91 Llmops Jobs - Page 3

You will be working full-time at Soothsayer Analytics in Hyderabad as a Generative AI/LLM Engineer. Your primary responsibility will be to design, develop, and deploy generative AI models using cutting-edge technologies like Azure OpenAI GPT-4, GPT-4 Vision, or GPT-4 Omni. You should have a strong background in building and deploying AI models, with a focus on leveraging technologies such as Retrieval-Augmented Generation (RAG) and working with Vector Databases. While experience in fine-tuning large language models (LLMs) is beneficial, it is not mandatory. You are expected to have a general understanding of training or fine-tuning deep learning models and be able to quickly learn and implement advanced techniques. Your key responsibilities will include designing, developing, and deploying generative AI models using GPT-4 variants, implementing and optimizing RAG techniques, building and managing AI services using Python frameworks like LangChain or LlamaIndex, and developing APIs with FastAPI or Quart for efficient integration. You will focus on scalability, performance, and optimization of AI solutions across cloud environments, particularly with Azure and AWS. Working with Vector Databases is mandatory, and experience with Graph Databases is optional. You will also utilize Cosmos DB and SQL for data storage and management solutions, apply MLOps or LLMOps practices, and manage Azure Pipelines for continuous integration and deployment. Additionally, you will continuously research and adopt the latest advancements in AI technologies. To qualify for this role, you should have a Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field, with at least 1+ years of experience in Generative AI/LLM technologies and 5+ years of experience in related fields. Proficiency in Python and experience with frameworks like LangChain, LlamaIndex, FastAPI, or Quart is required. Expertise in RAG and experience with Vector Databases are mandatory. Knowledge of Cosmos DB and SQL is also essential. Experience with fine-tuning LLMs and Graph Databases is beneficial but not mandatory. You should have proven experience in MLOps, LLMOps, or DevOps, a strong understanding of CI/CD processes, automation, and pipeline management, and familiarity with containers, Docker, or Kubernetes. Experience with cloud platforms, particularly Azure or AWS, and cloud-native AI services is desirable. Strong problem-solving abilities and a proactive approach to learning new AI trends and best practices quickly are necessary for this role.,

Role: Gen AI Engineer Exp: 5 to 8 yrs. Loc: Bangalore, Pune, Hyderabad NP: Immediate joiners, who can join in 30 days. Required Skills: Python, Large Language Models (LLM), Machine Learning (ML), Generative AI Required Candidate profile Looking for candidates working in Data Analytics companies preferably.

About the job: Key responsibilities: 1. Design and build AI-native backend systems (django) that power next-generation agentic workflows using Python and modular service architectures 2. Integrate Large Language Models (LLMs) into real-world applications using frameworks like LangGraph, AutoGen, and other orchestration tools 3. Develop autonomous agents capable of reasoning, tool use, and dynamic task execution across complex multi-step workflows 4. Work on the bleeding edge of AI automation, collaborating with researchers from institutions like Stanford and global AI innovation teams 5. Deploy and scale AI systems on the cloud (primarily AWS), ensuring high performance, security, and real-time responsiveness 6. Translate abstract prompts and product visions into intelligent, working systems from backend logic to toolchain integration 7. Take ownership from Day 1 you'll be trusted to build, break, and improve AI systems that are used in production Who can apply: Only those candidates can apply who: have minimum 1 years of experience are from Indore only are Computer Science Engineering students Salary: ₹ 4,50,000 - 8,00,000 /year Experience: 1 year(s) Deadline: 2025-08-17 23:59:59 Other perks: Informal dress code, 5 days a week Skills required: Python, Django, Machine Learning, Docker, GitHub, React, REST API, Backend development, Amazon Web Services (AWS), Deep Learning, Artificial intelligence, LangChain and LLMOps Other Requirements: 1. Think like builders, not just coders — you care about the why, not just the how 2. Have a strong command of Python and aren’t afraid to dive deep into new tools or frameworks 3. Are obsessed with AI — whether it’s LLMs, agentic systems, prompt design, or building autonomous workflows 4. Understand APIs, cloud (AWS), and can glue systems together like a pro 5. Learn fast, fail smart, and iterate even faster — AI moves quickly, and so do you 6. Love debugging, optimizing, and making systems cleaner and smarter, not just "getting it to work" 7. Enjoy working across layers — backend-first but ready to explore frontend or DevOps when needed 8. Want ownership, not tasks — you’ll help shape the product, not just follow tickets 9. Are curious about how autonomous agents think and act, and want to be part of building that future 10. Bring energy, initiative, and ideas to the table, not just skills About Company: Valutics helps organizations define and execute end-to-end strategies across AI, data, cloud, and business transformation. We design scalable architectures, build transformation roadmaps, and align every initiative to enterprise priorities. With our proprietary frameworks and accelerators, we drive faster execution, improved efficiency, and measurable outcomes. From AI strategy and platform engineering to agile delivery and optimization, we bring the structure and expertise to lead your most critical initiatives forward.

The Implementation Technical Architect role focuses on designing, developing, and deploying cutting-edge Generative AI (GenAI) solutions using the latest Large Language Models (LLMs) and frameworks. Your responsibilities include creating scalable and modular architecture for GenAI applications, leading Python development for GenAI applications, building tools for automated data curation, integrating solutions with cloud platforms like Azure, GCP, and AWS, applying advanced fine-tuning techniques to optimize LLM performance, establishing LLMOps pipelines, ensuring ethical AI practices, implementing Reinforcement Learning with Human Feedback and Retrieval-Augmented Generation techniques, collaborating with front-end developers, and more. 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. 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.,

