30 Tts Jobs

About us: University Living is a global student housing marketplace that helps international students find the best place to stay near university campuses around the world. Currently, our platform offers 2Mn beds in 65K + properties across 512+ international education hubs in the UK, Ireland, USA, Canada, Europe, Australia, New Zealand, Singapore & UAE. Students can consult a 24/7 support team of accommodation experts to discover, compare and book value-for-money accommodation based on their budget, desired location, and other personal preferences. As the AI & ML Lead, you will spearhead the development of our conversational AI system. You will define the technical strategy, lead model design and implementation, and work on large language models (LLMs) to fine-tune, deploy, and optimize human-like conversational agents. This role involves hands-on development, collaboration with cross-functional teams, and leveraging tools such as OpenAI and cloud systems like AWS for scalable AI deployments. Key Responsibilities Lead the design and development of LLM-based AI models Integrate voice APIs and platforms (e.g., Twilio, Exotel, ElevenLabs) for seamless call handling and speech workflows. Implement Retrieval-Augmented Generation (RAG) to improve response relevance and accuracy Deploy and manage AI models on cloud infrastructure (AWS/GCP/Azure) Establish MLOps practices, model monitoring, and version control. Stay updated on GenAI trends, LLM research, and speech tech advancements Required Qualifications 6+ years of hands-on AI/ML experience, with a strong focus on NLP, LLMs, and conversational AI. Proven expertise in fine-tuning and deploying large language models (e.g., GPT, OpenAI models, BERT). Strong understanding of ASR, speech recognition, & text-to-speech (TTS) Proficiency in Python and ML frameworks like TensorFlow or PyTorch. Experience with cloud-based AI deployments (AWS/GCP/Azure). Knowledge of RAG techniques, vector databases, and real-time inference pipelines. Strong problem-solving skills and experience leading AI/ML teams or projects.

As a Senior Machine Learning Engineer at Sarvam AI, you will be responsible for overseeing the orchestration of the end-to-end AI in Media pipeline. Your role will involve combining cutting-edge models, agile workflows, and media-grade quality automation to automate movie and OTT content creation & localization across diverse languages. **Key Responsibilities:** - Own the design and evolution of Sarvam's Media AI pipeline by combining agent-based orchestration, scalable ML integration, and rapid experimentation. - Build and lead the development of production-grade ML pipelines, ensuring robustness, automation, and adaptability to media workflows. - Prototype and deploy agentic systems to automate task sequencing, error handling, and quality evaluation. - Drive continual improvements through prompt engineering, fine-tuning, and metric-driven performance monitoring. - Define pipeline-wide standards for quality, testing, fallback mechanisms, and edge-case handling. - Collaborate with model researchers, infra/platform teams, and product stakeholders to ensure end-to-end reliability. - Provide technical leadership and mentorship across the orchestration and pipeline engineering team. **Technical Expertise:** - 4-7 years of experience in ML/AI engineering. - Hands-on expertise in Generative AI, Python, PyTorch/TensorFlow, MCP, and agent orchestration frameworks. - Proven track record of deploying ML pipelines in production at scale with monitoring and alerts. - Familiarity with ASR/TTS/translation models. - Experience with cloud platforms, containerized deployments, and scalable inference is a plus. **Leadership & Soft Skills:** - Entrepreneurial spirit with hands-on experience in 0-to-1 startup phases. - Proven ability to lead and mentor ML teams. - Strong systems thinking and cross-functional coordination. - Passion for keeping pace with the latest in Gen AI, LLMs, and ML engineering. - Experience working with media/OTT post-production pipelines is a plus.,

