361 Onnx Jobs - Page 2

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) Industry Technology, Information and Internet Employment Type Full-time Show more Show less

Job Description: Senior MLOps Engineer Position: Senior MLOps Engineer Location: Gurugram Relevant Experience Required: 6+ years Employment Type: Full-time About The Role We are seeking a Senior MLOps Engineer with deep expertise in Machine Learning Operations, Data Engineering, and Cloud-Native Deployments . This role requires building and maintaining scalable ML pipelines , ensuring robust data integration and orchestration , and enabling real-time and batch AI systems in production. The ideal candidate will be skilled in state-of-the-art MLOps tools , data clustering , big data frameworks , and DevOps best practices , ensuring high reliability, performance, and security for enterprise AI workloads. Key Responsibilities MLOps & Machine Learning Deployment Design, implement, and maintain end-to-end ML pipelines from experimentation to production. Automate model training, evaluation, versioning, deployment, and monitoring using MLOps frameworks. Implement CI/CD pipelines for ML models (GitHub Actions, GitLab CI, Jenkins, ArgoCD). Monitor ML systems in production for drift detection, bias, performance degradation, and anomaly detection. Integrate feature stores (Feast, Tecton, Vertex AI Feature Store) for standardized model inputs. Data Engineering & Integration Design and implement data ingestion pipelines for structured, semi-structured, and unstructured data. Handle batch and streaming pipelines with Apache Kafka, Apache Spark, Apache Flink, Airflow, or Dagster. Build ETL/ELT pipelines for data preprocessing, cleaning, and transformation. Implement data clustering, partitioning, and sharding strategies for high availability and scalability. Work with data warehouses (Snowflake, BigQuery, Redshift) and data lakes (Delta Lake, Lakehouse architectures). Ensure data lineage, governance, and compliance with modern tools (DataHub, Amundsen, Great Expectations). Cloud & Infrastructure Deploy ML workloads on AWS, Azure, or GCP using Kubernetes (K8s) and serverless computing (AWS Lambda, GCP Cloud Run). Manage containerized ML environments with Docker, Helm, Kubeflow, MLflow, Metaflow. Optimize for cost, latency, and scalability across distributed environments. Implement infrastructure as code (IaC) with Terraform or Pulumi. Real-Time ML & Advanced Capabilities Build real-time inference pipelines with low latency using gRPC, Triton Inference Server, or Ray Serve. Work on vector database integrations (Pinecone, Milvus, Weaviate, Chroma) for AI-powered semantic search. Enable retrieval-augmented generation (RAG) pipelines for LLMs. Optimize ML serving with GPU/TPU acceleration and ONNX/TensorRT model optimization. Security, Monitoring & Observability Implement robust access control, encryption, and compliance with SOC2/GDPR/ISO27001. Monitor system health with Prometheus, Grafana, ELK/EFK, and OpenTelemetry. Ensure zero-downtime deployments with blue-green/canary release strategies. Manage audit trails and explainability for ML models. Preferred Skills & Qualifications Core Technical Skills Programming: Python (Pandas, PySpark, FastAPI), SQL, Bash; familiarity with Go or Scala a plus. MLOps Frameworks: MLflow, Kubeflow, Metaflow, TFX, BentoML, DVC. Data Engineering Tools: Apache Spark, Flink, Kafka, Airflow, Dagster, dbt. Databases: PostgreSQL, MySQL, MongoDB, Cassandra, DynamoDB. Vector Databases: Pinecone, Weaviate, Milvus, Chroma. Visualization: Plotly Dash, Superset, Grafana. Tech Stack Orchestration: Kubernetes, Helm, Argo Workflows, Prefect. Infrastructure as Code: Terraform, Pulumi, Ansible. Cloud Platforms: AWS (SageMaker, S3, EKS), GCP (Vertex AI, BigQuery, GKE), Azure (ML Studio, AKS). Model Optimization: ONNX, TensorRT, Hugging Face Optimum. Streaming & Real-Time ML: Kafka, Flink, Ray, Redis Streams. Monitoring & Logging: Prometheus, Grafana, ELK, OpenTelemetry.

Job Area: Engineering Group, Engineering Group > Systems Engineering General Summary: We are seeking a passionate and skilled AI/ML Engineer to join our cutting-edge Extended Reality (XR) Software team. In this role, you will work on next-generation XR products that blend the physical and digital worlds, leveraging artificial intelligence and machine learning to create immersive, intelligent, and responsive experiences. You will collaborate with cross-functional teams of researchers, engineers, and designers to build real-time AI/ML software optimized for XR platforms. A strong background in C++ or embedded firmware development is essential, as you will be working close to hardware and performance-critical systems. Key Responsibilities Design, develop, and optimize AI/ML models for XR applications such as computer vision, sensor fusion, gesture recognition, and spatial understanding. Implement real-time inference pipelines on embedded or edge devices. Collaborate with firmware and hardware teams to integrate ML models into XR systems. Analyze system performance and optimize for latency, power, and memory. Stay up to date with the latest research and trends in AI/ML and XR technologies. Contribute to the full lifecycle of product development"”from prototyping to production. Required Qualifications Bachelors or Masters degree in Computer Science, Electrical Engineering, or a related field. 1"“10 years of industry experience in AI/ML engineering or embedded systems. Proficiency in C++ and/or embedded firmware development . Solid understanding of machine learning fundamentals and experience with frameworks like TensorFlow , PyTorch , or ONNX . Experience with deploying ML models on edge devices Familiarity with XR technologies (AR/VR/MR), sensor data processing, or 3D spatial computing. Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience. Applicants Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries). Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law. To all Staffing and Recruiting Agencies Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications. If you would like more information about this role, please contact Qualcomm Careers.

Job Title: AI/ML Validation Engineer Location: Bangalore (Onsite) Experience: 5-8 yrs Requirements: · Strong background in machine learning fundamentals, including deep learning,large language models, and recommender systems. · Strong background in validation, defect and software development life cycle · Strong knowledge on ubuntu / yocto linux · Experience working with opensource frameworks such as PyTorch, TensorFlow, and ONNX-Runtime. · Experience in profiling ML workloads · Prior experience in executing validation plans for AI/ML compute stacks such as HIP, CUDA, OpenCL, OpenVINO, ONNX Runtime and TensorFlow/PyTorch integrations. · Prior experience in validating end-to-end AI pipelines, for e.g. model conversion (e.g., PyTorch à ONNX), Inference runtimes (e.g, ONNX Runtime, TensorRT, ROCm/HIP), compilers/toolchains (e.g. TVM, Vitis AI, XDNA, XLA), kernel execution, memory transfer and inference results · Strong background in python programming. · Excellent problem-solving skills and willingness to think outside the box. · Experience with production software quality assurance practices, methodologies, and procedures · Strong ownership of deliverables, Excellent communication skills and experience working with global teams

