38 Milvus Jobs

As a Senior GenAI Engineer at NTT DATA in Delhi, Haryana (IN-HR), India, your role will involve designing, building, and productionizing GenAI and agentic systems on hyperscalers like Azure, AWS, and GCP. You will lead the development lifecycle from problem framing to MLOps deployment, while also mentoring junior engineers and collaborating with product, design, and clients. Key Responsibilities: - Design & build GenAI/agentic systems including chat copilots, workflow/graph agents, and tool use - Implement chunking, hybrid search, vector stores, re-ranking, feedback loops, and continuous data quality/evaluation - Select, integrate, and finetune LLMs & multimodal models - Apply prompt-engineering techniques to specific use cases - Work on solutions based on LLM, NLP, DL, ML, object detection/classification, etc. - Have a clear understanding of CI/CD, configuring guardrails, and PII redaction - Collaborate with clients/stakeholders from multiple geographies - Stay informed about the latest advancements in Gen AI, machine learning, and AI technologies Qualifications: - Bachelors/Masters Degree or equivalent - 8+ years in software/ML engineering, with 1.5+ years hands-on experience with LLMs/GenAI and agentic frameworks - Proven track record of shipping production AI systems on at least one hyperscaler - Experience leading teams and owning end-to-end delivery Required Skills: - Strong Python experience for building AI-ML/GenAI Solutions - Experience with leading frameworks like LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen - Strong experience with Vector DBs like Pinecone, Milvus, Redis/pgvector - Working experience with hybrid search, re-rankers, evaluation, and observability - Proficiency in SQL Query, NLP, CV, Deep Learning Algorithms - Experience with Open Source Models - Knowledge of UI/UX is an added advantage About NTT DATA: NTT DATA is a $30 billion global innovator of business and technology services, serving 75% of the Fortune Global 100. With diverse experts in over 50 countries, NTT DATA is committed to helping clients innovate, optimize, and transform for long-term success. As a Global Top Employer, NTT DATA offers services in business and technology consulting, data and artificial intelligence, industry solutions, as well as the development and management of applications, infrastructure, and connectivity. NTT DATA is a leading provider of digital and AI infrastructure and is part of the NTT Group, investing significantly in R&D to support organizations and society in the digital future. Visit us at us.nttdata.com,

Primary Title: Senior LLM Engineer (4+ years) Hybrid, India About The Opportunity A technology consulting firm operating at the intersection of Enterprise AI, Generative AI and Cloud Engineering seeks an experienced LLM-focused engineer. You will build and productionize LLM-powered products and integrations for enterprise customers across knowledge management, search, automation, and conversational AI use-cases. This is a hybrid role based in India for candidates with strong hands-on LLM engineering experience. Role & Responsibilities Own design and implementation of end-to-end LLM solutions: data ingestion ? retrieval (RAG) ? fine-tuning ? inference and monitoring for production workloads. Develop robust Python microservices to serve LLM inference, retrieval, and agentic workflows using LangChain/LangGraph or equivalent toolkits. Implement and optimise vector search pipelines (FAISS/Pinecone/Milvus), embedding generation, chunking strategies, and relevance tuning for sub-second retrieval. Perform parameter-efficient fine-tuning (LoRA/adapters) and evaluation workflows; manage model versioning and automated validation for quality and safety. Containerise and deploy models and services with Docker and Kubernetes; integrate with cloud infra (AWS/Azure/GCP) and CI/CD for repeatable delivery. Establish observability, alerting, and performance SLAs for LLM services; collaborate with cross-functional teams to define success metrics and iterate rapidly. Skills & Qualifications Must-Have 4+ years engineering experience with 2+ years working directly on LLM/Generative AI projects. Strong Python skills and hands-on experience with PyTorch and HuggingFace/transformers libraries. Practical experience building RAG pipelines, vector search (FAISS/Pinecone/Milvus), and embedding workflows. Experience with fine-tuning strategies (LoRA/adapters) and evaluation frameworks for model quality and safety. Familiarity with Docker, Kubernetes, cloud deployment (AWS/Azure/GCP), and Git-based CI/CD workflows. Solid understanding of prompt engineering, retrieval strategies, and production monitoring of ML services. Preferred Experience with LangChain/LangGraph, agent frameworks, or building tool-calling pipelines. Exposure to MLOps platforms, model registry, autoscaling low-latency inference, and cost-optimisation techniques. Background in productionising LLMs for enterprise use-cases (knowledge bases, search, virtual assistants). Benefits & Culture Highlights Hybrid work model with flexible in-office collaboration and remote days; competitive market compensation. Opportunity to work on high-impact enterprise AI initiatives and shape production-grade GenAI patterns across customers. Learning-first culture: access to technical mentorship, experimentation environments, and conferences/learning stipend. To apply: include a brief portfolio of LLM projects, links to relevant repositories or demos, and a summary of production responsibilities. This role is ideal for engineers passionate about turning cutting-edge LLM research into reliable, scalable enterprise solutions. Skills: llm,open ai,gemini Show more Show less

Primary Title: Senior LLM Engineer (4+ years) Hybrid, India About The Opportunity A technology consulting firm operating at the intersection of Enterprise AI, Generative AI and Cloud Engineering seeks an experienced LLM-focused engineer. You will build and productionize LLM-powered products and integrations for enterprise customers across knowledge management, search, automation, and conversational AI use-cases. This is a hybrid role based in India for candidates with strong hands-on LLM engineering experience. Role & Responsibilities Own design and implementation of end-to-end LLM solutions: data ingestion ? retrieval (RAG) ? fine-tuning ? inference and monitoring for production workloads. Develop robust Python microservices to serve LLM inference, retrieval, and agentic workflows using LangChain/LangGraph or equivalent toolkits. Implement and optimise vector search pipelines (FAISS/Pinecone/Milvus), embedding generation, chunking strategies, and relevance tuning for sub-second retrieval. Perform parameter-efficient fine-tuning (LoRA/adapters) and evaluation workflows; manage model versioning and automated validation for quality and safety. Containerise and deploy models and services with Docker and Kubernetes; integrate with cloud infra (AWS/Azure/GCP) and CI/CD for repeatable delivery. Establish observability, alerting, and performance SLAs for LLM services; collaborate with cross-functional teams to define success metrics and iterate rapidly. Skills & Qualifications Must-Have 4+ years engineering experience with 2+ years working directly on LLM/Generative AI projects. Strong Python skills and hands-on experience with PyTorch and HuggingFace/transformers libraries. Practical experience building RAG pipelines, vector search (FAISS/Pinecone/Milvus), and embedding workflows. Experience with fine-tuning strategies (LoRA/adapters) and evaluation frameworks for model quality and safety. Familiarity with Docker, Kubernetes, cloud deployment (AWS/Azure/GCP), and Git-based CI/CD workflows. Solid understanding of prompt engineering, retrieval strategies, and production monitoring of ML services. Preferred Experience with LangChain/LangGraph, agent frameworks, or building tool-calling pipelines. Exposure to MLOps platforms, model registry, autoscaling low-latency inference, and cost-optimisation techniques. Background in productionising LLMs for enterprise use-cases (knowledge bases, search, virtual assistants). Benefits & Culture Highlights Hybrid work model with flexible in-office collaboration and remote days; competitive market compensation. Opportunity to work on high-impact enterprise AI initiatives and shape production-grade GenAI patterns across customers. Learning-first culture: access to technical mentorship, experimentation environments, and conferences/learning stipend. To apply: include a brief portfolio of LLM projects, links to relevant repositories or demos, and a summary of production responsibilities. This role is ideal for engineers passionate about turning cutting-edge LLM research into reliable, scalable enterprise solutions. Skills: llm,open ai,gemini Show more Show less