You will be joining Credgenics, India's leading SaaS-based debt collection platform, where innovation and agility are highly valued. By leveraging AI, ML, and advanced analytics, Credgenics helps financial institutions improve recovery rates, optimize collection spends, and enhance operational efficiency. In your role as a Data Scientist specializing in Voice AI, you will focus on developing and deploying GenAI-powered Speech-to-Text (STT) and Text-to-Speech (TTS) models to drive voicebot automation within the collections platform. Collaboration with cross-functional teams is essential to implement voice solutions that enhance customer interactions, reduce manual intervention, and scale conversational engagement. **Key Responsibilities:** - Implement and fine-tune STT and TTS models for real-time customer conversations. - Work with engineering teams to integrate speech models into collection workflows and customer engagement platforms. - Develop ML pipelines for speech models to ensure low latency and high accuracy. - Continuously monitor and optimize model performance in terms of accuracy, latency, and naturalness of voice. - Enable automation of customer communication through seamless voicebot deployment. - Collaborate with product managers, engineers, and operations teams to design AI solutions for debt lifecycle management. **Qualifications Required:** - 3-5 years of experience in Voice AI, Voicebot development, or Conversational AI, preferably in a product/startup environment. - Strong experience with speech recognition (STT/ASR) and speech synthesis (TTS) deployment. - Hands-on experience with ML/DL frameworks such as PyTorch, TensorFlow, and Keras. - Proficiency in Python programming and experience in API-based model integration. - Practical experience deploying models in production environments, preferably on cloud platforms like AWS, GCP, or Azure. - Experience in latency optimization, error reduction, and real-time deployment of speech models. - Ability to collaborate effectively across teams to deliver impactful, applied AI solutions.,

As a Gen AI Researcher for Audio at Brahma, your role will involve researching and developing state-of-the-art voice synthesis models, building and fine-tuning models using frameworks like PyTorch and HuggingFace, designing training pipelines and datasets for scalable voice model training, exploring techniques for emotional expressiveness, multilingual synthesis, and speaker adaptation, working closely with product and creative teams to ensure models meet quality and production constraints, and staying updated on academic and industrial trends in speech synthesis and related fields. Key Responsibilities: - Research and develop state-of-the-art voice synthesis models such as TTS, voice cloning, and speech-to-speech. - Build and fine-tune models using frameworks like PyTorch and HuggingFace. - Design training pipelines and datasets for scalable voice model training. - Explore techniques for emotional expressiveness, multilingual synthesis, and speaker adaptation. - Collaborate with cross-functional teams to integrate your work into production-ready pipelines. - Stay on top of academic and industrial trends in speech synthesis and related fields. Qualifications Required: - Strong background in machine learning and deep learning, with a focus on speech/audio. - Hands-on experience with TTS, voice cloning, or related voice synthesis tasks. - Proficiency with Python and PyTorch; experience with libraries like torchaudio, ESPnet, or similar. - Experience training models at scale and working with large audio datasets. - Familiarity with vocoders and transformer-based architectures. - Strong problem-solving skills and the ability to work autonomously in a remote-first environment. Brahma is a pioneering enterprise AI company that develops Astras, AI-native products designed to help enterprises and creators innovate at scale. Part of the DNEG Group, Brahma brings together Hollywood's leading creative technologists, innovators in AI and Generative AI, and thought leaders in the ethical creation of AI content.,

As a Senior Machine Learning Engineer at Sarvam AI, you will be responsible for overseeing the orchestration of the end-to-end AI in Media pipeline. Your role will involve combining cutting-edge models, agile workflows, and media-grade quality automation to automate movie and OTT content creation & localization across India's diverse languages. **Key Responsibilities:** - Own the design and evolution of Sarvam's Media AI pipeline by combining agent-based orchestration, scalable ML integration, and rapid experimentation. - Build and lead the development of production-grade ML pipelines, ensuring robustness, automation, and adaptability to media workflows. - Prototype and deploy agentic systems to automate task sequencing, error handling, and quality evaluation. - Drive continual improvements through prompt engineering, RAG, fine-tuning, and metric-driven performance monitoring. - Define pipeline-wide standards for quality, testing, fallback mechanisms, and edge-case handling. - Collaborate with model researchers, infra/platform teams, and product stakeholders to ensure end-to-end reliability. - Provide technical leadership and mentorship across the orchestration and pipeline engineering team. **Technical Expertise:** - 4-7 years of experience in ML/AI engineering. - Hands-on expertise in Generative AI, including LLMs, prompt design, and building complex agentic systems. - Strong proficiency in Python, PyTorch/TensorFlow, MCP, and agent orchestration frameworks (LangChain, LangGraph, Langfuse, or custom DAGs). - Proven track record of deploying ML pipelines in production at scale with monitoring and alerts, comfortable with tensor debugging. - Familiarity with ASR/TTS/translation models such as Whisper, wav2vec, F5, Coqui TTS, FastSpeech, Bark, Wav2Lip, etc. - Experience with cloud platforms (GCP/AWS), containerized deployments (Docker/Kubernetes), and scalable inference is a plus. **Leadership & Soft Skills:** - Entrepreneurial spirit with hands-on experience in 0-to-1 startup phases. - Proven ability to lead and mentor ML teams, balancing research rigor with production needs. - Strong systems thinking and cross-functional coordination. - Passion for keeping pace with the latest in Gen AI, LLMs, and ML engineering. - Experience working with media/OTT post-production pipelines is a plus.,