Job Description – AI Developer (Agentic AI Frameworks, Computer Vision & LLMs) Location (Hybrid - Bangalore) About the Role We’re seeking an AI Developer who specializes in agentic AI frameworks —LangChain, LangGraph, CrewAI, or equivalents—and who can take both vision and language models from prototype to production. You will lead the design of multi‑agent systems that coordinate perception (image classification & extraction), reasoning, and action, while owning the end‑to‑end deep‑learning life‑cycle (training, scaling, deployment, and monitoring). Key Responsibilities Scope What You’ll Do Agentic AI Frameworks (Primary Focus) Architect and implement multi‑agent workflows using LangChain, LangGraph, CrewAI, or similar. Design role hierarchies, state graphs, and tool integrations that enable autonomous data processing, decision‑making, and orchestration. Benchmark and optimize agent performance (cost, latency, reliability). Image Classification & Extraction Build and fine‑tune CNN/ViT models for classification, detection, OCR, and structured data extraction. Create scalable data‑ingestion, labeling, and augmentation pipelines. LLM Fine‑Tuning & Retrieval‑Augmented Generation (RAG) Fine‑tune open‑weight LLMs with LoRA/QLoRA, PEFT; perform SFT, DPO, or RLHF as needed. Implement RAG pipelines using vector databases (FAISS, Weaviate, pgvector) and domain‑specific adapters. Deep Learning at Scale Develop reproducible training workflows in PyTorch/TensorFlow with experiment tracking (MLflow, W&B). Serve models via TorchServe/Triton/KServe on Kubernetes, SageMaker, or GCP Vertex AI. MLOps & Production Excellence Build robust APIs/micro‑services (FastAPI, gRPC). Establish CI/CD, monitoring (Prometheus, Grafana), and automated retraining triggers. Optimize inference on CPU/GPU/Edge with ONNX/TensorRT, quantization, and pruning. Collaboration & Mentorship Translate product requirements into scalable AI services. Mentor junior engineers, conduct code and experiment reviews, and evangelize best practices. Minimum Qualifications B.S./M.S. in Computer Science, Electrical Engineering, Applied Math, or related discipline. 5+ years building production ML/DL systems with strong Python & Git . Demonstrable expertise in at least one agentic AI framework (LangChain, LangGraph, CrewAI, or comparable). Proven delivery of computer‑vision models for image classification/extraction. Hands‑on experience fine‑tuning LLMs and deploying RAG solutions. Solid understanding of containerization (Docker) and cloud AI stacks (AWS/Azure). Knowledge of distributed training, GPU acceleration, and performance optimization. ---------------------------------------------------------------------------------------------------------------------------------------------------------- Job Type: Full-time Pay: Up to ₹1,200,000.00 per year Experience: AI, LLM, RAG: 4 years (Preferred) Vector database, Image classification: 4 years (Preferred) containerization (Docker): 3 years (Preferred) ML/DL systems with strong Python & Git: 3 years (Preferred) LangChain, LangGraph, CrewAI: 3 years (Preferred) Location: Bangalore, Karnataka (Preferred) Work Location: In person

THE ROLE AMD is looking for a talented, self-driven and motivated engineer to technically lead AIG s Vitis AI Compiler projects working on AMD s XDNA (AI Engine) architecture and the Vitis AI family of software tools. The XDNA is an industry leading NPU (Neural Processing Engine) architecture in terms of performance per watt and is used in AMD s client and embedded devices as the primary engine for Machine Learning workloads. It is the hardware engine behind Windows Co-pilot on AMD devices. The team provides a fast-paced environment offering each of its members immense opportunity to interact with a wide variety of people including from other organizations like hardware designers, marketing, support, and even direct customer interaction, and truly learn and grow their skills and capabilities. THE PERSON: The ideal candidate should be passionate about software engineering and possess leadership skills to drive sophisticated technical issues to resolution. They should have demonstrated ability to identify technical problems, explore and propose viable options, and apply technical solutions. They should be able to excel in a global team environment with strong verbal and written communication skills. KEY RESPONSIBILITIES: Vitis AI is AMD s primary SDK that enables users to compile and run their ML models on the XDNA architecture which forms the basis for AMD s. As a senior member of this high-performance team, the selected candidate will have the opportunity to work on integrating the ML tool chain into frameworks like ONNX, Pytorch, TensorFlow etc. Candidate will have opportunity to work on orchestrating the compilation of ML model through different phases Candidate will integrate runtime execution of ML model on the NPU hardware through the runtime and driver. Candidate will collaborate with compiler and runtime teams to bring up latest AI models like CNNs, Transformers, Stable Diffusion, NLPs etc. on the XDNA simulator. Candidates would develop a deeper understanding of the various ML models, and how they are executed, identify performance bottlenecks and enable faster development. PREFERRED EXPERIENCE: Minimum 10 years of relevant work experience. Strong background in large scale based development and debug, including Design Patterns Experience with multi-threaded programming infrastructure and performance optimization Experience in the software development environment on both Linux and Windows is required. Experience in any one of the ML Framework like ONNX, Pytorch etc is strongly desired. Experience with scalable builds and code versioning through github, docker, CMake, artifactory is highly desired. ACADEMIC CREDENTIALS: Bachelor s or Masters degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent

Backend & MLOps Engineer – Integration, API, and Infrastructure Expert 1.⁠ ⁠Role Objective: Responsible for building robust backend infrastructure, managing ML operations, and creating scalable APIs for AI applications. Must excel in deploying and maintaining AI products in production environments with high availability and security standards. The engineer will be expected to build secure, scalable backend systems that integrate AI models into services (REST, gRPC), manage data pipelines, enable model versioning, and deploy containerized applications in secure (air-gapped) Naval infrastructure. 2.⁠ ⁠Key Responsibilities: 2.1. Create RESTful and/or gRPC APIs for model services. 2.2. Containerize AI applications and maintain Kubernetes-compatible Docker images. 2.3. Develop CI/CD pipelines for model training and deployment. 2.4. Integrate models as microservices using TorchServe, Triton, or FastAPI. 2.5. Implement observability (metrics, logs, alerts) for deployed AI pipelines. 2.6. Build secured data ingestion and processing workflows (ETL/ELT). 2.7. Optimize deployments for CPU/GPU performance, power efficiency, and memory usage 3.⁠ ⁠Educational Qualifications Essential Requirements: 3.1. B.Tech/ M.Tech in Computer Science, Information Technology, or Software Engineering. 3.2. Strong foundation in distributed systems, databases, and cloud computing. 3.3. Minimum 70% marks or 7.5 CGPA in relevant disciplines. Professional Certifications: 3.4. AWS Solutions Architect/DevOps Engineer Professional 3.5. Google Cloud Professional ML Engineer or DevOps Engineer 3.6. Azure AI Engineer or DevOps Engineer Expert. 3.7. Kubernetes Administrator (CKA) or Developer (CKAD). 3.8. Docker Certified Associate Core Skills & Tools 4.⁠ ⁠Backend Development: 4.1. Languages: Python, FastAPI, Flask, Go, Java, Node.js, Rust (for performance-critical components) 4.2. Web Frameworks: FastAPI, Django, Flask, Spring Boot, Express.js. 4.3. API Development: RESTful APIs, GraphQL, gRPC, WebSocket connections. 4.4. Authentication & Security: OAuth 2.0, JWT, API rate limiting, encryption protocols. 5.⁠ ⁠MLOps & Model Management: 5.1. ML Platforms: MLflow, Kubeflow, Apache Airflow, Prefect 5.2. Model Serving: TensorFlow Serving, TorchServe, ONNX Runtime, NVIDIA Triton, BentoML 5.3. Experiment Tracking: Weights & Biases, Neptune, ClearML 5.4. Feature Stores: Feast, Tecton, Amazon SageMaker Feature Store 5.5. Model Monitoring: Evidently AI, Arize, Fiddler, custom monitoring solutions 6.⁠ ⁠Infrastructure & DevOps: 6.1. Containerization: Docker, Podman, container optimization. 6.2. Orchestration: Kubernetes, Docker Swarm, OpenShift. 6.3. Cloud Platforms: AWS, Google Cloud, Azure (multi-cloud expertise preferred). 6.4. Infrastructure as Code: Terraform, CloudFormation, Pulumi, Ansible. 6.5. CI/CD: Jenkins, GitLab CI, GitHub Actions, ArgoCD. 6.6. DevOps & Infra: Docker, Kubernetes, NGINX, GitHub Actions, Jenkins. 7.⁠ ⁠Database & Storage: 7.1. Relational: PostgreSQL, MySQL, Oracle (for enterprise applications) 7.2. NoSQL: MongoDB, Cassandra, Redis, Elasticsearch 7.3. Vector Databases: Pinecone, Weaviate, Chroma, Milvus 7.4. Data Lakes: Apache Spark, Hadoop, Delta Lake, Apache Iceberg 7.5. Object Storage: AWS S3, Google Cloud Storage, MinIO 7.6. Backend: Python (FastAPI, Flask), Node.js (optional) 7.7. DevOps & Infra: Docker, Kubernetes, NGINX, GitHub Actions, Jenkins 8.⁠ ⁠Secure Deployment: 8.1. Military-grade security protocols and compliance 8.2. Air-gapped deployment capabilities 8.3. Encrypted data transmission and storage 8.4. Role-based access control (RBAC) & IDAM integration 8.5. Audit logging and compliance reporting 9.⁠ ⁠Edge Computing: 9.1. Deployment on naval vessels with air gapped connectivity. 9.2. Optimization of applications for resource-constrained environment. 10.⁠ ⁠High Availability Systems: 10.1. Mission-critical system design with 99.9% uptime. 10.2. Disaster recovery and backup strategies. 10.3. Load balancing and auto-scaling. 10.4. Failover mechanisms for critical operations. 11.⁠ ⁠Cross-Compatibility Requirements: 11.1. Define and expose APIs in a documented, frontend-consumable format (Swagger/OpenAPI). 11.2. Develop model loaders for AI Engineer's ONNX/ serialized models. 11.3. Provide UI developers with test environments, mock data, and endpoints. 11.4. Support frontend debugging, edge deployment bundling, and user role enforcement. 12.⁠ ⁠Experience Requirements 12.1. Production experience with cloud platforms and containerization. 12.2. Experience building and maintaining APIs serving millions of requests. 12.3. Knowledge of database optimization and performance tuning. 12.4. Experience with monitoring and alerting systems. 12.5. Architected and deployed large-scale distributed systems. 12.6. Led infrastructure migration or modernization projects. 12.7. Experience with multi-region deployments and disaster recovery. 12.8. Track record of optimizing system performance and cost