As a highly skilled GenAI Lead Engineer, your role will involve designing and implementing advanced frameworks for alternate data analysis in the investment management domain. You will leverage LLM APIs (such as GPT, LLaMA, etc.), build scalable orchestration pipelines, and architect cloud/private deployments to drive next-generation AI-driven investment insights. Additionally, you will lead a cross-functional team of Machine Learning Engineers and UI Developers to deliver robust, production-ready solutions. **Key Responsibilities:** - **GenAI Framework Development:** Develop custom frameworks using GPT APIs or LLaMA for alternate data analysis and insights generation. Optimize LLM usage for investment-specific workflows, including data enrichment, summarization, and predictive analysis. - **Automation & Orchestration:** Design and implement document ingestion workflows using tools such as n8n (or similar orchestration frameworks). Build modular pipelines for structured and unstructured data. - **Infrastructure & Deployment:** Architect deployment strategies on cloud (AWS, GCP, Azure) or private compute environments (CoreWeave, on-premises GPU clusters). Ensure high availability, scalability, and security in deployed AI systems. **Qualification Required:** - Strong proficiency in Python with experience in frameworks such as TensorFlow or PyTorch. - 2+ years of experience in Generative AI and Large Language Models (LLMs). - Experience with VectorDBs (e.g., Pinecone, Weaviate, Milvus, FAISS) and document ingestion pipelines. - Familiarity with data orchestration tools (e.g., n8n, Airflow, LangChain Agents). - Understanding of cloud deployments and GPU infrastructure (CoreWeave or equivalent). - Proven leadership skills with experience managing cross-functional engineering teams. - Strong problem-solving skills and ability to work in fast-paced, data-driven environments. - Experience with financial or investment data platforms. - Knowledge of RAG (Retrieval-Augmented Generation) systems. - Familiarity with frontend integration for AI-powered applications. - Exposure to MLOps practices for continuous training and deployment.,

As a Senior AI Engineer at Uplevyl, you will play a crucial role in leading the design and deployment of AI-powered, agentic workflows that drive the future of personalized insights. Your main focus will be on vector search, retrieval-augmented generation (RAG), and intelligent automation, collaborating closely with full-stack engineers and product teams to bring scalable GenAI features into production. Key Responsibilities: - Design and implement RAG pipelines for semantic search, personalization, and contextual enrichment. - Build agentic AI workflows using Pinecone, LangChain/LangGraph, and custom orchestration. - Integrate LLM-driven features into production systems, balancing innovation with scalability. - Architect and optimize vector databases (Pinecone, FAISS, Milvus) for low-latency retrieval. - Work with structured/unstructured datasets for embedding, indexing, and enrichment. - Collaborate with data engineers on ETL/ELT pipelines to prepare data for AI applications. - Partner with backend and frontend engineers to integrate AI features into user-facing products. - Participate in Agile ceremonies (sprint planning, reviews, standups). - Maintain clear documentation and support knowledge sharing across the AI team. Required Qualifications: - 5+ years in AI/ML engineering or software engineering with applied AI focus. - Hands-on experience with RAG pipelines, vector databases (Pinecone, FAISS, Milvus), and LLM integration. - Strong background in Python for AI workflows (embeddings, orchestration, optimization). - Familiarity with agentic architectures (LangChain, LangGraph, or similar). - Experience deploying and scaling AI features on AWS cloud environments. - Strong collaboration and communication skills for cross-functional teamwork. Tech Stack: - AI Tools: Pinecone, LangChain, LangGraph, OpenAI APIs (ChatGPT, GPT-4/5), HuggingFace models - Languages: Python (primary for AI workflows), basic Node.js knowledge for integration - Cloud & DevOps: AWS (Lambda, S3, RDS, DynamoDB, IAM), Docker, CI/CD pipelines - Data Engineering: SQL, Python (Pandas, NumPy), ETL/ELT workflows, Databases (Postgres, DynamoDB, Redis) - Bonus Exposure: React, Next.js Preferred Skills: - Experience with embedding models, HuggingFace Transformers, or fine-tuning LLMs. - Knowledge of compliance frameworks (GDPR, HIPAA, SOC 2). - Exposure to personalization engines, recommender systems, or conversational AI.,

We are looking for a Director of Engineering (AI Systems & Secure Platforms) to join our client&aposs Core Engineering team at Thane (Maharashtra India). The ideal candidate should have 1215+ years of experience in architecting and deploying AI systems at scale, with deep expertise in agentic AI workflows, LLMs, RAG, Computer Vision, and secure mobile/wearable platforms. Top 3 Daily Tasks: ? Architect, optimize, and deploy LLMs, RAG pipelines, and Computer Vision models for smart glasses and other edge devices. ? Design and orchestrate agentic AI workflowsenabling autonomous agents with planning, tool usage, error handling, and closed feedback loops. ? Collaborate across AI, Firmware, Security, Mobile, Product, and Design teams to embed invisible intelligence within secure wearable systems. Must have 1215+ years of experience in Applied AI, Deep Learning, Edge AI deployment, Secure Mobile Systems, and Agentic AI Architecture. Must have: -Programming languages: Python, C/C++, Java (Android), Kotlin, JavaScript/Node.js, Swift, Objective-C, CUDA, Shell scripting -Expert in TensorFlow, PyTorch, ONNX, HuggingFace; model optimization with TensorRT, TFLite -Deep experience with LLMs, RAG pipelines, vector DBs (FAISS, Milvus) -Proficient in agentic AI workflowsmulti-agent orchestration, planning, feedback loops -Strong in privacy-preserving AI (federated learning, differential privacy) -Secure real-time comms (WebRTC, SIP, RTP) Nice to have: -Experience with MCP or similar protocol frameworks -Background in wearables/XR or smart glass AI platforms -Expertise in platform security architectures (sandboxing, auditability) Show more Show less