At PwC, our data and analytics team focuses on utilizing data to drive insights and support informed business decisions. We leverage advanced analytics techniques to assist clients in optimizing their operations and achieving strategic goals. As a data analysis professional at PwC, your role will involve utilizing advanced analytical methods to extract insights from large datasets, enabling data-driven decision-making. Your expertise in data manipulation, visualization, and statistical modeling will be pivotal in helping clients solve complex business challenges. PwC US - Acceleration Center is currently seeking a highly skilled MLOps/LLMOps Engineer to play a critical role in deploying, scaling, and maintaining Generative AI models. This position requires close collaboration with data scientists, ML/GenAI engineers, and DevOps teams to ensure the seamless integration and operation of GenAI models within production environments at PwC and for our clients. The ideal candidate will possess a strong background in MLOps practices and a keen interest in Generative AI technologies. With a preference for candidates with 4+ years of hands-on experience, core qualifications for this role include: - 3+ years of experience developing and deploying AI models in production environments, alongside 1 year of working on proofs of concept and prototypes. - Proficiency in software development, including building and maintaining scalable, distributed systems. - Strong programming skills in languages such as Python and familiarity with ML frameworks like TensorFlow and PyTorch. - Knowledge of containerization and orchestration tools like Docker and Kubernetes. - Understanding of cloud platforms such as AWS, GCP, and Azure, including their ML/AI service offerings. - Experience with continuous integration and delivery tools like Jenkins, GitLab CI/CD, or CircleCI. - Familiarity with infrastructure as code tools like Terraform or CloudFormation. Key Responsibilities: - Develop and implement MLOps strategies tailored for Generative AI models to ensure robustness, scalability, and reliability. - Design and manage CI/CD pipelines specialized for ML workflows, including deploying generative models like GANs, VAEs, and Transformers. - Monitor and optimize AI model performance in production, utilizing tools for continuous validation, retraining, and A/B testing. - Collaborate with data scientists and ML researchers to translate model requirements into scalable operational frameworks. - Implement best practices for version control, containerization, and orchestration using industry-standard tools. - Ensure compliance with data privacy regulations and company policies during model deployment. - Troubleshoot and resolve issues related to ML model serving, data anomalies, and infrastructure performance. - Stay updated with the latest MLOps and Generative AI developments to enhance AI capabilities. Project Delivery: - Design and implement scalable deployment pipelines for ML/GenAI models to transition them from development to production environments. - Oversee the setup of cloud infrastructure and automated data ingestion pipelines to meet GenAI workload requirements. - Create detailed documentation for deployment pipelines, monitoring setups, and operational procedures. Client Engagement: - Collaborate with clients to understand their business needs and design ML/LLMOps solutions. - Present technical approaches and results to technical and non-technical stakeholders. - Conduct training sessions and workshops for client teams. - Create comprehensive documentation and user guides for clients. Innovation And Knowledge Sharing: - Stay updated with the latest trends in MLOps/LLMOps and Generative AI. - Develop internal tools and frameworks to accelerate model development and deployment. - Mentor junior team members and contribute to technical publications. Professional And Educational Background: - Any graduate / BE / B.Tech / MCA / M.Sc / M.E / M.Tech / Masters Degree / MBA,

As a highly skilled Senior Machine Learning Engineer, you will leverage your expertise in Deep Learning, Large Language Models (LLMs), and MLOps/LLMOps to design, optimize, and deploy cutting-edge AI solutions. Your responsibilities will include developing and scaling deep learning models, fine-tuning LLMs (e.g., GPT, Llama), and implementing robust deployment pipelines for production environments. You will be responsible for designing, training, fine-tuning, and optimizing deep learning models (CNNs, RNNs, Transformers) for various applications such as NLP, computer vision, or multimodal tasks. Additionally, you will fine-tune and adapt LLMs for domain-specific tasks like text generation, summarization, and semantic similarity. Experimenting with RLHF (Reinforcement Learning from Human Feedback) and alignment techniques will also be part of your role. In the realm of Deployment & Scalability (MLOps/LLMOps), you will build and maintain end-to-end ML pipelines for training, evaluation, and deployment. Deploying LLMs and deep learning models in production environments using frameworks like FastAPI, vLLM, or TensorRT is crucial. You will optimize models for low-latency, high-throughput inference and implement CI/CD workflows for ML systems using tools like MLflow and Kubeflow. Monitoring & Optimization will involve setting up logging, monitoring, and alerting for model performance metrics such as drift, latency, and accuracy. Collaborating with DevOps teams to ensure scalability, security, and cost-efficiency of deployed models will also be part of your responsibilities. The ideal candidate will possess 5-7 years of hands-on experience in Deep Learning, NLP, and LLMs. Strong proficiency in Python, PyTorch, TensorFlow, Hugging Face Transformers, and LLM frameworks is essential. Experience with model deployment tools like Docker, Kubernetes, and FastAPI, along with knowledge of MLOps/LLMOps best practices and familiarity with cloud platforms (AWS, GCP, Azure) are required qualifications. Preferred qualifications include contributions to open-source LLM projects, showcasing your commitment to advancing the field of machine learning.,

At PwC, we focus on leveraging data to drive insights and make informed business decisions in the field of data and analytics. Our team utilises advanced analytics techniques to help clients optimize their operations and achieve their strategic goals. As a Data Analyst at PwC, you will play a crucial role in utilizing advanced analytical techniques to extract insights from large datasets and facilitate data-driven decision-making. Your responsibilities will include leveraging skills in data manipulation, visualization, and statistical modeling to assist clients in solving complex business problems. We are currently seeking a highly skilled MLOps/LLMOps Engineer to join PwC US - Acceleration Center. In this role, you will be responsible for the deployment, scaling, and maintenance of Generative AI models. Working closely with data scientists, ML/GenAI engineers, and DevOps teams, you will ensure seamless integration and operation of GenAI models within production environments at PwC and our clients. The ideal candidate will have a strong background in MLOps practices, coupled with experience and interest in Generative AI technologies. As a candidate, you should have a minimum of 4+ years of hands-on experience. Core qualifications for this role include 3+ years of experience developing and deploying AI models in production environments, with a year of experience in developing proofs of concept and prototypes. Additionally, a strong background in software development, proficiency in programming languages like Python, knowledge of ML frameworks and libraries, familiarity with containerization and orchestration tools, and experience with cloud platforms and CI/CD tools are essential. Key responsibilities of the role involve developing and implementing MLOps strategies tailored for Generative AI models, designing and managing CI/CD pipelines specialized for ML workflows, monitoring and optimizing the performance of AI models in production, collaborating with data scientists and ML researchers, and ensuring compliance with data privacy regulations. You will also be responsible for troubleshooting and resolving issues related to ML model serving, data anomalies, and infrastructure performance. The successful candidate will be proficient in MLOps tools such as MLflow, Kubeflow, Airflow, or similar, have expertise in generative AI frameworks, containerization technologies, MLOps and LLMOps practices, and cloud-based AI services. Nice-to-have qualifications include experience with advanced GenAI applications, familiarity with experiment tracking tools, knowledge of high-performance computing techniques, and contributions to open-source MLOps or GenAI projects. In addition to technical skills, the role requires project delivery capabilities such as designing scalable deployment pipelines for ML/GenAI models, overseeing cloud infrastructure setup, and creating detailed documentation for deployment pipelines. Client engagement is another essential aspect, involving collaboration with clients to understand their business needs, presenting technical approaches and results, conducting training sessions, and creating user guides for clients. To stay ahead in the field, you will need to stay updated with the latest trends in MLOps/LLMOps and Generative AI, apply this knowledge to improve existing systems and processes, develop internal tools and frameworks, mentor junior team members, and contribute to technical publications. The ideal candidate for this position should hold any graduate/BE/B.Tech/MCA/M.Sc/M.E/M.Tech/Masters Degree/MBA. Join us at PwC and be part of a dynamic team driving innovation in data and analytics!,

