be responsible for design ing , develop ing , and deploy ing Generative AI and ML models to power data-driven decisions across business domains. This role is ideal for an AI practitioner who thrives in a collaborative environment and brings a strategic mindset to applying advanced AI techniques to solve real-world problems. To succeed in this role, the candidate must have strong AI/ML, Data Science , GenAI experience along with MDM knowledge, hence the candidates having only MDM experience are not eligible for this role. Candidate must have AI/ML, data science and GenAI experience on technologies like ( PySpark / PyTorch , TensorFlow, LLM , Autogen , Hugging FaceVectorDB , Embeddings, RAGs etc ), along with knowledge of MDM (Master Data Management) Roles & Responsibilities: Develop enterprise-level GenAI applications using LLM frameworks such as Langchain, Autogen, and Hugging Face. Design and develop intelligent pipelines using PySpark, TensorFlow, and PyTorch within Databricks and AWS environments. Implement embedding models andmanage VectorStores for retrieval-augmented generation (RAG) solutions. Integrate and leverage MDM platforms like Informatica and Reltio to supply high-quality structured data to ML systems. Utilize SQL and Python for data engineering, data wrangling, and pipeline automation. Build scalable APIs and services to serve GenAI models in production. Lead cross-functional collaboration with data scientists, engineers, and product teams to scope, design, and deploy AI-powered systems. Ensure model governance, version control, and auditability aligned with regulatory and compliance expectations. Basic Qualifications and Experience: Masters degree with 4 - 6 years of experience in Business, Engineering, IT or related field OR Bachelors degree with 6 - 9 years of experience in Business, Engineering, IT or related field OR Diploma with 10 - 12 years of experience in Business, Engineering, IT or related field Functional Skills: Must-Have Skills: 6+ years of experience working in AI/ML or Data Science roles, including designing and implementing GenAI solutions. Extensive hands-on experience with LLM frameworks and tools such as Langchain, Autogen, Hugging Face, OpenAI APIs, and embedding models. Strong programming background with Python, PySpark, and experience in building scalable solutions using TensorFlow, PyTorch, and SK-Learn. Proven track record of building and deploying AI/ML applications in cloud environments such as AWS. Expertise in developing APIs, automation pipelines, and serving GenAI models using frameworks like Django, FastAPI, and DataBricks. Solid experience integrating and managing MDM tools (Informatica/Reltio) and applying data governance best practices. Guide the team on development activities and lead the solution discussions Must have core technical capabilities in GenAI, Data Science space Good-to-Have Skills: Prior experience in Data Modeling, ETL development, and data profiling to support AI/ML workflows. Working knowledge of Life Sciences or Pharma industry standards and regulatory considerations. Proficiency in tools like JIRA and Confluence for Agile delivery and project collaboration. Familiarity with MongoDB, VectorStores, and modern architecture principles for scalable GenAI applications. Professional Certifications : Any ETL certification (e.g. Informatica) Any Data Analysis certification (SQL) Any cloud certification (AWS or AZURE) Data Science and ML Certification Soft Skills: Strong analytical abilities to assess and improve master data processes and solutions. Excellent verbal and written communication skills, with the ability to convey complex data concepts clearly to technical and non-technical stakeholders. Effective problem-solving skills to address data-related issues and implement scalable solutions. Ability to work effectively with global, virtual teams We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

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.

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.

We are seeking a talented AI Engineer to join our team. The ideal candidate will have expertise in machine learning, NLP, deep learning, and deploying AI models, with experience in advanced tools and frameworks across AI and backend development. Responsibilities: Develop and deploy AI models using machine learning , deep learning (CNNs, RNNs, LSTMs), and NLP ( GPT , BERT ). Work with advanced AI frameworks ( Langchain , AutoGen , Streamlit ) and backend frameworks ( FastAPI , Flask ). Implement reinforcement learning and autonomous decision-making systems. Leverage cloud platforms ( AWS: Bedrock, Sagemaker ) for model deployment and scalability. Integrate third-party services ( OpenAI , Azure Foundry ) and tools ( RelevanceAI , Huggingface ). Work with databases ( MongoDB , Postgres , Redis ) and distributed systems. Utilize advanced AI techniques like RAG , fine-tuning , LoRA , QLoRA , multi-model systems , and stable diffusion . Explore AI applications in video generation models ( text-to-video , image-to-video ). Skills Needed: Expertise in AI frameworks : Langchain , AutoGen , Streamlit . Strong experience with FastAPI , Flask , and cloud platforms ( AWS , Bedrock , Sagemaker ). Knowledge of third-party services ( OpenAI , Azure Foundry ) and tools ( RelevanceAI , Huggingface ). Solid experience with databases ( MongoDB , Postgres , Redis ). Proficient in machine learning , deep learning , NLP , and reinforcement learning . Hands-on experience with advanced AI techniques: RAG , fine-tuning , LoRA , QLoRA , SLM , LLM , stable diffusion , and video generation models .