Job Description: We are seeking a highly skilled and experienced C++ Engineer to join our team. The primary responsibility will be converting existing Python-based computer vision and deep learning (CVDL) code into optimized, production-ready C++ code. The ideal candidate should be proficient in working with C++ frameworks and libraries, including TensorFlow, PyTorch, ONNX, MNN, NCNN, TensorFlow Lite (TFLite), MMDeploy, etc. The resulting C++ code will be used across Windows and Ubuntu environments, with a strong emphasis on cross-platform compatibility and performance optimization. Key Responsibilities: Convert Python-based CVDL (Computer Vision and Deep Learning) pipelines into optimized C++ implementations. Implement models and algorithms using C++ frameworks such as TensorFlow, PyTorch, ONNX, MNN, NCNN, TensorFlow Lite (TFLite), MMDeploy, and other relevant libraries. Optimize code for performance, ensuring efficient use of resources, especially in real-time processing pipelines. Ensure cross-platform compatibility, building C++ code that works seamlessly on both Windows and Ubuntu using CMakeLists. Debug, profile, and optimize deep learning inference pipelines, addressing issues related to memory usage, speed, and accuracy. Collaborate with AI teams to understand the Python codebase, gather requirements, and ensure the successful porting of features. Maintain up-to-date knowledge of the latest developments in C++ frameworks, deep learning inference engines, and performance optimization techniques. Requirements: Experience: - 5+ years of experience in C++ software development, specifically in converting Python code into C++. - 3 + years of experience with computer vision and deep learning frameworks such as TensorFlow, PyTorch, ONNX, MNN, NCNN, TensorFlow Lite (TFLite), MMDeploy , Mediapipe and Bazel build system. - Solid experience in cross-platform development for both Windows and Ubuntu using CMakeLists. Programming Skills: - Proficiency in C++ (C++11/14/17) with a deep understanding of memory management, multi-threading, and performance optimization. - Familiarity with Python, specifically in computer vision and deep learning applications, to interpret and convert code accurately. - Strong knowledge of CMake for building cross-platform applications. Technical Expertise: - Experience working with deep learning models and converting models between different formats (e.g., TensorFlow to ONNX, PyTorch to NCNN, etc.). - Experience with OpenCV and other related computer vision libraries. - Understanding of inference optimizations such as quantization, pruning, and model acceleration will be plus. Communication: - Strong problem-solving skills and the ability to work in a collaborative, fast-paced environment. - Ability to communicate effectively with cross-functional teams, including data scientists, ML engineers, and Python developers.

We are hring Software Engineers @ Hyderabad location | Experience : 4-5 Yrs | Education : CS or EE/CE degree | NP : 30 Days Mandatory skills: ML, C++ - Must Hands on experience in C/CPP, Python, NumPy, open CLC++ Hand on experience in ML frameworks like TensorFlow, PyTorch and ONNX Hand on experience in TVM REQUIRED KNOWLEDGE, SKILLS, AND ABILITIES: Hands on experience in C, C++, Python, NumPy, ML frameworks like TensorFlow, PyTorch, ONNX and others. Good knowledge of Linear algebra Knowledge of NWs optimization, graph lowering and finetuning Good analytical skills Good understanding of algorithms, OOPS concepts and SW Design Patterns. Good debugging skills. Strong knowledge of TVM FW Experience or knowledge in HW Architecture is an added advantage. Experience on full stack framework development l ike any of Multimedia frameworks, GStreamer, OpenVx, OpenMax, OpenGL, OpenGL-ES, Vulkun, Mesa, etc. is a plus. Experience on driver development on Linux platform . CLC/assembly compute kernels JOB RESPONSIBILITIES: Bring up, test and debug neural networks using ML frameworks like TensorFlow, PyTorch, ONNX etc Bring up and enhance TVM features Analyze and enhance efficiency & stability of neural networks. Develop & maintain Model Conversion Tool software stack Network Optimization, Node fusion, graph lowering, adding custom operations, profiling & performance fine tuning.

Greeting from Leadsoc Technologies Position: AI Model Validation Engineer Strong background in machine learning fundamentals, including deep learning,large language models , and recommender systems. Strong background in validation, defect and software development life cycle Strong knowledge on ubuntu / yocto linux Experience working with opensource frameworks such as PyTorch, TensorFlow, and ONNX-Runtime. Experience in profiling ML workloads Prior experience in executing validation plans for AI/ML compute stacks s uch as HIP, CUDA, OpenCL, OpenVINO, Strong background in python programming. Experience:3- 6 years Notice period: 0-15 days Regrads Murali

Senior AI Research Scientist Location: Sector 63, Gurgaon – On‑site Working Days: Monday to Saturday (2nd and 4th Saturdays are working) Working Hours: 10:30 AM – 8:00 PM Experience: 6–10 years in applied AI/ML research, with multiple publications or patents and demonstrable product impact Apply: careers@darwix.ai Subject Line: Application – Senior AI Research Scientist – [Your Name] About Darwix AI Darwix AI is a GenAI SaaS platform that powers real‑time conversation intelligence, multilingual coaching, and behavioural analytics for large revenue and service teams. Our products— Transform+ , Sherpa.ai , and Store Intel —integrate speech‑to‑text, LLM‑driven analysis, real‑time nudging, and computer vision to improve performance across BFSI, real estate, retail, and healthcare enterprises such as IndiaMart, Wakefit, Bank Dofar, GIVA, and Sobha. Role Overview The Senior AI Research Scientist will own the end‑to‑end research agenda that advances Darwix AI’s core capabilities in speech, natural‑language understanding, and generative AI. You will design novel algorithms, convert them into deployable prototypes, and collaborate with engineering to ship production‑grade features that directly influence enterprise revenue outcomes. Key ResponsibilitiesResearch Leadership Formulate and drive a 12‑ to 24‑month research roadmap covering multilingual speech recognition, conversation summarisation, LLM prompt optimisation, retrieval‑augmented generation (RAG), and behavioural scoring. Publish internal white papers and, where strategic, peer‑reviewed papers or patents to establish technological leadership. Model Development & Prototyping Design and train advanced models (e.g., Whisper fine‑tunes, Conformer‑RNN hybrids, transformer‑based diarisation, LLM fine‑tuning with LoRA/QLoRA). Build rapid prototypes in PyTorch or TensorFlow; benchmark against latency, accuracy, and compute cost targets relevant to real‑time use cases. Production Transfer Work closely with backend and MLOps teams to convert research code into containerised, scalable inference micro‑services. Define evaluation harnesses (WER, BLEU, ROUGE, accuracy, latency) and automate regression tests before every release. Data Strategy Lead data‑curation efforts: multilingual audio corpora, domain‑specific fine‑tuning datasets, and synthetic data pipelines for low‑resource languages. Establish annotation guidelines, active‑learning loops, and data quality metrics. Cross‑Functional Collaboration Act as the principal technical advisor in customer POCs involving custom language models, domain‑specific ontologies, or privacy‑sensitive deployments. Mentor junior researchers and collaborate with product managers on feasibility assessments and success metrics for AI‑driven features. Required Qualifications 6–10 years of hands‑on research in ASR, NLP, or multimodal AI, including at least three years in a senior or lead capacity. Strong publication record (top conferences such as ACL, INTERSPEECH, NeurIPS, ICLR, EMNLP) or patents showing applied innovation. Expert‑level Python and deep‑learning fluency (PyTorch or TensorFlow); comfort with Hugging Face, OpenAI APIs, and distributed training. Proven experience delivering research outputs into production systems with measurable business impact. Solid grasp of advanced topics: sequence‑to‑sequence modelling, attention mechanisms, LLM alignment, speaker diarisation, vector search, on‑device optimisation. Preferred Qualifications Experience with Indic or Arabic speech/NLP, code‑switching, or low‑resource language modelling. Familiarity with GPU orchestration, Triton inference servers, TorchServe, or ONNX runtime optimisation. Prior work on enterprise call‑centre datasets, sales enablement analytics, or real‑time speech pipelines. Doctorate (PhD) in Computer Science, Electrical Engineering, or a closely related field from a Tier 1 institution. Success Metrics Reduction of transcription error rate and/or inference latency by agreed percentage targets within 12 months. Successful deployment of at least two novel AI modules into production with adoption across Tier‑1 client accounts. Internal citation and reuse of developed components in other product lines. Peer‑recognised technical leadership through mentoring, documentation, and knowledge sharing. Application Process Send your résumé (and publication list, if separate) to careers@darwix.ai with the subject line indicated above. Optionally, include a one‑page summary of a research project you transitioned from lab to production, detailing the problem, approach, and measured impact. Joining Darwix AI as a Senior AI Research Scientist means shaping the next generation of real‑time, multilingual conversational intelligence for enterprise revenue teams worldwide. If you are passionate about applied research that moves the business needle, we look forward to hearing from you.