As an instructional designer, you will play a vital role in developing learner and focused content to create engaging and effective learning experiences. Work closely with subject matter experts, trainers, and learners to design, develop, and maintain interactive eLearning courses. Develop supporting materials and media, including audio, video, simulations, role plays, and gamification, ensuring alignment with the ADDIE learning model. Proactively enhance existing practices and processes, continuously exploring innovative ideas and methods to expand the eLearning team s impact. Stay updated with evolving technologies and industry trends by continuously learning and acquiring new skills. If you have a passion for education, technology, and designing meaningful learning experiences, this role offers an exciting opportunity to make a lasting impact Have you got what it takes At least 5 years of experience in eLearning instructional design. Proficiency in eLearning tools such as Articulate Storyline 360 and Camtasia. Experience with Text-to-Speech (TTS) technologies like Amazon Polly and Azure. Familiarity with programming languages such as HTML5, CSS, and JavaScript. Hands-on experience with SCORM packaging. Strong ability to work independently and take ownership of responsibilities. Proficiency in Microsoft Office Suite. Excellent communication and collaboration skills. Strong attention to detail to ensure accuracy and quality in eLearning content. Ability to manage multiple projects, prioritize tasks effectively, and meet deadlines. Previous training experience and knowledge of financial markets are advantageous.

We re looking for Full Stack Engineer with a strong backend python & Java focus to build and scale the infrastructure our AI-powered voice assistant platform. You'll collaborate with a small team to design robust systems fine-tune AI models

Job Title: Senior AI Engineer Experience: 6+ years Location: On-site (Goa And Bengaluru) Job Type: Full-time About the Role We are hiring a Senior AI Engineer to join our growing team. You'll be building AI-native and GenAI-powered products that enable lifelike and seamless conversations. If you're passionate about AI, system design, and building production-grade products, this role is for you. What You'll Do Build and deploy AI-first applications using GenAI, LLMs, and voice tech. Collaborate on architecture and system design for scalable AI systems. Work with NLP, STT, TTS, and other speech/voice-based AI tools and APIs. Develop and optimize real-time conversational flows and intelligent agents. Build cloud-native applications (preferably on AWS). Partner with cross-functional teams to bring AI products to life. What We're Looking For 6-8 years of hands-on experience in software/product development with exposure to AI, Data Platforms, GenAI, and LLMs. Proven track record of building AI-first or AI-native products and systems. Experience with GenAI/LLMs and Speech-to-Text / Text-to-Speech APIs is a big plus. Familiarity with cloud-native development (preferably AWS). Strong coding skills, system design experience is a plus. Must be based in or open to relocating to Goa or Bengaluru . Why Join Us Be part of a fast-paced AI product startup at the frontier of tech innovation. Work on high-impact projects with top-tier clients. Rapid learning and ownership opportunities. Competitive compensation + equity .