As an AI Developer specializing in Natural Language Processing (NLP) and Large Language Models (LLMs), you will have the opportunity to work remotely or in a hybrid setting. In this role, whether on a full-time or contract basis, your responsibilities will revolve around developing AI-driven conversational agents using LangChain and LangGraph. You will collaborate with various LLMs such as OpenAI, Claude, and Llama, fine-tuning and customizing models to meet project requirements. Additionally, you will be tasked with implementing vector search functionalities using databases like Milvus, MongoDB, or other relevant platforms. Integration of AI models into the backend of the platforms and optimizing their performance will be a crucial aspect of your work. Furthermore, active collaboration with front-end and back-end teams is essential to enhance the overall AI functionalities of the project. To excel in this role, you should possess a strong background in utilizing LangChain, LangGraph, and OpenAI/LLM APIs. Proficiency in programming languages such as Python, along with experience in working with PyTorch, TensorFlow, and fine-tuning transformer models, will be valuable assets. Your familiarity with vector databases like Milvus, Pinecone, Weaviate, or MongoDB Atlas will play a significant role in your day-to-day tasks. Knowledge of prompt engineering, RAG (Retrieval-Augmented Generation), and embeddings is highly desirable, alongside experience in API development and integration. Additionally, any prior exposure to cloud platforms such as AWS, GCP, or Azure will be considered advantageous. This position welcomes freshers who are eager to learn and contribute to the field of AI development, particularly in NLP and LLMs. If you are enthusiastic about leveraging advanced technologies to create innovative solutions, this role offers a stimulating environment to grow and excel in the realm of AI development.,

We are looking for AI/ML engineers who will be responsible for developing, deploying, and optimizing advanced AI models. You will have the opportunity to work with cutting-edge tools such as OpenAI APIs, Hugging Face Transformers, ONNX, and LangChain to create scalable AI solutions. Your key responsibilities will include: - Demonstrating expertise in Large Language Models (LLMs) and utilizing tools like OpenAI API, Hugging Face Transformers, and other NLP frameworks. - Proficiently fine-tuning and deploying pre-trained models across various NLP tasks. - Strong knowledge in LangChain for the construction and management of LLM pipelines. - Utilizing Vector Databases such as Pinecone, Milvus, and Weaviate for efficient data retrieval and AI-driven search. - Applying embedding techniques for tasks related to search, recommendations, and semantic similarity. - Having a solid grasp of Prompt Engineering to enhance model performance. - Demonstrating strong Python programming skills and experience with ML libraries like TensorFlow, PyTorch, and Scikit-learn. - Experience in deploying ML models using ONNX for scalable and efficient inference. - Hands-on involvement in the development, optimization, and maintenance of AI/ML models within production environments. - Ability to collaborate effectively with cross-functional teams and staying updated on the latest AI advancements to improve solutions.,

As a member of our team, your primary responsibility will be the development and training of foundational models across various modalities. You will be involved in the end-to-end lifecycle management of foundational model development, starting from data curation to model deployment, by collaborating closely with core team members. Your role will also entail conducting research to enhance model accuracy and efficiency, as well as implementing state-of-the-art AI techniques in Text/Speech and language processing. Collaboration with cross-functional teams will be essential as you work towards building robust AI stacks and seamlessly integrating them into production pipelines. You will be expected to develop pipelines for debugging, CI/CD, and ensuring observability throughout the development process. Demonstrating your ability to lead projects and offer innovative solutions will be crucial, along with documenting technical processes, model architectures, and experimental results, while maintaining clear and organized code repositories. Ideally, you should hold a Bachelor's or Master's degree in a related field and possess 2 to 5 years of industry experience in applied AI/ML. Proficiency in Python programming is a must, along with familiarity with a selection of tools such as TensorFlow, PyTorch, HF Transformers, NeMo, SLURM, Ray, Pytorch DDP, Deepspeed, NCCL, Git, DVC, MLFlow, W&B, KubeFlow, Dask, Milvus, Apache Spark, Numpy, Whisper, Voicebox, VALL-E, HuBERT/Unitspeech, LLMOPs Tools, Dockers, DSPy, Langgraph, langchain, and llamaindex. If you are passionate about AI and eager to contribute to cutting-edge projects in the field, we welcome your application to join our dynamic team.,