Applied Machine Learning Scientist – Voice AI, NLP & GenAI Applications Location : Sector 63, Gurugram, Haryana – 100% In-Office Working Days : Monday to Friday, with 2nd and 4th Saturdays off Working Hours : 10:30 AM – 8:00 PM Experience : 3–7 years in applied ML, with at least 2 years focused on voice, NLP, or GenAI deployments Function : AI/ML Research & Engineering | Conversational Intelligence | Real-time Model Deployment Apply : careers@darwix.ai Subject Line : “Application – Applied ML Scientist – [Your Name]” About Darwix AI Darwix AI is a GenAI-powered platform transforming how enterprise sales, support, and credit teams engage with customers. Our proprietary AI stack ingests data across calls, chat, email, and CCTV streams to generate: Real-time nudges for agents and reps Conversational analytics and scoring to drive performance CCTV-based behavior insights to boost in-store conversion We’re live across leading enterprises in India and MENA, including IndiaMart, Wakefit, Emaar, GIVA, Bank Dofar , and others. We’re backed by top-tier operators and venture investors and scaling rapidly across multiple verticals and geographies. Role Overview We are looking for a hands-on, impact-driven Applied Machine Learning Scientist to build, optimize, and productionize AI models across ASR, NLP, and LLM-driven intelligence layers . This is a core role in our AI/ML team where you’ll be responsible for building the foundational ML capabilities that drive our real-time sales intelligence platform. You will work on large-scale multilingual voice-to-text pipelines, transformer-based intent detection, and retrieval-augmented generation systems used in live enterprise deployments. Key ResponsibilitiesVoice-to-Text (ASR) Engineering Deploy and fine-tune ASR models such as WhisperX, wav2vec 2.0, or DeepSpeech for Indian and GCC languages Integrate diarization and punctuation recovery pipelines Benchmark and improve transcription accuracy across noisy call environments Optimize ASR latency for real-time and batch processing modes NLP & Conversational Intelligence Train and deploy NLP models for sentence classification, intent tagging, sentiment, emotion, and behavioral scoring Build call scoring logic aligned to domain-specific taxonomies (sales pitch, empathy, CTA, etc.) Fine-tune transformers (BERT, RoBERTa, etc.) for multilingual performance Contribute to real-time inference APIs for NLP outputs in live dashboards GenAI & LLM Systems Design and test GenAI prompts for summarization, coaching, and feedback generation Integrate retrieval-augmented generation (RAG) using OpenAI, HuggingFace, or open-source LLMs Collaborate with product and engineering teams to deliver LLM-based features with measurable accuracy and latency metrics Implement prompt tuning, caching, and fallback strategies to ensure system reliability Experimentation & Deployment Own model lifecycle: data preparation, training, evaluation, deployment, monitoring Build reproducible training pipelines using MLflow, DVC, or similar tools Write efficient, well-structured, production-ready code for inference APIs Document experiments and share insights with cross-functional teams Required Qualifications Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related fields 3–7 years experience applying ML in production, including NLP and/or speech Experience with transformer-based architectures for text or audio (e.g., BERT, Wav2Vec, Whisper) Strong Python skills with experience in PyTorch or TensorFlow Experience with REST APIs, model packaging (FastAPI, Flask, etc.), and containerization (Docker) Familiarity with audio pre-processing, signal enhancement, or feature extraction (MFCC, spectrograms) Knowledge of MLOps tools for experiment tracking, monitoring, and reproducibility Ability to work collaboratively in a fast-paced startup environment Preferred Skills Prior experience working with multilingual datasets (Hindi, Arabic, Tamil, etc.) Knowledge of diarization and speaker separation algorithms Experience with LLM APIs (OpenAI, Cohere, Mistral, LLaMA) and RAG pipelines Familiarity with inference optimization techniques (quantization, ONNX, TorchScript) Contribution to open-source ASR or NLP projects Working knowledge of AWS/GCP/Azure cloud platforms What Success Looks Like Transcription accuracy improvement ≥ 85% across core languages NLP pipelines used in ≥ 80% of Darwix AI’s daily analyzed calls 3–5 LLM-driven product features delivered in the first year Inference latency reduced by 30–50% through model and infra optimization AI features embedded across all Tier 1 customer accounts within 12 months Life at Darwix AI You will be working in a high-velocity product organization where AI is core to our value proposition. You’ll collaborate directly with the founding team and cross-functional leads, have access to enterprise datasets, and work on ML systems that impact large-scale, real-time operations. We value rigor, ownership, and speed. Model ideas become experiments in days, and successful experiments become deployed product features in weeks. Compensation & Perks Competitive fixed salary based on experience Quarterly/Annual performance-linked bonuses ESOP eligibility post 12 months Compute credits and model experimentation environment Health insurance, mental wellness stipend Premium tools and GPU access for model development Learning wallet for certifications, courses, and AI research access Career Path Year 1: Deliver production-grade ASR/NLP/LLM systems for high-usage product modules Year 2: Transition into Senior Applied Scientist or Tech Lead for conversation intelligence Year 3: Grow into Head of Applied AI or Architect-level roles across vertical product lines How to Apply Email the following to careers@darwix.ai : Updated resume (PDF) A short write-up (200 words max): “How would you design and optimize a multilingual voice-to-text and NLP pipeline for noisy call center data in Hindi and English?” Optional: GitHub or portfolio links demonstrating your work Subject Line : “Application – Applied Machine Learning Scientist – [Your Name]”