As an Android Voice Recognition expert with 6-9 years of experience, you will be responsible for developing Speech/VR components on the Android platform. Your proficiency in Java/Kotlin programming languages along with hands-on experience in developing Speech Applications for infotainment will be crucial. This includes integrating Speech engines for Speech recognition and TTS (text to speech). Additionally, you should possess knowledge of Android HAL and core Android frameworks. You will be expected to utilize developmental tools such as Enterprise Architect, Audacity, Polarion, Jira, or similar applications. Your ability to analyze issue logs, debug, and identify root causes will be essential. Adherence to Aptiv PDP 2.0 process and ASPICE L2 standards is required. Conducting Unit Tests, Integration Tests, and Functional Tests will be part of your responsibility. Active participation in all Aptiv Agile Auto SCRUM ceremonies and activities is a must. You will need to update the task status to Aptiv and communicate any impediments promptly. It would be beneficial to have experience in speech grammars and hierarchical state machines. Furthermore, you will be involved in Code Integration and customer interaction/communication. Your role will encompass a comprehensive approach to Android Voice Recognition, ensuring the seamless development and integration of speech-related features.,

Role & responsibilities As an Embedded C Engineer, you will develop the lightweight voice engine for Lavas feature phones, ensuring multilingual support, optimized memory usage, and smooth integration with hardware constraints. Key Responsibilities • Design and develop the proprietary TTS/voice playback engine in C/C++. • Implement audio compression algorithms (e.g., IMA ADPCM) and phoneme mapping. • Build language packs and integrate multi-language prompts. • Work with RTOS Engineer to integrate engine into Lava firmware. • Optimize for low memory, low latency, and power efficiency. • Prepare developer documentation and support handover to Lava engineering teams.

As an RTC / Voice Streaming Engineer, you will be responsible for building and optimizing real-time voice infrastructure with a focus on low-latency audio streaming and CPaaS integrations. Your expertise in Node.js and WebSocket audio streaming will be essential for integrating Exotel bi-directional WebSocket audio bridges with full observability, ensuring ultra-low-latency performance. You should have at least 3-7 years of experience in low-latency media engineering and strong proficiency in Node.js for real-time audio pipelines. Hands-on experience with WebSocket audio streaming (PCM16/16k) and knowledge of handling back-pressure, chunking, and silence detection are required. An understanding of RTP/SIP basics for telephony integration and proven experience in implementing barge-in for TTS/ASR pipelines will be beneficial for this role. Experience in production-grade integrations with CPaaS providers like Exotel or Twilio Media Streams is necessary. Familiarity with Exotel Stream/Voicebot applets and the ability to wire uni-directional streams for agent assist, call recording, and monitoring are also key requirements. You will be expected to focus on performance and observability by exposing metrics such as one-way delay, jitter, packet loss, and MOS. Keeping the median one-way latency within the range of 150-300 ms following ITU-T G.114 standards and building fallbacks for network degradation are crucial aspects of the role. Additionally, deep knowledge of WebRTC internals, experience with AEC (Acoustic Echo Cancellation), NS (Noise Suppression), PLC (Packet Loss Concealment), and audio DSP knowledge for optimizing quality in low-bandwidth conditions are considered nice-to-have skills for this position. The engagement is for a duration of 60 days with the possibility of early completion. The successful completion of the project within this timeframe involves delivering a production-ready WebSocket bridge with full observability and fallback routes meeting G.114/P.800.1 performance benchmarks. This is a contract role based in Bangalore, with the option for hybrid work. Freelancers are welcome to apply as long as they can meet deadlines and SLOs. If you have the required skills and experience in real-time communications and voice streaming, we look forward to receiving your application for this exciting opportunity.,