About the Role We are seeking a highly skilled and experienced Senior AI Engineer to lead the design, development, and implementation of robust and scalable pipelines and backend systems for our Generative AI applications. In this role, you will be responsible for orchestrating the flow of data, integrating AI services, developing RAG pipelines, working with LLMs, and ensuring the smooth operation of the backend infrastructure that powers our Generative AI solutions. You will also be expected to apply modern LLMOps practices, handle schema-constrained generation, optimize cost and latency trade-offs, mitigate hallucinations, and ensure robust safety, personalization, and observability across GenAI systems. Responsibilities Generative AI Pipeline Development Design and implement scalable and modular pipelines for data ingestion, transformation, and orchestration across GenAI workloads. Manage data and model flow across LLMs, embedding services, vector stores, SQL sources, and APIs. Build CI/CD pipelines with integrated prompt regression testing and version control. Use orchestration frameworks like LangChain or LangGraph for tool routing and multi-hop workflows. Monitor system performance using tools like Langfuse or Prometheus. Data and Document Ingestion Develop systems to ingest unstructured (PDF, OCR) and structured (SQL, APIs) data. Apply preprocessing pipelines for text, images, and code. Ensure data integrity, format consistency, and security across sources. AI Service Integration Integrate external and internal LLM APIs (OpenAI, Claude, Mistral, Qwen, etc.). Build internal APIs for smooth backend-AI communication. Optimize performance through fallback routing to classical or smaller models based on latency or cost budgets. Use schema-constrained prompting and output filters to suppress hallucinations and maintain factual accuracy. Retrieval-Augmented Generation (RAG) Pipelines Build hybrid RAG pipelines using vector similarity (FAISS/Qdrant) and structured data (SQL/API). Design custom retrieval strategies for multi-modal or multi-source documents. Apply post-retrieval ranking using DPO or feedback-based techniques. Improve contextual relevance through re-ranking, chunk merging, and scoring logic. LLM Integration and Optimization Manage prompt engineering, model interaction, and tuning workflows. Implement LLMOps best practices: prompt versioning, output validation, caching (KV store), and fallback design. Optimize generation using temperature tuning, token limits, and speculative decoding. Integrate observability and cost-monitoring into LLM workflows. Backend Services Ownership Design and maintain scalable backend services supporting GenAI applications. Implement monitoring, logging, and performance tracing. Build RBAC (Role-Based Access Control) and multi-tenant personalization. Support containerization (Docker, Kubernetes) and autoscaling infrastructure for production. Required Skills and Qualifications Education Bachelors or Masters in Computer Science, Artificial Intelligence, Machine Learning, or related field. Experience 5+ years of experience in AI/ML engineering with end-to-end pipeline development. Hands-on experience building and deploying LLM/RAG systems in production. Strong experience with public cloud platforms (AWS, Azure, or GCP). Technical Skills Proficient in Python and libraries such as Transformers, SentenceTransformers, PyTorch. Deep understanding of GenAI infrastructure, LLM APIs, and toolchains like LangChain/LangGraph. Experience with RESTful API development and version control using Git. Knowledge of vector DBs (Qdrant, FAISS, Weaviate) and similarity-based retrieval. Familiarity with Docker, Kubernetes, and scalable microservice design. Experience with observability tools like Prometheus, Grafana, or Langfuse. Generative AI Specific Skills Knowledge of LLMs, VAEs, Diffusion Models, GANs. Experience building structured + unstructured RAG pipelines. Prompt engineering with safety controls, schema enforcement, and hallucination mitigation. Experience with prompt testing, caching strategies, output filtering, and fallback logic. Familiarity with DPO, RLHF, or other feedback-based fine-tuning methods. Soft Skills Strong analytical, problem-solving, and debugging skills. Excellent collaboration with cross-functional teams: product, QA, and DevOps. Ability to work in fast-paced, agile environments and deliver production-grade solutions. Clear communication and strong documentation practices. Preferred Qualifications Experience with OCR, document parsing, and layout-aware chunking. Hands-on with MLOps and LLMOps tools for Generative AI. Contributions to open-source GenAI or AI infrastructure projects. Knowledge of GenAI governance, ethical deployment, and usage controls. Experience with hallucination suppression frameworks like Guardrails.ai, Rebuff, or Constitutional AI. Shift Time: 2:30 PM to 11:30 PM IST Location-Remote,Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad

We're looking for experienced GenAI Engineers in the following areas: - Architect and implement RAG pipelines (Retrieval-Augmented Generation) - Prompt Engineering - Open-source and proprietary LLMs (like LLaMA, Mistral, Claude, GPT-4o, etc.) - Building AI Agents, MCP, A2A Tech Stack [any other similar to this is also ok]: - LangChain, LangFlow, LangGraph, LangSmith, Dify, AWS bedrock, sageworks, GenAI, - Vector DBs [Postgres, Pinecone, etc] - Agents [A2A] SDK [google, others] and any Agentic AI platform - CrewAI, Cursor, Windsurf, etc

We are looking for a Machine Learning Engineer with expertise in MLOps (Machine Learning Operations) or LLMOps (Large Language Model Operations) to design, deploy, and maintain scalable AI/ML systems. You will work on automating ML workflows, optimizing model deployment, and managing large-scale AI applications, including LLMs (Large Language Models) , ensuring they run efficiently in production. Key Responsibilities: Design and implement end-to-end MLOps pipelines for training, validation, deployment, monitoring, and retraining of ML models. Optimize and fine-tune large language models (LLMs) for various applications, ensuring performance and efficiency. Develop CI/CD pipelines for ML models to automate deployment and monitoring in production. Monitor model performance, detect drift , and implement automated retraining mechanisms. Work with cloud platforms ( AWS, GCP, Azure ) and containerization technologies ( Docker, Kubernetes ) for scalable deployments. Implement best practices in data engineering , feature stores, and model versioning. Collaborate with data scientists, engineers, and product teams to integrate ML models into production applications. Ensure compliance with security, privacy, and ethical AI standards in ML deployments. Optimize inference performance and cost of LLMs using quantization, pruning, and distillation techniques . Deploy LLM-based APIs and services, integrating them with real-time and batch processing pipelines. Key Requirements: Technical Skills: Strong programming skills in Python, with experience in ML frameworks ( TensorFlow, PyTorch, Hugging Face, JAX ). Experience with MLOps tools (MLflow, Kubeflow, Vertex AI, SageMaker, Airflow). Deep understanding of LLM architectures , prompt engineering, and fine-tuning. Hands-on experience with containerization (Docker, Kubernetes) and orchestration tools . Proficiency in cloud services (AWS/GCP/Azure) for ML model training and deployment. Experience with monitoring ML models (Prometheus, Grafana, Evidently AI). Knowledge of feature stores (Feast, Tecton) and data pipelines (Kafka, Apache Beam). Strong background in distributed computing (Spark, Ray, Dask) . Soft Skills: Strong problem-solving and debugging skills. Ability to work in cross-functional teams and communicate complex ML concepts to stakeholders. Passion for staying updated with the latest ML and LLM research & technologies . Preferred Qualifications: Experience with LLM fine-tuning , Reinforcement Learning with Human Feedback ( RLHF ), or LoRA/PEFT techniques . Knowledge of vector databases (FAISS, Pinecone, Weaviate) for retrieval-augmented generation ( RAG ). Familiarity with LangChain, LlamaIndex , and other LLMOps-specific frameworks. Experience deploying LLMs in production (ChatGPT, LLaMA, Falcon, Mistral, Claude, etc.) .