Job Description As a Python Backend Engineer with exposure to AI engineering at Quantanite, you will be an integral part of our team responsible for building a scalable, cognitive data platform. Your role will involve designing and developing high-performance backend services using Python (FastAPI), developing RESTful APIs for data ingestion, transformation, and AI-based feature access, and collaborating closely with DevOps and data engineering teams to integrate backend services with Azure data pipelines and databases. Your primary responsibilities will include managing database schemas, writing complex SQL queries, and supporting ETL processes using Python-based tools. Additionally, you will be tasked with building secure, scalable, and production-ready services that adhere to best practices in logging, authentication, and observability. You will also implement background tasks and async event-driven workflows for data crawling and processing. In terms of AI engineering contributions, you will support the integration of AI models (NLP, summarization, information retrieval) within backend APIs. You will collaborate with the AI team to deploy lightweight inference pipelines using PyTorch, TensorFlow, or ONNX, and participate in training data pipeline design and minor model fine-tuning as needed for business logic. Furthermore, you will contribute to the testing, logging, and monitoring of AI agent behavior in production environments. To be successful in this role, you should have at least 3 years of experience in Python backend development, with a strong proficiency in FastAPI or equivalent frameworks. A solid understanding of RESTful API design, asynchronous programming, and web application architecture is essential. Additionally, you should demonstrate proficiency in working with relational databases (e.g., PostgreSQL, MS SQL Server) and Azure cloud services, as well as experience with ETL workflows, job scheduling, and data pipeline orchestration (Airflow, Prefect, etc.). Exposure to machine learning libraries (e.g., Scikit-learn, Transformers, OpenAI APIs) is a plus, along with familiarity with containerization (Docker), CI/CD practices, and performance tuning. A mindset of code quality, scalability, documentation, and collaboration is highly valued at Quantanite. If you are looking for a challenging yet rewarding opportunity to work in a collaborative environment with a focus on innovation and growth, we encourage you to apply to join our dynamic team at Quantanite.,

We are looking for a highly experienced Voice AI /ML Engineer to take the lead in designing and deploying real-time voice intelligence systems. This position specifically involves working on ASR, TTS, speaker diarization, wake word detection, and developing production-grade modular audio processing pipelines to support next-generation contact center solutions, intelligent voice agents, and high-quality audio systems. You will be operating at the convergence of deep learning, streaming infrastructure, and speech/NLP technology, with a focus on creating scalable, low-latency systems that cater to diverse audio formats and real-world applications. Your responsibilities will include: - Building, fine-tuning, and deploying ASR models such as Whisper, wav2vec2.0, and Conformer for real-time transcription. - Developing high-quality TTS systems using VITS, Tacotron, FastSpeech for natural-sounding voice generation. - Implementing speaker diarization to segment and identify speakers in multi-party conversations using embeddings and clustering techniques. - Designing wake word detection models with ultra-low latency and high accuracy even in noisy conditions. In addition to the above, you will also be involved in: - Architecting bi-directional real-time audio streaming pipelines utilizing WebSocket, gRPC, Twilio Media Streams, or WebRTC. - Integrating voice AI models into live voice agent solutions, IVR automation, and AI contact center platforms. - Building scalable microservices for audio processing, encoding, and streaming across various codecs and containers. - Leveraging deep learning and NLP techniques for speech and language tasks. Furthermore, you will be responsible for: - Developing reusable modules for different voice tasks and system components. - Designing APIs and interfaces for orchestrating voice tasks across multi-stage pipelines. - Writing efficient Python code, optimizing models for real-time inference, and deploying them on cloud platforms. Join us to be part of impactful work, tremendous growth opportunities, and an innovative environment at Tanla, where diversity is championed and inclusivity is valued.,

Job Description Oracle Cloud Infrastructure (OCI) is a strategic growth area for Oracle. It is a comprehensive cloud service offering in the enterprise software industry, spanning Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). OCI is currently building a future-ready Gen2 cloud Data Science service platform. At the core of this platform, lies Cloud AI Cloud Service. What OCI AI Cloud Services are: A set of services on the public cloud, that are powered by ML and AI to meet the Enterprise modernization needs, and that work out of the box. These services and models can be easily specialized for specific customers/domains by demonstrating existing OCI services. Key Points: Enables customers to add AI capabilities to their Apps and Workflows easily via APIs or Containers, Useable without needing to build AI expertise in-house and Covers key gaps – Decision Support, NLP, for Public Clouds and Enterprise in NLU, NLP, Vision and Conversational AI. You’re Opportunity: As we innovate to provide a single collaborative ML environment for data-science professionals, we will be extremely happy to have you join us and share the very future of our Machine Learning platform - by building an AI Cloud service. We are addressing exciting challenges at the intersection of artificial intelligence and innovative cloud infrastructure. We are building cloud services in Computer vision for Image/Video and Document Analysis, Decision Support (Anomaly Detection, Time series forecasting, Fraud detection, Content moderation, Risk prevention, predictive analytics), Natural Language Processing (NLP), and, Speech that works out of the box for enterprises. Our product vision includes the ability for enterprises to be able to customize the services for their business and train them to specialize in their data by creating micro models that enhance the global AI models. What You’ll Do Develop scalable infrastructure, including microservices and a backend, that automates training, deployment, and optimization of ML model inference. Building a core of Artificial Intelligence and AI services such as Vision, Speech, Language, Decision, and others. Brainstorm and design various POCs using AI Perpetual AI Services for new or existing enterprise problems. Collaborate with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively communicating your needs, understanding theirs, and addressing external and internal shareholder product challenges. Lead research and development efforts to explore new tools, frameworks, and methodologies to improve backend development processes. Experiment with ML models in Python/C++ using machine learning libraries (Pytorch, ONNX, TensorRT, Triton, TensorFlow, Jax), etc. Leverage Cloud technology – Oracle Cloud (OCI), AWS, GCP, Azure, or similar technology. Qualifications Master’s degree or equivalent experience (preferred) in computer science, Statistics or Mathematics, artificial intelligence, machine learning, Computer vision, operations research, or related technical field. 3+ years for PhD or equivalent experience, 5+ years for Masters, or demonstrated ability designing, implementing, and deploying machine learning models in production environments. Practical experience in design, implementation, and production deployment of distributed systems using microservices architecture and APIs using common frameworks like Spring Boot (Java), etc. Practical experience working in a cloud environment: Oracle Cloud (OCI), AWS, GCP, Azure, and containerization (Docker, Kubernetes). Working knowledge of current techniques, approaches, and inference optimization strategies in machine learning models. Experience with performance tuning, scalability, and load balancing techniques. Expert in at least one high-level language such as Java/C++ (Java preferred). Expert in at least one scripting language such as Python, Javascript, and Shell . Deep understanding of data structures, and algorithms, and excellent problem-solving skills. Experience or willingness to learn and work in Agile and iterative development and DevOps processes. Strong drive to learn and master new technologies and techniques. You enjoy a fast-paced work environment. Additional Preferred Qualifications Experience with Cloud Native Frameworks tools and products is a plus Experience in Computer vision tasks like Image Classification, Object Detection, Segmentation, Text detection & recognition, Information extraction from documents, etc. Having an impressive set of GitHub projects or contributions to open-source technologies is a plus Hands-on experience with horizontally scalable data stores such as Hadoop and other NoSQL technologies like Cassandra is a plus. Our vision is to provide an immersive AI experience on Oracle Cloud. Aggressive as it might sound, our growth journey is fueled by highly energetic, technology-savvy engineers like YOU who are looking to grow with us to meet the demands of building a powerful next-generation platform. Are you ready to do something big? Career Level - IC3 About Us As a world leader in cloud solutions, Oracle uses tomorrow’s technology to tackle today’s challenges. We’ve partnered with industry-leaders in almost every sector—and continue to thrive after 40+ years of change by operating with integrity. We know that true innovation starts when everyone is empowered to contribute. That’s why we’re committed to growing an inclusive workforce that promotes opportunities for all. Oracle careers open the door to global opportunities where work-life balance flourishes. We offer competitive benefits based on parity and consistency and support our people with flexible medical, life insurance, and retirement options. We also encourage employees to give back to their communities through our volunteer programs. We’re committed to including people with disabilities at all stages of the employment process. If you require accessibility assistance or accommodation for a disability at any point, let us know by emailing accommodation-request_mb@oracle.com or by calling +1 888 404 2494 in the United States. Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