About Zudu AI: Zudu AI is revolutionizing voice automation by replacing traditional call centers with advanced, AI-powered voice agents. The company is rapidly expanding and has already started integrating with major enterprise platforms and CPaaS providers. With significant partnerships in progress, Zudu AI is in search of a forward-thinking Chief Technology Officer (CTO) to spearhead the next phase of growth. What You'll Do: - Drive Tech Vision: Define and execute Zudu's technology roadmap in alignment with enterprise partnerships and platform integrations. - Architect at Scale: Design and oversee scalable, secure, and low-latency AI infrastructure to support high-volume voice interactions. - Lead Engineering: Manage, mentor, and develop high-performance backend, frontend, and AI teams. - Enterprise Integration: Lead technical discussions and implementation strategies for integrating Zudu AI with large-scale CCaaS, CPaaS, and telco systems. - Collaborate with Founders: Work closely with the CEO and leadership team to translate business objectives into scalable product and tech strategies. - Innovate Constantly: Stay abreast of advancements in AI voice technology, including STT, TTS, NLP, and knowledge base integrations (RAG, vector DBs, etc.). Who You Are: - 10+ Years Experience in software engineering, with a minimum of 3+ years in a senior leadership role (VP Eng/CTO/Tech Co-Founder). - Strong Python/AI/Cloud Skills, preferably with expertise in AI-based SaaS, voice tech, or real-time systems. - Enterprise-Ready: Demonstrated success in working with or integrating into large enterprise ecosystems and managing product scaling across regions. - Product-First Thinker: Proficient in building and scaling B2B SaaS products from inception to enterprise adoption. - Startup DNA: Thrives in fast-paced environments with a proactive mindset and hands-on leadership approach. - Excellent Communicator: Capable of engaging with investors, enterprise clients, and technical teams effectively. Why Join Us - Contribute to building a category-defining AI product that is already gaining traction in global enterprise markets. - Lead a growing tech team and contribute to shaping product architecture from the ground up. - Participate in high-level strategic discussions and global partnerships. - Enjoy the flexibility and high-impact leadership opportunities with ESOP upside. Interested Let's connect. Zudu AI is moving swiftly and is in search of an individual who can match their pace.,

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.,

New JD- 9+ years of experience in ML, AI, or deep learning, with hands-on experience in NLP, speech recognition(ASR) and text to speech (TTS) Knowledge of multimodal learning and techniques for fusing audio and text information. Solid understanding of audio processing concepts, including audio feature extraction, signal processing, and acoustic modeling. Experience with fine-tuning transformer models and developing training pipelines. Strong programming skills in Python, with experience in deep learning frameworks such as PyTorch or TensorFlow Familiarity with Docker, Kubernetes, Airflow, and serverless computing. Ability to build automated model training, evaluation, and monitoring pipelines.

As a talented Full Stack Developer with expertise in Generative AI and Natural Language Processing, you will be a key member of our team, contributing to the design, development, and scaling of cutting-edge LLM and Generative AI applications to enhance user experiences. Your responsibilities will include developing backend logic and intelligent workflows using pre-trained AI models such as large language models (LLMs) and natural language understanding (NLU) engines. You will integrate and operationalise NLP and Generative AI models in production environments, including speech processing pipelines like automatic speech recognition (ASR) and text-to-speech (TTS) technologies. Applying techniques such as LLM fine-tuning, prompt engineering, and Retrieval-Augmented Generation (RAG) will be crucial for enhancing AI system performance. Moreover, you will design and deploy scalable full-stack solutions supporting AI-driven applications, working with various data sources to enable contextual AI retrieval and responses. Utilising cloud platforms like AWS/Azure effectively for hosting, managing, and scaling AI-enabled services will also be part of your role. If you are passionate about combining full-stack development with AI and LLM technologies to create innovative text and voice applications, we look forward to hearing from you. Qualifications: - 3+ years of hands-on experience in full-stack application development with a strong understanding of frontend and backend technologies. - 12 years of proven experience in designing and implementing AI-driven conversational systems. - Deep knowledge of integrating Speech-to-Text (STT) and Natural Language Processing (NLP) components into production-ready systems. Nice-to-Have Skills: - Exposure to MLOps practices, including model deployment, monitoring, lifecycle management, and performance optimization in production environments. What You'll Get: - Opportunity to work on one of the most advanced AI systems. - A high-performing, fast-paced startup culture with a deep tech focus.,