MSCI Services is looking for a Senior AI Engineer to join our AI team. In this role, you will design and develop highly scalable, robust systems that drive our AI initiatives and data operations. Success depends on strong software engineering skills, familiarity with large-scale distributed systems, and expertise in AI technologies. Our ideal candidate has a proven ability to build reliable platforms (rather than standalone applications) and to iterate effectively over multiple release cycles. Throughout your work, you'll apply best practices in software engineering and system architecture. If you're passionate about delivering high-impact AI solutions in a dynamic environment, this is an exciting opportunity to have a substantial influence on our AI capabilities through your expertise. Responsibilities: Design and implement scalable distributed systems . Architect solutions that can handle large volumes of data for real-time and batch processing. Design and develop efficient AI pipelines with automation and reliability across the platform. Integrate agentic workflows and AI agents into data extraction processes, and enable systems to perform multi-step reasoning and tool usage to improve accuracy and efficiency of data extraction. Deploy, monitor, and maintain LLM-based extraction systems in production , ensuring reliability and scalability. Set up appropriate monitoring, logging, and evaluation metrics to track performance, and perform continual tuning and improvements based on human-in-the-loop feedback. Conduct applied research and experimentation with the latest generative AI models and techniques to enhance extraction capabilities. Prototype new approaches and iterate quickly to integrate successful methods into the production pipeline. Collaborate with cross-functional teams (data engineers, product managers, domain experts) to gather requirements and align AI solutions with business needs. Qualifications: Experience in applied AI or machine learning engineering , with a track record of building and deploying AI solutions (especially in NLP). Hands-on experience with using Generative AI models and APIs/frameworks (e.g., OpenAI GPT-4, Google Gemini). Ability to build Agentic AI systems where LLMs interact with tools or perform multi-step workflows. Proficiency in Python (preferred) and experience deploying machine learning models or pipelines at scale. Good understanding of embeddings, LLM models , and experience with retrieval-augmented generation (RAG) workflows to incorporate external knowledge into LLM-based systems. Knowledge of LLMOps and cloud services (Azure, GCP, or similar) for deploying and managing AI solutions. Experience with containerization, orchestration, and monitoring of ML models in a production cloud environment. Excellent collaboration and communication skills , with the ability to work effectively in a team, translate complex technical concepts to non-technical stakeholders, and document work clearly.

MSCI Services is looking for a Senior AI Engineer to join our AI team. In this role, you will design and develop highly scalable, robust systems that drive our AI initiatives and data operations. Success depends on strong software engineering skills, familiarity with large-scale distributed systems, and expertise in AI technologies. Our ideal candidate has a proven ability to build reliable platforms (rather than standalone applications) and to iterate effectively over multiple release cycles. Throughout your work, you'll apply best practices in software engineering and system architecture. If you're passionate about delivering high-impact AI solutions in a dynamic environment, this is an exciting opportunity to have a substantial influence on our AI capabilities through your expertise. Responsibilities: Design and implement scalable distributed systems . Architect solutions that can handle large volumes of data for real-time and batch processing. Design and develop efficient AI pipelines with automation and reliability across the platform. Integrate agentic workflows and AI agents into data extraction processes, and enable systems to perform multi-step reasoning and tool usage to improve accuracy and efficiency of data extraction. Deploy, monitor, and maintain LLM-based extraction systems in production , ensuring reliability and scalability. Set up appropriate monitoring, logging, and evaluation metrics to track performance, and perform continual tuning and improvements based on human-in-the-loop feedback. Conduct applied research and experimentation with the latest generative AI models and techniques to enhance extraction capabilities. Prototype new approaches and iterate quickly to integrate successful methods into the production pipeline. Collaborate with cross-functional teams (data engineers, product managers, domain experts) to gather requirements and align AI solutions with business needs. Qualifications: Experience in applied AI or machine learning engineering , with a track record of building and deploying AI solutions (especially in NLP). Hands-on experience with using Generative AI models and APIs/frameworks (e.g., OpenAI GPT-4, Google Gemini). Ability to build Agentic AI systems where LLMs interact with tools or perform multi-step workflows. Proficiency in Python (preferred) and experience deploying machine learning models or pipelines at scale. Good understanding of embeddings, LLM models , and experience with retrieval-augmented generation (RAG) workflows to incorporate external knowledge into LLM-based systems. Knowledge of LLMOps and cloud services (Azure, GCP, or similar) for deploying and managing AI solutions. Experience with containerization, orchestration, and monitoring of ML models in a production cloud environment. Excellent collaboration and communication skills , with the ability to work effectively in a team, translate complex technical concepts to non-technical stakeholders, and document work clearly.

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.

Role: Python with Gen AI Developer Exp: 7+yrs Budget: Max 28 LPA Location: Bangalore (Hybrid) Must Have: Python libraries and frameworks,Generative AI, Prompt Engineering. Immediate to 30 Days. Ragul 8428065584