Position Summary... What you&aposll do... About the Team: The Enterprise People Technology team supports the successful deployment and adoption of new People technology across the enterprise. As a Fortune #1 company, our work impacts millions of associates globally. We strive to continuously improve people technology and products to help managers and associates focus on what matters most -supporting our customers and members. People Technology is a significant segment of Walmart Global Techs Enterprise Business Services, invested in building a compact, robust organization that includes service operations and technology solutions for Finance, People, and the Associate Digital Experience. About People.AI Team: The People.AI team is responsible for developing and deploying AI/ML solutions supporting the Walmart associates globally. In this role, you will build an LLM-powered intelligent voice-based experience to integrate with associate facing contact centers to enhance associate experience, increase first call resolution and drive productivity by implementing self-service Q&A and action agents. You will design and build an intelligent conversational architecture that improves communication, automates tasks, accesses data and insights, and provides personalized Q&A support to associates in a multi-modal setup and ultimately creating a more efficient and engaging work environment. What you&aposll do: This is a vital role with significant growth prospects. You will have work to do from day one and you will be counted on to create opportunities that align with the overall organization&aposs goals. Some of this roles key responsibilities include, but not limited to: Design, develop, and deploy LLM-powered intelligent applications that enhance productivity and engagement for Walmart associates across the globe. Build and integrate enterprise-grade conversational agents and AI copilots into contact centers, chatbots, internal portals, and business applications to automate tasks, improve communication, and provide real-time, contextualized support. Leverage state-of-the-art LLMs and frameworks like LangChain, LangGraph to create dynamic, natural language interfaces over enterprise data. Implement retrieval-augmented generation (RAG) pipelines using vector databases such as Milvus or FAISS to enable deep integration with Walmarts knowledge ecosystem. Enhance the clarity, conciseness, and readability of generated knowledge Q&A to ensure it is wellsuited for voice-based service agents. Build developer-friendly tools and reusable frameworks that abstract key aspects of ML and LLM system complexityenabling developers without prior ML experience to quickly build highquality, scalable AI solutions with minimal friction, while maintaining reliability, governance, and performance standards. Act as a platform enabler and technical collaborator, working closely with Data Scientists, Software Engineers, Product Managers, and MLOps/DevOps teams to streamline model deployment, monitoring, and lifecycle management. Design end-to-end system architecture for GenAI applications and its integration with contact centers, chatbots and other business applications What youll bring: You have 15+ years' experience in design, development and maintenance of highly scalable, secure and user-centric products and platforms Expert-level proficiency in programming languages such as Python or Java, with a strong track record of delivering high-performance, production-grade code for machine learning applications. Proven experience designing, developing, and operationalizing end-to-end ML systems, including data ingestion, feature engineering, model training, deployment, and monitoring. A solid understanding of core machine learning principles, including various model families (e.g., tree-based models, deep neural networks), feature selection techniques, and practical challenges such as data quality, bias, and concept/data drift. Deep experience in Linux-based development environments, along with containerization using Docker and orchestration with Kubernetes for scalable ML service deployment. Hands-on experience working with cloud platforms like Google Cloud Platform (GCP) or Microsoft Azure, with familiarity in their AI/ML tools, infrastructure components, and DevOps ecosystems. Proficiency in building and scaling API-based services and distributed systems, and in managing workflows using tools such as Apache Airflow or similar orchestration frameworks. Strong working knowledge of SQL and modern data engineering practices, enabling seamless integration of ML pipelines with batch and streaming data sources. Proficient in version control with Git, and experienced in collaborating within agile, crossfunctional software teams. A deep grasp of software engineering best practices, including API design, automated testing, CI/CD pipelines, and observability for ML-driven systems. Clear and confident communication skills, with the ability to understand cross-functional requirements and translate them into scalable technical solutions. A mindset grounded in intellectual curiosity, with a passion for MLOps, cloud-native technologies, and driving innovation in scalable AI infrastructure. About Walmart Global Tech Imagine working in an environment where one line of code can make life easier for hundreds of millions of people. Thats what we do at Walmart Global Tech. Were a team of software engineers, data scientists, cybersecurity expert&aposs and service professionals within the worlds leading retailer who make an epic impact and are at the forefront of the next retail disruption. People are why we innovate, and people power our innovations. We are people-led and tech-empowered. We train our team in the skillsets of the future and bring in experts like you to help us grow. We have roles for those chasing their first opportunity as well as those looking for the opportunity that will define their career. Here, you can kickstart a great career in tech, gain new skills and experience for virtually every industry, or leverage your expertise to innovate at scale, impact millions and reimagine the future of retail. Flexible, hybrid work We use a hybrid way of working with primary in office presence coupled with an optimal mix of virtual presence. We use our campuses to collaborate and be together in person, as business needs require and for development and networking opportunities. This approach helps us make quicker decisions, remove location barriers across our global team, be more flexible in our personal lives. Benefits Beyond our great compensation package, you can receive incentive awards for your performance. Other great perks include a host of best-in-class benefits maternity and parental leave, PTO, health benefits, and much more. Belonging We aim to create a culture where every associate feels valued for who they are, rooted in respect for the individual. Our goal is to foster a sense of belonging, to create opportunities for all our associates, customers and suppliers, and to be a Walmart for everyone. At Walmart, our vision is "everyone included." By fostering a workplace culture where everyone isand feelsincluded, everyone wins. Our associates and customers reflect the makeup of all 19 countries where we operate. By making Walmart a welcoming place where all people feel like they belong, were able to engage associates, strengthen our business, improve our ability to serve customers, and support the communities where we operate. Equal Opportunity Employer Walmart, Inc., is an Equal Opportunities Employer By Choice. We believe we are best equipped to help our associates, customers and the communities we serve live better when we really know them. That means understanding, respecting and valuing unique styles, experiences, identities, ideas and opinions while being inclusive of all people. Minimum Qualifications... Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications. Minimum Qualifications:Option 1: Bachelor&aposs degree in computer science, computer engineering, computer information systems, software engineering, or related area and6 years experience in software engineering or related area. Option 2: 8 years experience in software engineering or related area. Preferred Qualifications... Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications. Masters degree in computer science, computer engineering, computer information systems, software engineering, or related area and 4 years' experience in software engineering or related area Primary Location... Pardhanani Wilshire Ii, Cessna Business Park, Kadubeesanahalli Village, Varthur Hobli , India R-2218294 Show more Show less

You should have expertise in ML/DL, model lifecycle management, and MLOps tools such as MLflow and Kubeflow. Proficiency in Python, TensorFlow, PyTorch, Scikit-learn, and Hugging Face models is essential. You must possess strong experience in NLP, fine-tuning transformer models, and dataset preparation. Hands-on experience with cloud platforms like AWS, GCP, Azure, and scalable ML deployment tools like Sagemaker and Vertex AI is required. Knowledge of containerization using Docker and Kubernetes, as well as CI/CD pipelines, is expected. Familiarity with distributed computing tools like Spark and Ray, vector databases such as FAISS and Milvus, and model optimization techniques like quantization and pruning is necessary. Additionally, you should have experience in model evaluation, hyperparameter tuning, and model monitoring for drift detection. As a part of your roles and responsibilities, you will be required to design and implement end-to-end ML pipelines from data ingestion to production. Developing, fine-tuning, and optimizing ML models to ensure high performance and scalability is a key aspect of the role. You will be expected to compare and evaluate models using key metrics like F1-score, AUC-ROC, and BLEU. Automation of model retraining, monitoring, and drift detection will be part of your responsibilities. Collaborating with engineering teams for seamless ML integration, mentoring junior team members, and enforcing best practices are also important aspects of the role. This is a full-time position with a day shift schedule from Monday to Friday. The total experience required for this role is 4 years, with at least 3 years of experience in Data Science roles. The work location is in person. Application Question: How soon can you join us ,