As a Senior AI Engineer specialized in Voice AI and Autonomous Agents at Spyne, you will be responsible for owning and building Spynes in-house voice bot stack. This pivotal role involves leveraging your expertise in LLMs, ASR/TTS, and voice UX to develop immersive, human-like conversations between auto dealerships and their customers. Located in Gurugram, you will work from the office five days a week to drive the development of cutting-edge AI solutions in the automotive retail sector. Your primary responsibilities will include: - Voice AI Stack Ownership: Developing and managing the complete voice bot pipeline encompassing ASR, NLU, dialog state management, tool calling, and TTS to deliver a seamless conversation experience. - LLM Orchestration & Tooling: Designing systems utilizing MCP to facilitate structured context exchange between real-time ASR, memory, APIs, and the LLM. - RAG Integration: Implementing retrieval-augmented generation to support responses based on dealership knowledge bases, inventory data, recall lookups, and FAQs. - Vector Store & Memory: Creating scalable vector-based search functionalities for dynamic FAQ handling, call recall, and user-specific memory embedding. - Latency Optimization: Engineering low-latency, streaming ASR + TTS pipelines and optimizing turn-taking models for natural conversations. - Model Tuning & Hallucination Control: Customizing tone, reducing hallucinations, and aligning responses with business objectives via fine-tuning, LoRA, or instruction tuning. - Instrumentation & QA Looping: Establishing robust observability, conducting real-time call QA processes, and analyzing interruptions, hallucinations, and fallbacks. - Cross-functional Collaboration: Collaborating closely with product, infra, and leadership teams to scale the voice bot solution to numerous US dealerships effectively. To excel in this role, you should possess: - Architectural Thinking: Ability to comprehend the integration of ASR, LLMs, memory, and tools and design modular, observable, and resilient systems. - LLM Tooling Mastery: Proficiency in implementing tool calling, retrieval pipelines, function calls, or prompt chaining across various workflows. - Fluency in Vector Search & RAG: Expertise in chunking, embedding, indexing, and retrieval processes while avoiding prompt bloat and token overflow. - Latency-First Mindset: Capability to identify and rectify token delays, optimize API round-trip time, and ensure human-like call interactions. - Grounding > Hallucination: Skill in tracing hallucinations to weak prompts, lack of guardrails, or tool access deficiencies and addressing them effectively. - Prototyping Skills: Comfort with building from scratch and rapid iteration using open-source or hosted tools as required. Requirements for this role include: - 5+ years of experience in AI/ML or voice/NLP systems with real-time exposure. - Profound knowledge of LLM orchestration, RAG, vector search, and prompt engineering. - Experience with MCP-style architectures and structured context pipelines between LLMs and APIs/tools. - Familiarity with integrating ASR (Whisper/Deepgram), TTS (ElevenLabs/Coqui), and OpenAI/GPT-style models. - Strong understanding of latency optimization, streaming inference, and real-time audio pipelines. - Proficiency in Python, FastAPI, vector DBs (Pinecone, Weaviate, FAISS), and cloud infrastructures (AWS/GCP). - Solid debugging, logging, and QA capabilities for hallucination, grounding, and UX analysis. Join Spyne for a real-world AI impact, a superior team balancing speed with technical depth, high autonomy, and visibility from day one, accelerated career growth, MacBook along with essential tools, and a flat structure focused on meaningful work without unnecessary bureaucracy.,

You'll need to have: Bachelors degree in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field. Six or more years of professional experience in data science, machine learning, or a related role. Expert-level hands-on proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn, Matplotlib). Deep theoretical and practical knowledge of a wide range of machine learning algorithms (e.g., classification, regression, clustering, ensemble methods) and deep learning. Proven hands-on experience developing and deploying Generative AI solutions, with a strong understanding of LLMs, transformers, and vector databases. Experience with Multi-Modal RAG systems (handling text, images, and other data types). Strong proficiency in SQL for complex data extraction, manipulation, and analysis on large-scale datasets. Hands-on experience with cloud computing platforms, specifically Google Cloud Platform (GCP) and its AI/ML services. Even better if you have: A Master’s or Ph.D. in a relevant quantitative field. Practical experience in developing AI agents using frameworks like LangChain, Agent Development Kit (ADK), or LlamaIndex. Experience in designing and implementing robust LLM evaluation metrics and frameworks (e.g., RAGAS, ARES, TruLens) Experience with voice and speech technologies, such as Speech-to-Text (STT), Text-to-Speech (TTS), and building conversational AI or voicebot systems. Experience with MLOps/LLMOps principles and tools (e.g., Docker, Kubernetes, CI/CD pipelines for models, MLflow). GCP Professional Machine Learning Engineer certification.