About the Role We are seeking a highly skilled and experienced Senior AI Engineer to lead the design, development, and implementation of robust and scalable pipelines and backend systems for our Generative AI applications. In this role, you will be responsible for orchestrating the flow of data, integrating AI services, developing RAG pipelines, working with LLMs, and ensuring the smooth operation of the backend infrastructure that powers our Generative AI solutions. You will also be expected to apply modern LLMOps practices, handle schema-constrained generation, optimize cost and latency trade-offs, mitigate hallucinations, and ensure robust safety, personalization, and observability across GenAI systems. Responsibilities Generative AI Pipeline Development Design and implement scalable and modular pipelines for data ingestion, transformation, and orchestration across GenAI workloads. Manage data and model flow across LLMs, embedding services, vector stores, SQL sources, and APIs. Build CI/CD pipelines with integrated prompt regression testing and version control. Use orchestration frameworks like LangChain or LangGraph for tool routing and multi-hop workflows. Monitor system performance using tools like Langfuse or Prometheus. Data and Document Ingestion Develop systems to ingest unstructured (PDF, OCR) and structured (SQL, APIs) data. Apply preprocessing pipelines for text, images, and code. Ensure data integrity, format consistency, and security across sources. AI Service Integration Integrate external and internal LLM APIs (OpenAI, Claude, Mistral, Qwen, etc.). Build internal APIs for smooth backend-AI communication. Optimize performance through fallback routing to classical or smaller models based on latency or cost budgets. Use schema-constrained prompting and output filters to suppress hallucinations and maintain factual accuracy. Retrieval-Augmented Generation (RAG) Pipelines Build hybrid RAG pipelines using vector similarity (FAISS/Qdrant) and structured data (SQL/API). Design custom retrieval strategies for multi-modal or multi-source documents. Apply post-retrieval ranking using DPO or feedback-based techniques. Improve contextual relevance through re-ranking, chunk merging, and scoring logic. LLM Integration and Optimization Manage prompt engineering, model interaction, and tuning workflows. Implement LLMOps best practices: prompt versioning, output validation, caching (KV store), and fallback design. Optimize generation using temperature tuning, token limits, and speculative decoding. Integrate observability and cost-monitoring into LLM workflows. Backend Services Ownership Design and maintain scalable backend services supporting GenAI applications. Implement monitoring, logging, and performance tracing. Build RBAC (Role-Based Access Control) and multi-tenant personalization. Support containerization (Docker, Kubernetes) and autoscaling infrastructure for production. Required Skills and Qualifications Education Bachelors or Masters in Computer Science, Artificial Intelligence, Machine Learning, or related field. Experience 5+ years of experience in AI/ML engineering with end-to-end pipeline development. Hands-on experience building and deploying LLM/RAG systems in production. Strong experience with public cloud platforms (AWS, Azure, or GCP). Technical Skills Proficient in Python and libraries such as Transformers, SentenceTransformers, PyTorch. Deep understanding of GenAI infrastructure, LLM APIs, and toolchains like LangChain/LangGraph. Experience with RESTful API development and version control using Git. Knowledge of vector DBs (Qdrant, FAISS, Weaviate) and similarity-based retrieval. Familiarity with Docker, Kubernetes, and scalable microservice design. Experience with observability tools like Prometheus, Grafana, or Langfuse. Generative AI Specific Skills Knowledge of LLMs, VAEs, Diffusion Models, GANs. Experience building structured + unstructured RAG pipelines. Prompt engineering with safety controls, schema enforcement, and hallucination mitigation. Experience with prompt testing, caching strategies, output filtering, and fallback logic. Familiarity with DPO, RLHF, or other feedback-based fine-tuning methods. Soft Skills Strong analytical, problem-solving, and debugging skills. Excellent collaboration with cross-functional teams: product, QA, and DevOps. Ability to work in fast-paced, agile environments and deliver production-grade solutions. Clear communication and strong documentation practices. Preferred Qualifications Experience with OCR, document parsing, and layout-aware chunking. Hands-on with MLOps and LLMOps tools for Generative AI. Contributions to open-source GenAI or AI infrastructure projects. Knowledge of GenAI governance, ethical deployment, and usage controls. Experience with hallucination suppression frameworks like Guardrails.ai, Rebuff, or Constitutional AI. Experience and Shift Shift Time: 2:30 PM to 11:30 PM IST Location: Remote- Bengaluru,Hyderabad,Delhi / NCR,Chennai,Pune,Kolkata,Ahmedabad,Mumbai

Mandatory Skills & Experience : - Expertise in designing and optimizing machine-learning operations, with a preference for LLM Ops. - Proficient in Data Science, Machine Learning, Python, SQL, Linux/Unix shell scripting. - Experience on Large Language Models and Natural Language Processing (NLP), and experience with researching, training, and fine-tuning LLMs. - Contribute towards fine-tune Transformer models for optimal performance in NLP tasks, if required. - Implement and maintain automated testing and deployment processes for machine learning models w.r.t LLMOps. - Implement version control, CI/CD pipelines, and containerization techniques to streamline ML and LLM workflows. - Develop and maintain robust monitoring and alerting systems for generative AI models ensuring proactive identification and resolution of issues. - Research or engineering experience in deep learning with one or more of the following : generative models, segmentation, object detection, classification, model optimisations. - Experience implementing RAG frameworks as part of available-ready products. - Experience in setting up the infrastructure for the latest technology such as Kubernetes, Serverless, Containers, Microservices etc. - Experience in scripting/programming to automate deployments and testing, working on tools like Terraform and Ansible. - Scripting languages like Python, bash, YAML etc. - Experience on CI/CD opensource and enterprise tool sets such as Argo CD, and Jenkins (others like Jenkins X, Circle CI, Argo CD, Tekton, Travis, Concourse an advantage). - Experience with the GitHub/DevOps Lifecycle. - Experience in Observability solutions (Prometheus, EFK stacks, ELK stacks, Grafana, Dynatrace, AppDynamics). - Experience in at-least one of the clouds for example Azure/AWS/GCP. - Significant experience on microservices-based, container-based or similar modern approaches of applications and workloads. - You have exemplary verbal and written communication skills (English). - Able to interact and influence at the highest level, you will be a confident presenter and speaker, able to command the respect of your audience. Desired Skills & Experience : - Bachelor level technical degree or equivalent experience; Computer Science, Data Science, or Engineering background preferred; Master's Degree desired. - Experience in LLM Ops or related areas, such as DevOps, data engineering, or ML infrastructure. - Hands-on experience in deploying and managing machine learning and large language model pipelines in cloud platforms (i.e., AWS, Azure) for ML workloads. - Familiar with data science, machine learning, deep learning, and natural language processing concepts, tools, and libraries such as Python, TensorFlow, PyTorch, NLTK etc. - Experience in using retrieval augmented generation and prompt engineering techniques to improve the model's quality and diversity to improve operations efficiency. - Proven experience in developing and fine-tuning Language Models (LLMs). - Stay up-to-date with the latest advancements in Generative AI, conduct research, and explore innovative techniques to improve model quality and efficiency. - The perfect candidate will already be working within a System Integrator, Consulting or Enterprise organisation with 8+ years of experience in a technical role within the Cloud domain. - Deep understanding of core practices including SRE, Agile, Scrum, XP and Domain Driven Design. - Familiarity with the CNCF open-source community. - Enjoy working in a fast-paced and dynamic environment using the latest technologies.