As an experienced AI Software Engineer, you will be responsible for designing, developing, and deploying Agentic AI applications and advanced software solutions leveraging Large Language Models (LLMs) and modern AI tools. Your primary focus will be on building autonomous AI agents, integrating them with business systems, and delivering production-grade applications that effectively solve complex problems. Your key responsibilities will include designing and developing agentic AI systems capable of autonomous reasoning, planning, and execution. You will also be integrating LLMs (such as OpenAI, Anthropic, Mistral, LLaMA, etc.) into scalable software applications, and building and optimizing multi-agent workflows using frameworks like LangChain, AutoGen, CrewAI, or custom solutions. Additionally, you will implement vector databases (Pinecone, Weaviate, FAISS, Milvus) for semantic search and retrieval-augmented generation (RAG), fine-tune LLMs or use instruction-tuning and prompt engineering for domain-specific tasks, develop APIs, microservices, and backend logic to support AI applications, collaborate with product teams to identify opportunities for AI automation, and deploy applications to cloud platforms (AWS/GCP/Azure) with a focus on security, scalability, and reliability. Monitoring, testing, and improving model performance through real-world feedback loops will also be part of your responsibilities. To qualify for this role, you should have at least 2+ years of experience in software development using Python, Node.js, or similar technologies, along with 1+ years of hands-on experience in AI/ML development, specifically with LLMs. Additionally, you should have a minimum of 1 year of experience in building autonomous AI agents or agent-based applications, a strong understanding of AI orchestration frameworks like LangChain, AutoGen, CrewAI, etc., experience with vector databases and embedding models, and cloud deployment experience (AWS Lambda, EC2, ECS, GCP Cloud Run, Azure Functions). Strong problem-solving skills, the ability to work independently, and an ownership mindset are also required. Preferred skills for this role include experience with multi-modal AI (text, image, audio), knowledge of data pipelines, ETL, and real-time event-driven architectures, a background in NLP, transformers, and deep learning frameworks (PyTorch, TensorFlow), familiarity with DevOps, CI/CD pipelines, and container orchestration (Docker, Kubernetes), and contributions to open-source AI projects or publications in AI/ML. In return for your expertise, we offer a competitive salary, performance-based bonuses, flexible working hours, remote work opportunities, exposure to cutting-edge AI technologies and tools, and the opportunity to lead innovative AI projects with real-world impact. This is a full-time, permanent position with benefits including Provident Fund. If you meet the required qualifications and have the desired skills, we look forward to welcoming you to our team on the expected start date of 19/08/2025.,

You will be joining a fast-growing enterprise AI & data science consultancy that caters to global clients in finance, healthcare, and enterprise software sectors. Your primary responsibility as a Senior LLM Engineer will involve designing, fine-tuning, and operationalizing large language models for real-world applications using PyTorch and Hugging Face tooling. You will also be tasked with architecting and implementing RAG pipelines, building scalable inference services and APIs, collaborating with data engineers and ML scientists, and establishing engineering best practices. To excel in this role, you must have at least 4 years of experience in data science/ML engineering with a proven track record of delivering LLM-based solutions to production. Proficiency in Python programming, PyTorch, and Hugging Face Transformers is essential. Experience with RAG implementation, production deployment technologies such as Docker and Kubernetes, and cloud infrastructure (AWS/GCP/Azure) is crucial. Preferred qualifications include familiarity with orchestration frameworks like LangChain/LangGraph, ML observability, model governance, and mitigation techniques for bias. Besides technical skills, you will benefit from a hybrid working model in India, opportunities to work on cutting-edge GenAI projects, and a collaborative consultancy culture that emphasizes mentorship and career growth. If you are passionate about LLM engineering and seek end-to-end ownership of projects, Zorba Consulting India offers an equal opportunity environment where you can contribute to diverse and inclusive teams. To apply for this role, submit your resume along with a brief overview of a recent LLM project you led, showcasing your expertise in models, infrastructure, and outcomes.,

You are a highly skilled GenAI Lead Engineer responsible for designing and implementing advanced frameworks for alternate data analysis in the investment management domain. You will leverage LLM APIs (GPT, LLaMA, etc.), build scalable orchestration pipelines, and architect cloud/private deployments to drive next-generation AI-driven investment insights. Your role includes leading a cross-functional team of Machine Learning Engineers and UI Developers to deliver robust, production-ready solutions. Your responsibilities will include developing custom frameworks using GPT APIs or LLaMA for alternate data analysis and insights generation. You will optimize LLM usage for investment-specific workflows, including data enrichment, summarization, and predictive analysis. Additionally, you will design and implement document ingestion workflows using tools such as n8n (or similar orchestration frameworks) and build modular pipelines for structured and unstructured data. You are also expected to architect deployment strategies on cloud (AWS, GCP, Azure) or private compute environments (CoreWeave, on-premises GPU clusters) ensuring high availability, scalability, and security in deployed AI systems. The ideal candidate should have a strong proficiency in Python with experience in frameworks such as TensorFlow or PyTorch. You must have 2+ years of experience in Generative AI and Large Language Models (LLMs) along with experience with VectorDBs (e.g., Pinecone, Weaviate, Milvus, FAISS) and document ingestion pipelines. Familiarity with data orchestration tools (e.g., n8n, Airflow, LangChain Agents), cloud deployments, GPU infrastructure (CoreWeave or equivalent), proven leadership skills, and experience managing cross-functional engineering teams are essential. Strong problem-solving skills, the ability to work in fast-paced, data-driven environments, experience with financial or investment data platforms, knowledge of RAG (Retrieval-Augmented Generation) systems, familiarity with frontend integration for AI-powered applications, and exposure to MLOps practices for continuous training and deployment are also required.,