Title: - AI Engineer Location: Remote Type: Full-time Experience: 2–5 years About Omelo Omelo is India’s first intelligent pet health companion, an AI-powered assistant that helps pet parents care for their pets proactively. From image-based symptom scanning (skin, eyes, stool) to multilingual chat and vet-backed guidance, we’re building the health stack pets deserve. Every year, millions of pets in India go undiagnosed due to a lack of access or awareness. At Omelo, we’re changing that, with inclusive, AI-first tools accessible via WhatsApp, web, and mobile. Why This Role Matters AI isn’t a support function here; it is the product. From visual health scans to symptom conversations, every user interaction is powered by real-time AI. What You’ll Work On Build and scale computer vision models to detect pet health symptoms (e.g., rashes, eye infections, stool quality) Enhance our multimodal AI assistant, combining vision, NLP, and structured inputs Lead end-to-end AI workflows: data labeling, training, optimization, and deployment Tune models with real-world feedback loops, improving performance across diverse pet types and conditions Collaborate with product, design, and vet advisors to ship features that are clinically useful and intuitive What Success Looks Like In 3–6 Months Ship 2 high-impact CV modules (e.g., skin or stool detection) used in production Improve top-line model precision/recall by 30% via iteration and feedback Deploy your models into production across mobile/web with real user traction Contribute meaningfully to our core health scoring and assistant logic What We’re Looking For 2–5 years of experience building and shipping ML models, especially in health, agri, food, or diagnostic tech Strong hands-on experience with computer vision (CNNs, classification, segmentation, etc.) Comfort with NLP or LLMs for enhancing chat-based UX (Hugging Face, LangChain, etc.) Fluent in Python and familiar with tools like TensorFlow/PyTorch, FastAPI, GCP/AWS Bonus: Experience with mobile inference (ONNX, Core ML, TensorFlow Lite) Bonus: You’ve worked with real-world health or animal datasets (not just academic ones) Tools & Workflow We Work With Python, PyTorch, TensorFlow, Hugging Face FastAPI, Label Studio, ONNX GCP, Firebase, Postgres You don’t need to know them all, but familiarity helps! What We Offer AI is the product, not a side project Real-world pet health data, with vet-labeled image and symptom datasets Direct collaboration with the founder, engineers, and vet experts Fast cycles, zero bureaucracy, and full model ownership Path to early equity and technical leadership for the right person Skills: machine learning,multimodal ai,nlp,datasets,python,mobile,models,ai,data,bonus,building,pytorch,aws,fastapi,tensorflow,computer vision,chat,gcp,health

Company Description Quantanite is a business process outsourcing (BPO) and customer experience (CX) solutions company that helps fast-growing companies and leading global brands to transform and grow. We do this through a collaborative and consultative approach, rethinking business processes and ensuring our clients employ the optimal mix of automation and human intelligence. We’re an ambitious team of professionals spread across four continents and looking to disrupt our industry by delivering seamless customer experiences for our clients, backed up with exceptional results. We have big dreams and are constantly looking for new colleagues to join us who share our values, passion, and appreciation for diversity Job Description We are looking for a Python Backend Engineer with exposure to AI engineering to join our team in building a scalable, cognitive data platform. This platform will crawl and process unstructured data sources, enabling intelligent data extraction and analysis. The ideal candidate will have deep expertise in backend development using FastAPI, RESTful APIs, SQL, and Azure data technologies, with a secondary focus on integrating AI/ML capabilities into the product. Core Responsibilities Design and develop high-performance backend services using Python (FastAPI). Develop RESTful APIs to support data ingestion, transformation, and AI-based feature access. Work closely with DevOps and data engineering teams to integrate backend services with Azure data pipelines and databases. Manage database schemas, write complex SQL queries, and support ETL processes using Python-based tools. Build secure, scalable, and production-ready services following best practices in logging, authentication, and observability. Implement background tasks and async event-driven workflows for data crawling and processing. AI Engineering Contributions : Support integration of AI models (NLP, summarization, information retrieval) within backend APIs. Collaborate with AI team to deploy lightweight inference pipelines using PyTorch, TensorFlow, or ONNX. Participate in training data pipeline design and minor model fine-tuning as needed for business logic. Contribute to the testing, logging, and monitoring of AI agent behavior in production environments. Qualifications 3+ years of experience in Python backend development, with strong experience in FastAPI or equivalent frameworks. Solid understanding of RESTful API design, asynchronous programming, and web application architecture. Proficiency in working with relational databases (e.g., PostgreSQL, MS SQL Server) and Azure cloud services. Experience with ETL workflows, job scheduling, and data pipeline orchestration (Airflow, Prefect, etc.). Exposure to machine learning libraries (e.g., Scikit-learn, Transformers, OpenAI APIs) is a plus. Familiarity with containerization (Docker), CI/CD practices, and performance tuning. A mindset of code quality, scalability, documentation, and collaboration. Additional Information Benefits At Quantanite, we ask a lot of our associates, which is why we give so much in return. In addition to your compensation, our perks include: Dress: Wear anything you like to the office. We want you to feel as comfortable as when working from home. Employee Engagement: Experience our family community and embrace our culture where we bring people together to laugh and celebrate our achievements. Professional development: We love giving back and ensure you have opportunities to grow with us and even travel on occasion. Events: Regular team and organisation-wide get-togethers and events. Value orientation: Everything we do at Quantanite is informed by our Purpose and Values. We Build Better. Together. Future development: At Quantanite, you’ll have a personal development plan to help you improve in the areas you’re looking to develop over the coming years. Your manager will dedicate time and resources to supporting you in getting you to the next level. You’ll also have the opportunity to progress internally. As a fast-growing organization, our teams are growing, and you’ll have the chance to take on more responsibility over time. So, if you’re looking for a career full of purpose and potential, we’d love to hear from you!

Job Description Oracle Cloud Infrastructure (OCI) is a strategic growth area for Oracle. It is a comprehensive cloud service offering in the enterprise software industry, spanning Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). OCI is currently building a future-ready Gen2 cloud Data Science service platform. At the core of this platform, lies Cloud AI Cloud Service. What OCI AI Cloud Services are: A set of services on the public cloud, that are powered by ML and AI to meet the Enterprise modernization needs, and that work out of the box. These services and models can be easily specialized for specific customers/domains by demonstrating existing OCI services. Key Points: Enables customers to add AI capabilities to their Apps and Workflows easily via APIs or Containers, Useable without needing to build AI expertise in-house and Covers key gaps – Decision Support, NLP, for Public Clouds and Enterprise in NLU, NLP, Vision and Conversational AI. You’re Opportunity: As we innovate to provide a single collaborative ML environment for data-science professionals, we will be extremely happy to have you join us and share the very future of our Machine Learning platform - by building an AI Cloud service. We are addressing exciting challenges at the intersection of artificial intelligence and innovative cloud infrastructure. We are building cloud services in Computer vision for Image/Video and Document Analysis, Decision Support (Anomaly Detection, Time series forecasting, Fraud detection, Content moderation, Risk prevention, predictive analytics), Natural Language Processing (NLP), and, Speech that works out of the box for enterprises. Our product vision includes the ability for enterprises to be able to customize the services for their business and train them to specialize in their data by creating micro models that enhance the global AI models. What You’ll Do Develop scalable infrastructure, including microservices and a backend, that automates training, deployment, and optimization of ML model inference. Building a core of Artificial Intelligence and AI services such as Vision, Speech, Language, Decision, and others. Brainstorm and design various POCs using AI Perpetual AI Services for new or existing enterprise problems. Collaborate with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively communicating your needs, understanding theirs, and addressing external and internal shareholder product challenges. Lead research and development efforts to explore new tools, frameworks, and methodologies to improve backend development processes. Experiment with ML models in Python/C++ using machine learning libraries (Pytorch, ONNX, TensorRT, Triton, TensorFlow, Jax), etc. Leverage Cloud technology – Oracle Cloud (OCI), AWS, GCP, Azure, or similar technology. Qualifications Master’s degree or equivalent experience (preferred) in computer science, Statistics or Mathematics, artificial intelligence, machine learning, Computer vision, operations research, or related technical field. 3+ years for PhD or equivalent experience, 5+ years for Masters, or demonstrated ability designing, implementing, and deploying machine learning models in production environments. Practical experience in design, implementation, and production deployment of distributed systems using microservices architecture and APIs using common frameworks like Spring Boot (Java), etc. Practical experience working in a cloud environment: Oracle Cloud (OCI), AWS, GCP, Azure, and containerization (Docker, Kubernetes). Working knowledge of current techniques, approaches, and inference optimization strategies in machine learning models. Experience with performance tuning, scalability, and load balancing techniques. Expert in at least one high-level language such as Java/C++ (Java preferred). Expert in at least one scripting language such as Python, Javascript, and Shell . Deep understanding of data structures, and algorithms, and excellent problem-solving skills. Experience or willingness to learn and work in Agile and iterative development and DevOps processes. Strong drive to learn and master new technologies and techniques. You enjoy a fast-paced work environment. Additional Preferred Qualifications Experience with Cloud Native Frameworks tools and products is a plus Experience in Computer vision tasks like Image Classification, Object Detection, Segmentation, Text detection & recognition, Information extraction from documents, etc. Having an impressive set of GitHub projects or contributions to open-source technologies is a plus Hands-on experience with horizontally scalable data stores such as Hadoop and other NoSQL technologies like Cassandra is a plus. Our vision is to provide an immersive AI experience on Oracle Cloud. Aggressive as it might sound, our growth journey is fueled by highly energetic, technology-savvy engineers like YOU who are looking to grow with us to meet the demands of building a powerful next-generation platform. Are you ready to do something big? Career Level - IC3 About Us As a world leader in cloud solutions, Oracle uses tomorrow’s technology to tackle today’s challenges. We’ve partnered with industry-leaders in almost every sector—and continue to thrive after 40+ years of change by operating with integrity. We know that true innovation starts when everyone is empowered to contribute. That’s why we’re committed to growing an inclusive workforce that promotes opportunities for all. Oracle careers open the door to global opportunities where work-life balance flourishes. We offer competitive benefits based on parity and consistency and support our people with flexible medical, life insurance, and retirement options. We also encourage employees to give back to their communities through our volunteer programs. We’re committed to including people with disabilities at all stages of the employment process. If you require accessibility assistance or accommodation for a disability at any point, let us know by emailing accommodation-request_mb@oracle.com or by calling +1 888 404 2494 in the United States. Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