Job Description Summary role description: Hiring for a Solution Architect for an InsurTech platform provider, Life and Health Insurance. Company description: Our client is a VC-funded InsurTech platform company, providing software platforms for Life Insurance and Health Insurance companies across the globe. Leveraging their domain expertise, regulatory knowledge and technology experience, they architect innovative products and disrupt the Insurance value chain from Customer Acquisition to Engagement. Their products serve customers across the APAC region. Role details: Title / Designation : Solutions Architect Location: Pune/Mumbai Work Mode: Work from office Role & responsibilities: Define and evolve AI/ML architecture roadmap for FWA, IDP, and Agentic AI frameworks. Lead technical presentations and solution design sessions with customers. Design scalable architectures for multi-agent systems and autonomous decision-making. Drive innovation by evaluating emerging AI/ML technologies, especially AI agents. Architect cloud-native platforms supporting the complete AI/ML lifecycle. Provide technical leadership across product development and customer implementation. Collaborate with data scientists, engineers, and business stakeholders. Stay at the forefront of AI/ML innovations, particularly autonomous agents and LLMs. Establish and enforce technical standards and architectural guidelines. Candidate requirements: 10+ years in software architecture/system design in insurance domain, with 5+ years in AI/ML systems/platforms. Proven experience delivering large-scale AI/ML solutions, preferably with autonomous agents. Experience with cloud-native architectures (AWS, Azure, GCP), containerization (Docker, Kubernetes), and microservices. Deep expertise in AI/ML system architecture (model serving, MLOps/LLMOps pipelines, distributed computing). Strong understanding of Agentic AI, multi-agent systems, and LLMs (including LoRA, PEFT fine-tuning). Bachelor's or Master's in CS, SE, Data Science, or related technical field. Exceptional technical leadership and communication skills. Selection process: Interview with Senior Solution Architect Interview with CTO HR Discussion Check Your Resume for Match Upload your resume and our tool will compare it to the requirements for this job like recruiters do.

Job Description : As an LLMOps Engineer, you will play a crucial role in the deployment, maintenance, and optimization of large language models (LLMs). Your responsibilities will span the entire lifecycle of LLMs, from initial deployment to ongoing operations, ensuring optimal performance, scalability, and reliability. Key Responsibilities : LLM Deployment and Integration - Deploy and integrate large language models into production environments, ensuring seamless integration with existing systems and applications. Infrastructure Planning and Scaling - Collaborate with cross-functional teams to plan and design the infrastructure required for LLM deployment. Implement scalable solutions to accommodate growing data volumes and user loads. Automation of Deployment Processes - Develop and maintain automation scripts and tools for efficient deployment, scaling, and versioning of LLMs. Streamline deployment processes to minimize downtime. Continuous Monitoring and Alerting - Implement monitoring systems to track LLM performance metrics. Set up alerts for potential issues and respond promptly to ensure uninterrupted service. Performance Monitoring and Optimization - Monitor the performance of LLMs in real-time, conduct regular assessments, and implement optimizations to enhance efficiency and responsiveness. Fault Tolerance and Disaster Recovery - Design and implement fault-tolerant systems for LLMs, incorporating strategies such as redundancy, sharding, and replication. Develop and maintain disaster recovery plans. Security Measures Implementation - Implement robust security measures to safeguard LLMs and associated data. Ensure compliance with data security regulations and industry standards. Collaboration with NLP Engineers and Data Scientists - Collaborate with NLP (Natural Language Processing) engineers and data scientists to understand model requirements and implement necessary infrastructure adjustments. Skills & Tools Infrastructure as Code (IaC) - Experience with IaC tools such as Terraform or Ansible for automating infrastructure provisioning. Containerization and Orchestration - Proficiency in containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) for managing LLM deployments. Cloud Platforms - Familiarity with cloud platforms such as AWS (Bedrock), Azure, or GCP, and experience in deploying and managing applications in a cloud environment. Monitoring and Logging Tools - Knowledge of monitoring tools (e.g., Prometheus, Grafana) and logging systems (e.g., ELK stack) for real-time performance monitoring and analysis. Security Measures - Understanding of security guardrails using tools like LLM Guard and familiarity of how to mask / redact / obfuscate sensitive data, protect the input and output of toxic and harmful content to / from LLMs and understand the performance implications of the same. Scripting and Automation - Proficient in scripting languages such as Python, Shell, or similar, and experience in automating deployment and maintenance processes.

Role: AIML Engineer Experience: 2 to 8 Yrs Notice period: 0- 10 Days Job Location: Hyderabad and Bangalore. (Hybrid) Job Description: Strong proficiency in Python for AI/ML development. Hands-on experience with OpenAI, GPT models, and LangChain or LlamaIndex. Deep understanding of Retrieval-Augmented Generation (RAG) concepts and implementations. Familiarity with vector databases like FAISS, Pinecone, or Weaviate. Good knowledge of data engineering principles (ETL, data modeling, batch/streaming pipelines). Experience with cloud platforms (Azure, AWS, or GCP) for deploying AI/ML solutions. Proficient in tools like Pandas, NumPy, Scikit-learn, and MLflow. Exposure to LLMOps, model versioning, and monitoring is a plus.

About the job: As a Full Stack GenAI Developer at MeetMinutes, you will be responsible for creating cutting-edge AI solutions using Python, Generative AI Development, LangChain, LLM evaluation, LLMOps, JavaScript, React, Amazon Web Services (AWS), Google Cloud Platforms (GCP), Docker, Machine Learning, Natural Language Processing (NLP), PostgreSQL, REST API, FastAPI, GitHub, System Design, and Prompt Engineering. Key responsibilities: 1. Developing and implementing AI algorithms and models to enhance the functionality of our platform. 2. Integrating AI technologies and features into our existing systems to improve user experience. 3. Collaborating with the engineering team to optimize system performance and scalability. 4. Building and maintaining RESTful APIs for seamless communication between different components. 5. Monitoring and troubleshooting any issues related to AI functionality and recommending solutions. 6. Contributing to the overall architecture and design of our AI-driven products. 7. Staying updated on the latest trends and advancements in AI and actively participating in knowledge sharing within the team. If you are passionate about using AI to revolutionize meeting productivity and collaboration, and have a strong background in full-stack development and AI technologies, we'd love to have you join our team! Who can apply: Only those candidates can apply who: have minimum 1 years of experience are Computer Science Engineering students Salary: ₹ 6,50,000 /year Experience: 1 year(s) Deadline: 2025-07-13 23:59:59 Other perks: 5 days a week Skills required: JavaScript, Python, Machine Learning, PostgreSQL, Docker, GitHub, React, REST API, Amazon Web Services (AWS), Natural Language Processing (NLP), Google Cloud Platforms (GCP), FastAPI, Generative AI Development, LangChain, Prompt Engineering, System Design, LLMOps and LLM evaluation About Company: A platform for the future of AI meeting productivity. It is a tool for professionals having conversations in mixed Indian languages and need workflow automations. It has support for Google Meet, MS Teams, and many more meeting platforms. It is a startup recognized by the Ministry of IT and won several accolades working with businesses, IT firms, agencies, listed companies, and SMBs.