Hello Connections, This is a REMOTE position. We are seeking a Staff LLMRAG Engineer to lead the development and optimization of enterprise-grade retrieval-augmented generation systems. You will architect scalable AI solutions, integrate large language models with advanced retrieval pipelines, and ensure production readiness. This role combines deep technical expertise with the ability to guide teams and deliver results on aggressive timelines. Most Important Skills/Responsibilities: Lead RAG Architecture Design Define and implement best practices for retrieval-augmented generation systems, ensuring reliability, scalability, and low-latency performance. Full-Stack AI Development Build and optimize multi-stage pipelines using LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, or custom). Programming & Integration Develop services and APIs in Python and Golang to support AI workflows, document ingestion, and retrieval processes. Search & Retrieval Optimization Implement hybrid search, vector embeddings, and semantic ranking strategies to improve contextual accuracy. Prompt Engineering Design and iterate on few-shot, chain-of-thought, and tool-augmented prompts for domain-specific applications. -Bachelors degree required - Strong proficiency in Python and Golang or RUST, with experience building high-performance services and APIs. Key Responsibilities Lead RAG Architecture Design Define and implement best practices for retrieval-augmented generation systems, ensuring reliability, scalability, and low-latency performance. Full-Stack AI Development Build and optimize multi-stage pipelines using LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, or custom). Programming & Integration Develop services and APIs in Python and Golang to support AI workflows, document ingestion, and retrieval processes. Search & Retrieval Optimization Implement hybrid search, vector embeddings, and semantic ranking strategies to improve contextual accuracy. Prompt Engineering Design and iterate on few-shot, chain-of-thought, and tool-augmented prompts for domain-specific applications. Mentorship & Collaboration Partner with cross-functional teams and guide engineers on RAG and LLM best practices. Performance Monitoring Establish KPIs and evaluation metrics for RAG pipeline quality and model performance. Qualifications Must Have: 8+ years in software engineering or applied AI/ML, with at least 2+ years focused on LLMs and retrieval systems. Strong proficiency in Python and Golang or RUST, with experience building high-performance services and APIs. Expertise in RAG frameworks (LangChain, LangGraph, LlamaIndex) and embedding models. Hands-on experience with vector databases (Databricks Vector Store, Pinecone, Weaviate, Milvus, Chroma). Strong understanding of hybrid search (semantic + keyword) and embedding optimization. -Bachelors degree required Preferred: LLM fine-tuning experience (LoRA, PEFT). Knowledge graph integration with LLMs. Familiarity with cloud ML deployment (AWS (preferred), Databricks, Azure). - Masters or PHD degree in CS Soft Skills Strong problem-solving and decision-making skills under tight timelines. Excellent communication for cross-functional collaboration. Ability to work independently while aligning with strategic goals. Show more Show less

Job Description Role Summary We are seeking a Generative AI/ML Engineer to design, build, and deploy intelligent AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and edge computing. You will work hands-on with cloud-native AI services, LLMOps pipelines, and enterprise-grade deployment patterns to solve business problems. Key Responsibilities Design, develop, and fine-tune LLM-powered applications for enterprise use cases. Experience in evaluating LLM applications and developing observability frameworks Implement RAG pipelines using vector databases, embeddings, and optimized retrieval strategies. Build agentic AI workflows with multi-step reasoning, tool calling, and integration with APIs. Integrate GenAI solutions into multi-cloud or hybrid cloud environments (AWS, Azure, GCP). Develop and optimize edge AI deployments for low-latency use cases. Create data strategy, ingestion, transformation, enrichment, validations, quality checks via pipelines for AI ingestion, preprocessing, and governance. Implement AI safety, bias mitigation, and compliance measures. Work closely with LLMOps teams to enable continuous integration & deployment of AI models. Write well-documented, production-ready code in Python, Node.js, Rust . Benchmark and evaluate model performance , latency, and cost-efficiency. Required Skills Proficient in cloud AI services (e.g. AWS Bedrock/SageMaker, Azure AI Foundry, Google Vertex AI, Anthropic, OpenAI APIs). Strong proficiency with Python and LLM frameworks (e.g. PromptFlow, LangGraph, LlamaIndex, HuggingFace, PyTorch, TensorFlow). Hands-on experience with vector DBs (e.g. Pinecone, Weaviate, Milvus, FAISS, Azure Cognitive Search). Experience building RAG-based and agentic AI solutions. Familiarity with Edge AI frameworks (e.g. NVIDIA Jetson, AWS IoT Greengrass, Azure Percept). Multi-modal AI (text, image, speech, video) experience. Strong grasp of APIs, microservices, and event-driven architectures . Knowledge of AI governance (data privacy, model explainability, security). Experience in containerized deployments (Docker, Kubernetes, serverless AI functions). Preferred Skills Generative agents with memory and planning capabilities . Real-time AI streaming with WebSockets or Kafka. Prior contributions to open-source GenAI projects . Experience in build, test and deploy various ML models Experience in building MCP, A2A protocol Show more Show less

Primary Role Title: Senior LLM Engineer About The Opportunity We are a fast-growing enterprise AI & data science consultancy serving global clients across finance, healthcare, and enterprise software. The team builds production-grade LLM-driven productsRAG systems, intelligent assistants, and custom inference pipelinesthat deliver measurable business outcomes. Location: India (Hybrid) Role & Responsibilities Design, fine-tune and productionize large language models (instruction tuning, LoRA/PEFT) using PyTorch and Hugging Face tooling for real-world applications. Architect and implement RAG pipelines: embeddings generation, chunking strategies, vector search integration (FAISS/Pinecone/Milvus) and relevance tuning for high-quality retrieval. Build scalable inference services and APIs (FastAPI/Falcon), containerize (Docker) and deploy to cloud/Kubernetes with low-latency and cost-optimized inference (quantization, ONNX/Triton). Collaborate with data engineers and ML scientists to productionize data pipelines, automate retraining, monitoring, evaluation and drift detection. Drive prompt-engineering, evaluation frameworks and safety/guardrail implementation to ensure reliable, explainable LLM behavior in production. Establish engineering best-practices (Git workflows, CI/CD, unit tests, observability) and mentor junior engineers to raise team delivery standards. Skills & Qualifications Must-Have 4+ years in data science/ML engineering with demonstrable experience building and shipping LLM-based solutions to production. Strong Python engineering background and hands-on experience with PyTorch and Hugging Face Transformers (fine-tuning, tokenizers, model optimization). Practical experience implementing RAG: embeddings, vector DBs (FAISS/Pinecone/Weaviate/Milvus), chunking and retrieval tuning. Production deployment experience: Docker, Kubernetes, cloud infra (AWS/GCP/Azure) and inference optimization (quantization, batching, ONNX/Triton). Preferred Experience with LangChain/LangGraph or similar orchestration frameworks, and building agentic workflows. Familiarity with ML observability, model governance, safety/bias mitigation techniques and cost/performance trade-offs for production LLMs. Benefits & Culture Highlights Hybrid working model in India with flexible hours, focused on outcomes and work-life balance. Opportunity to work on cutting-edge GenAI engagements for enterprise customers and accelerate your LLM engineering career. Collaborative consultancy culture with mentorship, learning stipend and clear growth paths into technical leadership. This role is with Zorba Consulting India. If you are an experienced LLM practitioner who enjoys end-to-end ownershipfrom research experiments to robust production systemsapply with your resume and a short note on a recent LLM project you led (models, infra, and outcomes). Zorba Consulting India is an equal opportunity employer committed to diversity and inclusion. Skills: ml,llm,data science Show more Show less