About the Role: We are seeking a highly experienced Voice AI /ML Engineer to lead the design and deployment of real-time voice intelligence systems. This role focuses on ASR, TTS, speaker diarization, wake word detection, and building production-grade modular audio processing pipelines to power next-generation contact centre solutions, intelligent voice agents, and telecom-grade audio systems. You will work at the intersection of deep learning, streaming infrastructure, and speech/NLP technology, creating scalable, low-latency systems across diverse audio formats and real-world applications. Key Responsibilities: Voice & Audio Intelligence:  Build, fine-tune, and deploy ASR models (e.g., Whisper, wav2vec2.0, Conformer) for real-time transcription.  Develop and finetune high-quality TTS systems using VITS, Tacotron, FastSpeech for lifelike voice generation and cloning.  Implement speaker diarization for segmenting and identifying speakers in multi-party conversations using embeddings (x-vectors/d-vectors) and clustering (AHC, VBx, spectral clustering).  Design robust wake word detection models with ultra-low latency and high accuracy in noisy conditions. Real-Time Audio Streaming & Voice Agent Infrastructure:  Architect bi-directional real-time audio streaming pipelines using WebSocket, gRPC, Twilio Media Streams, or WebRTC.  Integrate voice AI models into live voice agent solutions, IVR automation, and AI contact center platforms.  Optimize for latency, concurrency, and continuous audio streaming with context buffering and voice activity detection (VAD).  Build scalable microservices to process, decode, encode, and stream audio across common codecs (e.g., PCM, Opus, μ-law, AAC, MP3) and containers (e.g., WAV, MP4). Deep Learning & NLP Architecture:  Utilize transformers, encoder-decoder models, GANs, VAEs, and diffusion models, for speech and language tasks.  Implement end-to-end pipelines including text normalization, G2P mapping, NLP intent extraction, and emotion/prosody control.  Fine-tune pre-trained language models for integration with voice-based user interfaces. Modular System Development:  Build reusable, plug-and-play modules for ASR, TTS, diarization, codecs, streaming inference, and data augmentation.  Design APIs and interfaces for orchestrating voice tasks across multi-stage pipelines with format conversions and buffering.  Develop performance benchmarks and optimize for CPU/GPU, memory footprint, and real-time constraints. Engineering & Deployment:  Writing robust, modular, and efficient Python code  Experience with Docker, Kubernetes, cloud deployment (AWS, Azure, GCP)  Optimize models for real-time inference using ONNX, TorchScript, and CUDA, including quantization, context-aware inference, model caching.  On device voice model deployment. Why join us?  Impactful Work: Play a pivotal role in safeguarding Tanla's assets, data, and reputation in the industry.  Tremendous Growth Opportunities: Be part of a rapidly growing company in the telecom and CPaaS space, with opportunities for professional development.  Innovative Environment: Work alongside a world-class team in a challenging and fun environment, where innovation is celebrated. Tanla is an equal opportunity employer. We champion diversity and are committed to creating an inclusive environment for all employees. www.tanla.com

Senior Assistant Vice President EXL/SAVP/1418398 Digital SolutionsNoida Posted On 24 Jul 2025 End Date 07 Sep 2025 Required Experience 8 - 12 Years Basic Section Number Of Positions 1 Band D2 Band Name Senior Assistant Vice President Cost Code D014959 Campus/Non Campus NON CAMPUS Employment Type Permanent Requisition Type New Max CTC 4500000.0000 - 6000000.0000 Complexity Level Not Applicable Work Type Hybrid – Working Partly From Home And Partly From Office Organisational Group EXL Digital Sub Group Digital Solutions Organization Digital Solutions LOB CX Transformation Practice SBU CX Capability Development Country India City Noida Center Noida - Centre 59 Skills Skill TECHNICAL CONSULTING DATA SCIENCE - AI PRE-SALES CONSULTING SOLUTIONING Minimum Qualification GRADUATION Certification No data available Job Description Job Description Designation and SRF Name- Technical CX Consultant role Role- Permanent/ Full time Panel and Hiring Manager- Sanjay Pathak Experience- 8-12 years relevant experience Location- Noida/ Gurgaon/ Pune/ Bangalore Shift- 12PM to 10PM (10 Hours Shift. Also depends on the project/work dependencies) Working Days- 5 days Work Mode- Hybrid Job Description: Highly skilled CX Consulting with deep expertise in CCaasS, Integrations, IVR, Natural Language Processing (NLP), Language Models, and scalable cloud-based solution deployment. Skills: Technical Expertise: Having a deep understanding of Conversational AI, Smart Agent Assist, CCaaS and their technical capabilities. Stay current with industry trends, emerging technologies, and competitor offerings. Customer Engagement: Engage with prospective clients to understand their technical requirements and business challenges. Conduct needs assessments and provide tailored technical solutions. Solution Demonstrations: Deliver compelling product demonstrations that showcase the features and benefits of our solutions. Customize demonstrations to align with the specific needs and use cases of potential customers. Strong NLP and Language Model fundamentals (e.g., transformer architectures, embeddings, tokenization, fine-tuning). Expert in Python, with clean, modular, and scalable coding practices. Experience developing and deploying solutions on Azure, AWS, or Google Cloud Platform. Familiarity with Vertex AI, including Model Registry, Pipelines, and RAG integrations (preferred). Experience with PyTorch, including model training, evaluation, and serving. Knowledge of GPU-based inferencing (e.g., ONNX, Torch Script, Triton Inference Server). Understanding of ML lifecycle management, including MLOps best practices. Experience with containerization (Docker) and orchestration tools (e.g., Kubernetes). Exposure to REST APIs, gRPC, and real-time data pipelines is a plus. Degree in Computer Science, Mathematics, Computational Linguistics, AI, ML or similar field. PhD is a plus. Responsibilities: Consulting and design end-to-end AI solutions for CX. Consulting engagement of scalable AI services on cloud infrastructure (Azure/AWS/GCP). Collaborate with engineering, product, and data teams to define AI-driven features and solutions. Optimize model performance, scalability, and cost across CPU and GPU environments. Ensure reliable model serving with a focus on low-latency, high-throughput inferencing. Keep abreast of the latest advancements in NLP, LLMs, and AI infrastructure. Workflow Workflow Type Digital Solution Center

computer vision or deep learning roles industrial/safety inspection datasets (e.g., PPE detection, visual defect classification). Familiarity with MLOps tools like MLflow, DVC, or ClearML. ONNX, TensorRT, OpenVINO