Perform performance evaluations of the LLM models, ImplementLLMOpsprocesses to run the end-to-end lifecycle of LLMs Deploy,monitor, andmaintainMachine Learning models and build AI Products in production environments, ensuringoptimalperformance and reliability. Ensure high code quality, performance, and reliability through rigorous testing, code reviews, and adherence to software development best practices. Drive innovation by researching and incorporatingstate-of-the-artmachine learning techniques, tools, and frameworks into the platform. Effective communication, listening, interpersonal, influencing, and alignment driving skills; able to convey important messages in a clear and compelling manner Mentor team members,providetechnical guidance, and foster a culture of collaboration, innovation, and continuous learning. What do you need to bring Qualifications Masters / bachelor s in computer science, Computer engineering, Machine Learning, Data Mining, Information Systems, or related disciplines, with technicalexpertisein one or more of the above-mentioned areas or equivalent practical experience. Strong background in deep learning techniques, particularly in NLP and Vision Expertisein applying LLMs, prompt design, and fine-tuning methods Strongproficiencyin machine learning concepts, algorithms, and techniques, with hands-on experience in developing and deploying machine learning models. Expert in multiple Programming/scripting languages,i.e.,Python, Java, Scala, SQL, NoSQL (like HBase, Redis, Aerospike) Good understanding of distributed systems, data streaming, complex event Processing,NoSQLsolutions for creating and managing data integration pipelines for batch and Real Time data needs. Expertisein machine learning libraries/frameworks such as TensorFlow,PyTorch, scikit-learn,etc. Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes). Experience in Azure is a plus Stay up to date with the latest advancements in AI/ML technology and industry trends andleveragethis knowledge to enhance the platforms capabilities Strong communication, listening, interpersonal, influencing, and alignment driving skills; able to convey important messages in a clear and compelling manner Expertisein Big Data technologies such as Hadoop, Spark, HBase, Kafka. Preferred Prior experience in Content Understanding, enrichment, entity resolution or knowledge graph Experience developing Gen AI applications/services for sophisticated business use cases andlarge amountsof unstructured data. Strong background in MLOps and experimentation frameworks

Role - Senior Data Scientist / Senior Gen AI Engineer Exp Range - 8 to 18 yrs Position - Permanent Fulltime Company - Data Analytics & AIML MNC Location - Hyderabad, Pune, Bangalore (Relocation accepted) About the Role: We are seeking a Software Engineer with expertise in Generative AI and Microsoft technologies to design, develop, and deploy AI-powered solutions using the Microsoft ecosystem. You will work with cross-functional teams to build scalable applications leveraging generative AI models and Azure services. Skills Required: Experience with Large Language Models (LLMs) like GPT, LLaMA, Claude, etc. Proficiency in Python for building and fine-tuning AI/ML models Familiarity with LangChain , LLMOps , or RAG (Retrieval-Augmented Generation) pipelines Experience with Vector Databases (e.g. FAISS, Pinecone, Weaviate) Knowledge of Prompt Engineering and model evaluation techniques Exposure to cloud platforms (Azure, AWS or GCP) for deploying GenAI solutions Preferred Skills: Experience with Azure OpenAI , Databricks or Microsoft Fabric Hands-on with Hugging Face Transformers , OpenAI APIs or custom model training

Position Summary LLMOps(Large language model operations) Engineer will play a pivotal role in building and maintaining the infrastructure and pipelines for our cutting-edge Generative AI applications, establishing efficient and scalable systems for LLM research, evaluation, training, and fine-tuning. Engineer will be responsible for managing and optimizing large language models (LLMs) across various platforms This position is uniquely tailored for those who excel in crafting pipelines, cloud infrastructure, environments, and workflows. Your expertise in automating and streamlining the ML lifecycle will be instrumental in ensuring the efficiency, scalability, and reliability of our Generative AI models and associated platform. LLMOps engineers expertise will ensure the smooth deployment, maintenance, and performance of these AI platforms and powerful large language models. You will follow Site Reliability Engineering & MLOps principles and will be encouraged to contribute your own best practices and ideas to our ways of working. Reporting to the Head of Cloud Native operations, you will be an experienced thought leader, and comfortable engaging senior managers and technologists. You will engage with clients, display technical leadership, and guide the creation of efficient and complex products/solutions. Key Responsibilities Technical & Architectural Leadership Contribute to the technical delivery of projects, ensuring a high quality of work that adheres to best practices, brings innovative approaches and meets client expectations. Project types include following (but not limited to): Solution architecture, Proof of concepts (PoCs), MVP, design, develop, and implementation of ML/LLM pipelines for generative AI models, data management & preparation for fine tuning, training, deployment, and monitoring. Automate ML tasks across the model lifecycle. Contribute to HCL thought leadership across the Cloud Native domain with an expert understanding of advanced AI solutions using Large Language Models (LLM) & Natural Language Processing (NLP) techniques and partner technologies. Collaborate with cross-functional teams to integrate LLM and NLP technologies into existing systems. Ensure the highest levels of governance and compliance are maintained in all ML and LLM operations. Stay abreast of the latest developments in ML and LLM technologies and methodologies, integrating these innovations to enhance operational efficiency and model effectiveness. Collaborate with global peers from partner ecosystems on joint technical projects. This partner ecosystem includes Google, Microsoft, Nvidia, AWS, IBM, Red Hat, Intel, Cisco, and Dell VMware etc. Service Delivery Provide a technical hands-on contribution. Create scalable infra to support enterprise loads (distributed GPU compute, foundation models, orchestrating across multiple cloud vendors, etc.) Ensuring the reliable and efficient platform operations. Apply data science, machine learning, deep learning, and natural language processing methods to analyse, process, and improve the models data and performance. Understanding of Explainability & Biased Detection concepts. Create and optimize prompts and queries for retrieval augmented generation and prompt engineering techniques to enhance the models capabilities and user experience w.r.t Operations & associated platforms. Client-facing influence and guidance, engaging in consultative client discussions and performing a Trusted Advisor role. Provide effective support to HCL Sales and Delivery teams. Support sales pursuits and enable HCL revenue growth. Define the modernization strategy for client platform and associated IT practices, create solution architecture and provide oversight of the client journey. Innovation & Initiative Always maintain hands-on technical credibility, keep in front of the industry, and be prepared to show and lead the way forward to others. Engage in technical innovation and support HCLs position as an industry leader. Actively contribute to HCL sponsorship of leading industry bodies such as the CNCF and Linux Foundation. Contribute to thought leadership by writing Whitepapers, blogs, and speaking at industry events. Be a trusted, knowledgeable internal innovator driving success across our global workforce. Client Relationships Advise on best practices related to platform & Operations engineering and cloud native operations, run client briefings and workshops, and engage technical leaders in a strategic dialogue. Develop and maintain strong relationships with client stakeholders. Perform a Trusted Advisor role. Contribute to technical projects with a strong focus on technical excellence and on-time delivery. Mandatory Skills & Experience Expertise in designing and optimizing machine-learning operations, with a preference for LLMOps. Proficient in Data Science, Machine Learning, Python, SQL, Linux/Unix shell scripting. Experience on Large Language Models and Natural Language Processing (NLP), and experience with researching, training, and fine-tuning LLMs. Contribute towards fine-tune Transformer models for optimal performance in NLP tasks, if required. Implement and maintain automated testing and deployment processes for machine learning models w.r.t LLMOps. Implement version control, CI/CD pipelines, and containerization techniques to streamline ML and LLM workflows. Develop and maintain robust monitoring and alerting systems for generative AI models ensuring proactive identification and resolution of issues. Research or engineering experience in deep learning with one or more of the following: generative models, segmentation, object detection, classification, model optimisations. Experience implementing RAG frameworks as part of available-ready products. Experience in setting up the infrastructure for the latest technology such as Kubernetes, Serverless, Containers, Microservices etc. Experience in scripting programming to automate deployments and testing, worked on tools like Terraform and Ansible. Scripting languages like Python, bash, YAML etc. Experience on CI/CD opensource and enterprise tool sets such as Argo CD, Jenkins. Experience with the GitHub/DevOps Lifecycle Experience in at least one of the Observability solutions (Prometheus, EFK stacks, ELK stacks, Grafana, Dynatrace, AppDynamics) Experience in at-least one of the clouds for example - Azure/AWS/GCP Significant experience on microservices-based, container-based or similar modern approaches of applications and workloads. You have exemplary verbal and written communication skills (English). Able to interact and influence at the highest level, you will be a confident presenter and speaker, able to command the respect of your audience. Desired Skills & Experience Bachelor level technical degree or equivalent experience; Computer Science, Data Science, or Engineering background preferred; masters degree desired. Experience in LLMOps or related areas, such as DevOps, data engineering, or ML infrastructure. Hands-on experience in deploying and managing machine learning and large language model pipelines in cloud platforms (e.g., AWS, Azure) for ML workloads. Familiar with data science, machine learning, deep learning, and natural language processing concepts, tools, and libraries such as Python, TensorFlow, PyTorch, NLTK etc. Experience in using retrieval augmented generation and prompt engineering techniques to improve the models quality and diversity to improve operations efficiency. Proven experience in developing and fine-tuning Language Models (LLMs). Stay up-to-date with the latest advancements in Generative AI, conduct research, and explore innovative techniques to improve model quality and efficiency. The perfect candidate will already be working within a System Integrator, Consulting or Enterprise organisation with 8+ years of experience in a technical role within the Cloud domain. Deep understanding of core practices including SRE, Agile, Scrum, XP and Domain Driven Design. Familiarity with the CNCF open-source community. Enjoy working in a fast-paced environment using the latest technologies, love Labs dynamic and high-energy atmosphere, and want to build your career with an industry leader.