As an Amdocs Software Architect, you will be responsible for leading the architectural roadmap and providing architecture solutions throughout the software development lifecycle. Your role will involve making decisions based on extensive analysis and interpretation, encompassing both tangible and intangible factors. You will offer technical expertise in software usage, functional, and non-functional aspects, collaborating with software engineers and fellow architects to define and refine product structures in alignment with business requirements. Additionally, you will work closely with customers and product line management to identify, refine, and translate customer needs into technical requirements. Leading architectural decisions and tasks within a product line or across multiple product lines, you will oversee projects, review technical designs, and offer guidance to software engineers on technical and architectural design decisions. Your responsibilities will also include researching, evaluating, and prototyping new methodologies, technologies, and products, proposing and implementing improvements in processes and tools. Acquiring a deep understanding of the customer context will be crucial in making technical decisions and choices. Key Responsibilities: - Extensive exposure to working with Various LLMs such as Lama, Gpt, and others. - Hands-on experience in handling GenAI use cases. - Expertise and experience in developing Cloud Jobs for data ingestion for learnings. - Establish partnerships with project stakeholders to provide technical assistance for key decisions. - Development and implementation of Gen AI Use-cases in Live Production as per business/user requirements. Technical Skills: Mandatory Skills: 1. Proficiency in Deep learning engineering, particularly in MLOps. 2. Strong experience in NLP/LLM and processing text using LLM. 3. Proficient in Python & Terraform programming. 4. Building backend applications (e.g., data processing) using Python and Deep learning frameworks. 5. Deployment of models and building APIs (FAST API, FLASK API). 6. Experience working with GPUs. 7. Working knowledge of Vector databases like Milvus, azure cognitive search, quadrant, etc. 8. Experience in transformers and working with hugging face models such as llama, Mixtral AI, and embedding models. Good to have: 1. Knowledge and experience in Kubernetes, Docker, etc. 2. Cloud experience working with VMs and Azure storage. 3. Strong data engineering experience. Behavioral Skills: - Effective communication with clients/operational managers, actively listening and providing solutions. - Strong problem-solving skills, with the ability to gather and assimilate information. - Building solid relationships with clients/operational managers and colleagues. - Adaptability, prioritization skills, ability to work under pressure, and meet deadlines. - Forward-thinking approach, anticipating problems, issues, and solutions. - Innovative mindset with good presentation skills. - Willingness to work extended hours as necessary.,

We are looking for a skilled and enthusiastic Applied AI/ML Engineer to be a part of our team. As an Applied AI/ML Engineer, you will be responsible for leading the entire process of foundational model development, focusing on cutting-edge generative AI techniques. Your main objective will be to implement efficient learning methods for data and compute, specifically addressing challenges relevant to the Indian scenario. Your tasks will involve optimizing model training and inference pipelines, deploying production-ready models, ensuring scalability through distributed systems, and fine-tuning models for domain adaptation. Collaboration with various teams will be essential as you work towards building strong AI stacks and seamlessly integrating them into production pipelines. Apart from conducting research and experiments, you will be crucial in converting advanced models into operational systems that generate tangible results. Your leadership in this field will involve working closely with technical team members and subject matter experts, documenting technical processes, and maintaining well-structured codebases to encourage innovation and reproducibility. This position is perfect for proactive individuals who are passionate about spearheading significant advancements in generative AI and implementing scalable solutions for real-world impact. Your responsibilities will include: - Developing and training foundational models across different modalities - Managing the end-to-end lifecycle of foundational model development, from data curation to model deployment, through collaboration with core team members - Conducting research to enhance model accuracy and efficiency - Applying state-of-the-art AI techniques in Text/Speech and language processing - Collaborating with cross-functional teams to construct robust AI stacks and smoothly integrate them into production pipelines - Creating pipelines for debugging, CI/CD, and observability of the development process - Demonstrating project leadership and offering innovative solutions - Documenting technical processes, model architectures, and experimental outcomes, while maintaining clear and organized code repositories To be eligible for this role, you should hold a Bachelor's or Master's degree in a related field and possess 2 to 5 years of industry experience in applied AI/ML. Minimum requirements for this position include proficiency in Python programming and familiarity with 3-4 tools from the specified list below: - Foundational model libraries and frameworks (TensorFlow, PyTorch, HF Transformers, NeMo, etc) - Experience with distributed training (SLURM, Ray, Pytorch DDP, Deepspeed, NCCL, etc) - Inference servers (vLLM) - Version control systems and observability (Git, DVC, MLFlow, W&B, KubeFlow) - Data analysis and curation tools (Dask, Milvus, Apache Spark, Numpy) - Text-to-Speech tools (Whisper, Voicebox, VALL-E (X), HuBERT/Unitspeech) - LLMOPs Tools, Dockers, etc - Hands-on experience with AI application libraries and frameworks (DSPy, Langgraph, langchain, llamaindex, etc),

As a Senior Machine Learning Engineer with expertise in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), you will play a critical role in supporting a high-impact Proof of Concept (PoC) focused on legacy code analysis, transformation, and modernization. Your primary responsibility will be to enable intelligent code migration and documentation through the implementation of advanced AI/ML tooling. You will be expected to develop and integrate LLM-based pipelines, utilizing tools such as Claude Sonnet 3.7 or 4 on AWS Bedrock. Additionally, you will design and implement RAG-based systems for code understanding, leveraging Vector Databases like Milvus or Pinecone. Your expertise in Abstract Syntax Tree (AST) techniques will be crucial for parsing, analyzing, and transforming legacy code for migration and documentation purposes. You will also apply CodeRAG techniques to facilitate context-aware retrieval and transformation of code artifacts. Iterative validation and correction of AI-generated outputs will be part of your routine to ensure the production of high-quality code assets. Data preprocessing and metadata enrichment, including embeddings, structured knowledge, or fine-tuning for LLM input optimization, will also be within your scope of work. Collaboration with domain experts and engineering teams is essential to ensure alignment with architecture and business logic. You will utilize version control systems like Git to manage changes, support collaboration, and ensure reproducibility. Additionally, you will contribute to QA strategies and help define testing protocols for model output validation. To excel in this role, you must have at least 5 years of experience in Machine Learning, with a strong focus on LLM applications and code understanding. Proficiency in Python and solid software engineering principles are a must, as well as experience working with AWS Bedrock for model deployment and orchestration. You should possess a strong understanding and hands-on experience with AST-based code parsing and transformation, familiarity with RAG architectures, and experience working with vector databases like Milvus, Pinecone, or similar. Experience in preprocessing legacy codebases, enriching metadata for LLM consumption, and using Git or other version control systems in collaborative environments are crucial skills. A solid understanding of code migration, modernization processes, and business logic documentation is also required. Nice-to-have skills include ensuring compliance with architectural and code specifications, documenting code flows, aligning with business requirements, familiarity with QA and testing strategies in AI/ML or code-generation workflows, and a collaborative mindset with strong communication skills and a proactive attitude essential for working in a fast-paced PoC environment with tight feedback loops.,