You are a Java Developer with AI/ML experience, required to have at least 5+ years of industry experience in Java, Spring Boot, Spring Data, and a minimum of 2 years of AI/ML project or professional experience. You should possess a strong background in developing and consuming REST APIs and asynchronous messaging using technologies like Kafka or RabbitMQ. Your role involves integrating AI/ML models into Java services or making calls to external ML endpoints. You need to have a comprehensive understanding of the ML lifecycle encompassing training, validation, inference, monitoring, and retraining. Familiarity with tools such as TensorFlow, PyTorch, Scikit-Learn, or ONNX is essential. Previous experience in implementing domain-specific ML solutions like fraud detection, recommendation systems, or NLP chatbots is beneficial. Proficiency in working with various data formats including JSON, Parquet, Avro, and CSV is required. You should have a solid grasp of both SQL (PostgreSQL, MySQL) and NoSQL (Redis) database systems. Your responsibilities will include integrating machine learning models (both batch and real-time) into backend systems and APIs, optimizing and automating AI/ML workflows using MLOps best practices, and monitoring model performance, versioning, and rollbacks. Collaboration with cross-functional teams such as DevOps, SRE, and Product Engineering is necessary to ensure smooth deployment. Exposure to MLOps tools like MLflow, Kubeflow, or Seldon is desired. Experience with at least one cloud platform, preferably AWS, and knowledge of observability tools, metrics, events, logs, and traces (e.g., Prometheus, Grafana, Open Telemetry, Splunk, Data Dog, App Dynamics) are valuable skills in this role. Thank you. Aatmesh,

As an AI/ML Lead specializing in Facial Recognition & Video Intelligence at Live Eye Surveillance, your primary responsibility will be to drive the development of advanced features for our AI-powered Video Management Software (VMS) platform. You will play a crucial role in leading the research, creation, and optimization of AI models that enhance real-time monitoring and security operations using IP camera feeds. Additionally, you will oversee a team of AI/ML engineers, collaborating with cross-functional teams to ensure the seamless integration of cutting-edge AI modules into our surveillance technology. Your contributions will directly impact the efficiency, accuracy, and scalability of our security solutions across various business environments. To excel in this role, you should possess at least 3 years of practical experience in Machine Learning and Deep Learning, with a focus on Computer Vision. Proficiency in Python, TensorFlow/PyTorch, OpenCV, and other relevant deep learning libraries is essential for creating robust models for facial recognition and object detection. Your expertise in optimizing AI pipelines for real-time performance and compatibility with edge devices will be critical for ensuring the effectiveness of our surveillance systems. Furthermore, your ability to lead, mentor, and inspire a team of AI/ML engineers, combined with strong communication and problem-solving skills, will be key assets in driving the success of our AI initiatives. Ideally, you should hold a Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Experience with optimization tools such as ONNX, TensorRT, and familiarity with integrating AI models with IP camera feeds using RTSP/ONVIF protocols will be advantageous. Keeping abreast of the latest advancements in deep learning and computer vision research is essential to stay ahead in this rapidly evolving field. Additionally, any background in surveillance systems, familiarity with cloud platforms like AWS, Azure, or GCP, and experience with deploying ML models using Docker, Git, and CI/CD pipelines would be beneficial. Joining Live Eye Surveillance offers a unique opportunity to lead in a dynamic environment and contribute to the development of innovative security technologies. You will have the chance to work on groundbreaking projects, collaborate with talented teams, and make a tangible impact on global security deployments. At Live Eye, we offer a competitive compensation package, a flexible hybrid work setup, and the potential for rapid career growth. If you are passionate about leveraging AI and ML to enhance security solutions, we invite you to send your resume to careers@myliveeye.com and explore the exciting opportunities available at www.myliveeye.com.,

As a Senior AI Engineer at Avathon, you will be part of a cutting-edge team revolutionizing industrial AI by developing groundbreaking solutions that shape the future. Your role will involve designing, training, and deploying computer vision models using frameworks like TensorFlow, PyTorch, or ONNX to harness the full potential of operational data. You will utilize your expertise in model optimization techniques such as quantization, pruning, distillation, and structured sparsity to enhance performance on edge devices and low-power hardware. Hands-on experience with state-of-the-art architectures like YOLO, Faster R-CNN, and Vision Transformers will be essential for optimizing models for deployment in industrial environments. Your strong understanding of image preprocessing, feature extraction, traditional computer vision techniques, and end-to-end model pipelines will enable you to create real-time virtual replicas of physical assets for predictive maintenance, performance simulation, and operational optimization. Proficiency in Python and C++ for developing AI solutions, along with experience in parallel processing and hardware-aware optimizations, will be key in driving AI-driven projects that have a meaningful impact across industries. Furthermore, your expertise in profiling and optimizing model inference speed, memory usage, and throughput for resource-constrained environments, as well as practical experience in deploying AI models on embedded systems and low-power hardware, will be crucial for anomaly detection, performance forecasting, and asset lifetime extension in industrial settings. Familiarity with MLOps practices, version control with Git, and collaborative workflows will ensure efficient management of AI workflows and seamless collaboration within cross-functional teams. Join Avathon in Bengaluru and thrive in a high-growth environment where agility, collaboration, and rapid professional growth are the norm. Make a difference by working on AI-driven projects that drive real change across industries and improve lives. If you are a forward-thinking AI Engineer with a passion for innovation and a drive to create scalable solutions in industrial AI, we invite you to be a part of our team and contribute to the revolutionizing of industrial AI.,

Job Description Designation and SRF Name- Technical CX Consultant role Role- Permanent/ Full time Panel and Hiring Manager- Sanjay Pathak Experience- 8-12 years relevant experience Location- Noida/ Gurgaon/ Pune/ Bangalore Shift- 12PM to 10PM (10 Hours Shift. Also depends on the project/work dependencies) Working Days- 5 days Work Mode- Hybrid Job Description: Highly skilled CX Consulting with deep expertise in CCaasS, Integrations, IVR, Natural Language Processing (NLP), Language Models, and scalable cloud-based solution deployment. Skills Technical Expertise: Having a deep understanding of Conversational AI, Smart Agent Assist, CCaaS and their technical capabilities. Stay current with industry trends, emerging technologies, and competitor offerings. Customer Engagement: Engage with prospective clients to understand their technical requirements and business challenges. Conduct needs assessments and provide tailored technical solutions. Solution Demonstrations: Deliver compelling product demonstrations that showcase the features and benefits of our solutions. Customize demonstrations to align with the specific needs and use cases of potential customers. Strong NLP and Language Model fundamentals (e.g., transformer architectures, embeddings, tokenization, fine-tuning). Expert in Python, with clean, modular, and scalable coding practices. Experience developing and deploying solutions on Azure, AWS, or Google Cloud Platform. Familiarity with Vertex AI, including Model Registry, Pipelines, and RAG integrations (preferred). Experience with PyTorch, including model training, evaluation, and serving. Knowledge of GPU-based inferencing (e.g., ONNX, Torch Script, Triton Inference Server). Understanding of ML lifecycle management, including MLOps best practices. Experience with containerization (Docker) and orchestration tools (e.g., Kubernetes). Exposure to REST APIs, gRPC, and real-time data pipelines is a plus. Degree in Computer Science, Mathematics, Computational Linguistics, AI, ML or similar field. PhD is a plus. Responsibilities Consulting and design end-to-end AI solutions for CX. Consulting engagement of scalable AI services on cloud infrastructure (Azure/AWS/GCP). Collaborate with engineering, product, and data teams to define AI-driven features and solutions. Optimize model performance, scalability, and cost across CPU and GPU environments. Ensure reliable model serving with a focus on low-latency, high-throughput inferencing. Keep abreast of the latest advancements in NLP, LLMs, and AI infrastructure.