AI Ops Senior Architect 12 -17 Years Work Location - Pune/ Bengaluru/Hyderabad/Chennai/ Gurugram Tredence is Data science, engineering, and analytics consulting company that partners with some of the leading global Retail, CPG, Industrial and Telecom companies. We deliver business impact by enabling last mile adoption of insights by uniting our strengths in business analytics, data science and data engineering. Headquartered in the San Francisco Bay Area, we partner with clients in US, Canada, and Europe. Bangalore is our largest Centre of Excellence with skilled analytics and technology teams serving our growing base of Fortune 500 clients. JOB DESCRIPTION At Tredence, you will lead the evolution of Industrializing AI ” solutions for our clients by implementing ML/LLM/GenAI & Agent Ops best practices. You will lead the Architecture , Design & development of large scale ML/LLMOps platforms for our clients. You’ll build and maintain tools for deployment, monitoring, and operations. You’ll be a trusted advisor to our clients in ML/GenAI/Agent Ops space & coach to the ML engineering practitioners to build effective solutions to Industrialize AI solutions THE IDEAL CANDIDATE WILL BE RESPONSIBLE FOR AI Ops Strategy, Innovation, Research and Technical Standards 1. Conduct research and experiment with emerging AI Ops technologies and trends. Create POV’s, POC’s & present Proof of Technology to use latest tools, Technologies & services from Hyper scalers focussed on ML, GenAI & Agent Ops 2. Define and propose new technical standards and best practices for the organization's AI Ops environment. 3. Lead the evaluation and adoption of innovative MLOps solutions to address critical business challenges. 4. Conduct meet ups, attend & present in Industry events, conferences, etc 5. Ideate & develop accelerators to strengthen service offerings of AI Ops practice Solution Design & Architectural Development 6. Lead Design & architecture of scalable model training & deployment pipelines for large-scale deployments 7. Architect & Design large scale ML & GenAI Ops platforms 8. Collaborate with Data science & GenAI practice to define and implement strategies of AI solutions for model explainability and interpretability 9. Mentor and guide senior architects in crafting cutting-edge AI Ops solutions 10. Lead architecture reviews and identify opportunities for significant optimizations and improvements. Documentation and Best Practices 11. Develop and maintain comprehensive documentation of AIOps architectures designs and best practices. 12. Lead the development and delivery of training materials and workshops on AIOps tools and techniques. 13. Actively participate in sharing knowledge and expertise with the MLOps team through internal presentations and code reviews. Qualifications and Skills: 1. Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field with minimum 12 years of experience 2. Proven experience in architecting & developing AIOps solutions – to streamline Machine Learning & GenAI development lifecycle 3. Proven experience as an AI Ops Architect – ML & GenAI in architecting & design of ML & GenAI platforms 4. Hands on experience in Model deployment strategies, Designing ML & GenAI model pipelines to scale in production, Model Observability techniques used to monitor performance of ML & LLM’s 5. Strong coding skills with experience in implementing best coding practices Technical Skills & Expertise Python, PySpark, PyTorch ,Java, Micro Services, API’s LLMOps – Vector DB, RAG, LLM Orchestration tools, LLM Observability, LLM Guardrails, Responsible AI MLOps - MLFlow, ML/DL libraries, Model & Data Drift Detection libraries & techniques Real Time & Batch Streaming Container Orchestration Platforms Cloud platforms – Azure/ AWS/ GCP, Data Platforms – Databricks/ Snowflake Nice to Have: Understanding of Agent Ops Exposure to Databricks platform You can expect to – Work with world’s biggest Retailers, CPG’s, HealthCare, Banking & Manufacturing customers and help them solve some of their most critical problems Create multi-million Dollar business opportunities by leveraging impact mindset, cutting edge solutions and industry best practices. Work in a diverse environment that keeps evolving Hone your entrepreneurial skills as you contribute to growth of the organization