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 Summary Director, Collection Platforms and AI As a director, you will be essential to drive customer satisfaction by delivering tangible business results to the customers. You will be working for the Enterprise Data Organization and will be an advocate and problem solver for the customers in your portfolio as part of the Collection Platforms and AI team. You will be using communication and problem-solving skills to support the customer on their automation journey with emerging automation tools to build and deliver end to end automation solutions for them. Team Collection Platforms and AI Enterprise Data Organizations objective is to drive growth across S&P divisions, enhance speed and productivity in our operations, and prepare our data estate for the future, benefiting our customers. Therefore, automation represents a massive opportunity to improve quality and efficiency, to expand into new markets and products, and to create customer and shareholder value. Agentic automation is the next frontier in intelligent process evolution, combining AI agents, orchestration layers, and cloud-native infrastructure to enable autonomous decision-making and task execution. To leverage the advancements in automation tools, its imperative to not only invest in the technologies but also democratize them, build literacy, and empower the work force. The Collection Platforms and AI team's mission is to drive this automation strategy across S&P Global and help create a truly digital workplace. We are responsible for creating, planning, and delivering transformational projects for the company using state of the art technologies and data science methods, developed either in house or in partnership with vendors. We are transforming the way we are collecting the essential intelligence our customers need to do decision with conviction, delivering it faster and at scale while maintaining the highest quality standards. What were looking for ? You will lead the design, development, and scaling of AI-driven agentic pipelines to transform workflows across S&P Global. This role requires a strategic leader who can architect end-to-end automation solutions using agentic frameworks, cloud infrastructure, and orchestration tools while managing senior stakeholders and driving adoption at scale. A visionary technical leader with knowledge of designing agentic pipelines and deploying AI applications in production environments. Understanding of cloud infrastructure (AWS/Azure/GCP), orchestration tools (e.g., Airflow, Kubeflow), and agentic frameworks (e.g., LangChain, AutoGen). Proven ability to translate business workflows into automation solutions, with emphasis on financial/data services use cases. An independent proactive person who is innovative, adaptable, creative, and detailed-oriented with high energy and a positive attitude. Exceptional skills in listening to clients, articulating ideas, and complex information in a clear and concise manner. Proven record of creating and maintaining strong relationships with senior members of client organizations, addressing their needs, and maintaining a high level of client satisfaction. Ability to understand what the right solution is for all type of problems, understanding and identifying the ultimate value of each project. Operationalize this technology across S&P Global, delivering scalable solutions that enhance efficiency, reduce latency, and unlock new capabilities for internal and external clients. Exceptional communication skills with experience presenting to C-level executives Responsibilities Engage with the multiple client areas (external and internal) and truly understand their problem and then deliver and support solutions that fit their needs. Understand the existing S&P Global product to leverage existing products as necessary to deliver a seamless end to end solution to the client. Evangelize agentic capabilities through workshops, demos, and executive briefings. Educate and spread awareness within the external client-base about automation capabilities to increase usage and idea generation. Increase automation adoption by focusing on distinct users and distinct processes. Deliver exceptional communication to multiple layers of management for the client. Provide automation training, coaching, and assistance specific to a users role. Demonstrate strong working knowledge of automation features to meet evolving client needs. Extensive knowledge and literacy of the suite of products and services offered through ongoing enhancements, and new offerings and how they fulfill customer needs. Establish monitoring frameworks for agent performance, drift detection, and self-healing mechanisms. Develop governance models for ethical AI agent deployment and compliance. Preferred Qualification 12+ years work experience with 5+ years in the Automation/AI space Knowledge of: Cloud platforms (AWS SageMaker, Azure ML; etc) Orchestration tools (Prefect, Airflow; etc) Agentic toolkits (LangChain, LlamaIndex, AutoGen) Experience in productionizing AI applications. Strong programming skills in python and common AI frameworks Experience with multi-modal LLMs and integrating vision and text for autonomous agents. Excellent written and oral communication in English Excellent presentation skills with a high degree of comfort speaking with senior executives, IT Management, and developers. Hands-on ability to build quick prototype/visuals to assist with high level product concepts and capabilities. Experience in deployment and management of applications utilizing cloud-based infrastructure. A desire to work in a fast-paced and challenging work environment Ability to work in a cross functional, multi geographic teams

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