In-depth experience with the Eliza framework and its agent coordination capabilities In-depth experience with the Agentic AI Practical implementation experience with vector databases (Pinecone, Weaviate, Milvus, or Chroma) Hands-on experience with embedding models (e.g., OpenAI, Cohere, or open-source alternatives) Deep knowledge of LangChain/LlamaIndex for agent memory and tool integration Experience designing and implementing knowledge graphs at scale Strong background in semantic search optimization and efficient RAG architectures Experience with Model Control Plane (MCP) for both LLM orchestration and enterprise system integration Advanced Pythondevelopment with expertise in async patterns and API design

We are looking for a Director of Engineering (AI Systems & Secure Platforms) to join our client&aposs Core Engineering team at Thane (Maharashtra - India). The ideal candidate should have 12-15+ years of experience in architecting and deploying AI systems at scale, with deep expertise in agentic AI workflows, LLMs, RAG, Computer Vision, and secure mobile/wearable platforms. Top 3 Daily Tasks: Architect, optimize, and deploy LLMs, RAG pipelines, and Computer Vision models for smart glasses and other edge devices. Design and orchestrate agentic AI workflowsenabling autonomous agents with planning, tool usage, error handling, and closed feedback loops. Collaborate across AI, Firmware, Security, Mobile, Product, and Design teams to embed "invisible intelligence" within secure wearable systems. Must have 12-15+ years of experience in Applied AI, Deep Learning, Edge AI deployment, Secure Mobile Systems, and Agentic AI Architecture. Must have: Programming languages: Python C/C++ Java Kotlin JavaScript/Node.js Swift Objective-C CUDA Shell scripting Expert in: TensorFlow PyTorch ONNX HuggingFace model optimization with TensorRT TFLite Deep experience with: LLMs RAG pipelines vector DBs (FAISS, Milvus) Proficient in agentic AI workflowsmulti-agent orchestration, planning, feedback loops Strong in privacy-preserving AI (federated learning, differential privacy) Secure real-time comms (WebRTC, SIP, RTP) Nice to have: Experience with MCP or similar protocol frameworks Background in wearables/XR or smart glass AI platforms Expertise in platform security architectures (sandboxing, auditability) Show more Show less

As a Senior Machine Learning Engineer at our AI/ML team, you will be responsible for designing and building intelligent search systems. Your focus will be on utilizing cutting-edge techniques in vector search, semantic similarity, and natural language processing to create innovative solutions. Your key responsibilities will include designing and implementing high-performance vector search systems using tools like FAISS, Milvus, Weaviate, or Pinecone. You will develop semantic search solutions that leverage embedding models and similarity scoring for precise and context-aware retrieval. Additionally, you will be expected to research and integrate the latest advancements in ANN algorithms, transformer-based models, and embedding generation. Collaboration with cross-functional teams, including data scientists, backend engineers, and product managers, will be essential to bring ML-driven features from concept to production. Furthermore, maintaining clear documentation of methodologies, experiments, and findings for technical and non-technical stakeholders will be part of your role. To qualify for this position, you should have at least 3 years of experience in Machine Learning, with a focus on NLP and vector search. A deep understanding of semantic embeddings, transformer models (e.g., BERT, RoBERTa, GPT), and hands-on experience with vector search frameworks is required. You should also possess a solid understanding of similarity search techniques such as cosine similarity, dot-product scoring, and clustering methods. Strong programming skills in Python and familiarity with libraries like NumPy, Pandas, Scikit-learn, and Hugging Face Transformers are necessary. Exposure to cloud platforms, preferably Azure, and container orchestration tools like Docker and Kubernetes is preferred. This is a full-time position with benefits including health insurance, internet reimbursement, and Provident Fund. The work schedule consists of day shifts, fixed shifts, and morning shifts, and the work location is in-person. The application deadline for this role is 18/04/2025.,

Details: Job Description Stefanini Group is a multinational company with a global presence in 41 countries and 44 languages, specializing in technological solutions. We believe in digital innovation and agility to transform businesses for a better future. Our diverse portfolio includes consulting, marketing, mobility, AI services, service desk, field service, and outsourcing solutions. Experience in Deep learning engineering (mostly on MLOps) Strong NLP/LLM experience and processing text using LLM Proficient in Pyspark/Databricks & Python programming. Building backend applications (data processing etc) using Python and Deep learning frame works. Deploying models and building APIS (FAST API, FLASK API) Need to have experience working with GPU&aposS. Working knowledge of Vector databases like 1) Milvus 2) azure cognitive search 3) quadrant etc Experience in transformers and working with hugging face models like llama, Mixtral AI and embedding models etc. Job Requirements Details: Good to have: Knowledge and experience in Kubernetes, docker etc. Cloud Experience working with VM&aposS and azure storage. Sound data engineering experience. Show more Show less

We are looking for a Director of Engineering (AI Systems & Secure Platforms) to join our client&aposs Core Engineering team at Thane (Maharashtra India). The ideal candidate should have 1215+ years of experience in architecting and deploying AI systems at scale, with deep expertise in agentic AI workflows, LLMs, RAG, Computer Vision, and secure mobile/wearable platforms. 1. Top 3 Daily Tasks: - Architect, optimize, and deploy LLMs, RAG pipelines, and Computer Vision models for smart glasses and other edge devices. - Design and orchestrate agentic AI workflowsenabling autonomous agents with planning, tool usage, error handling, and closed feedback loops. - Collaborate across AI, Firmware, Security, Mobile, Product, and Design teams to embed invisible intelligence within secure wearable systems. 2. Must have: - Must have 1215+ years of experience in Applied AI, Deep Learning, Edge AI deployment, Secure Mobile Systems, and Agentic AI Architecture. Programming languages: Python C/C++ Java Kotlin JavaScript/Node.js Swift Objective-C CUDA Shell scripting - Expert in: TensorFlow PyTorch ONNX HuggingFace model optimization with TensorRT TFLite - Deep experience with: LLMs RAG pipelines vector DBs (FAISS, Milvus) - Proficient in agentic AI workflowsmulti-agent orchestration, planning, feedback loops - Strong in privacy-preserving AI (federated learning, differential privacy) - Secure real-time comms (WebRTC, SIP, RTP) 3. Nice to have: - Experience with MCP or similar protocol frameworks - Background in wearables/XR or smart glass AI platforms - Expertise in platform security architectures (sandboxing, auditability) Show more Show less