What will you do Voice AI Stack Ownership: Build and own the end-to-end voice bot pipeline ASR, NLU, dialog state management, tool calling, and TTS to create a natural, human-like conversation experience. LLM Orchestration & Tooling: Architect systems using MCP (Model Context Protocol) to mediate structured context between real-time ASR, memory, APIs, and the LLM. RAG Integration: Implement retrieval-augmented generation to ground responses using dealership knowledge bases, inventory data, recall lookups, and FAQs. Vector Store & Memory: Design scalable vector-based search for dynamic FAQ handling, call recall, and user-specific memory embedding. Latency Optimization: Engineer low-latency, streaming ASR + TTS pipelines and fine-tune turn-taking models for natural conversation. Model Tuning & Hallucination Control: Use fine-tuning, LoRA, or instruction tuning to customize tone, reduce hallucinations, and align responses to business goals. Instrumentation & QA Looping: Build robust observability, run real-time call QA pipelines, and analyze interruptions, hallucinations, and fallbacks. Cross-functional Collaboration: Work closely with product, infra, and leadership to scale this bot to thousands of US dealerships. What will make you successful in this role Architect-level thinking: You understand how ASR, LLMs, memory, and tools fit together and can design modular, observable, and resilient systems. LLM Tooling Mastery: You've implemented tool calling, retrieval pipelines, function calls, or prompt chaining across multiple workflows. Fluency in Vector Search & RAG: You know how to chunk, embed, index, and retrieve and how to avoid prompt bloat and token overflow. Latency-First Mindset: You debug token delays, know the cost of each API hop, and can optimize round-trip time to keep calls human-like. Grounding > Hallucination: You know how to trace hallucinations back to weak prompts, missing guardrails, or lack of tool access and fix them. Prototyper at heart: You're not scared of building from scratch and iterating fast, using open-source or hosted tools as needed. What you must have 5+ years in AI/ML or voice/NLP systems with real-time experience Deep knowledge of LLM orchestration, RAG, vector search, and prompt engineering Experience with MCP-style architectures or structured context pipelines between LLMs and APIs/tools Experience integrating ASR (Whisper/Deepgram), TTS (ElevenLabs/Coqui), and OpenAI/GPT-style models Solid understanding of latency optimization, streaming inference, and real-time audio pipelines Hands-on with Python, FastAPI, vector DBs (Pinecone, Weaviate, FAISS), and cloud infra (AWS/GCP) Strong debugging, logging, and QA instincts for hallucination, grounding, and UX behavior

As a Senior AI Engineer - Voice AI / Autonomous Agents at Spyne, you will have the opportunity to own and build Spynes in-house voice bot stack. In this high-impact individual contributor role, you will be at the intersection of LLMs, ASR/TTS, and voice UX, focusing on creating deeply human, latency-optimized conversations between auto dealerships and their customers. Your main responsibilities will include: Voice AI Stack Ownership: Building and owning the end-to-end voice bot pipeline, including ASR, NLU, dialog state management, tool calling, and TTS to deliver a natural, human-like conversation experience. LLM Orchestration & Tooling: Architecting systems using MCP (Model Context Protocol) to mediate structured context between real-time ASR, memory, APIs, and the LLM. RAG Integration: Implementing retrieval-augmented generation to ground responses using dealership knowledge bases, inventory data, recall lookups, and FAQs. Vector Store & Memory: Designing scalable vector-based search for dynamic FAQ handling, call recall, and user-specific memory embedding. Latency Optimization: Engineering low-latency, streaming ASR + TTS pipelines and fine-tuning turn-taking models for natural conversation. Model Tuning & Hallucination Control: Using fine-tuning, LoRA, or instruction tuning to customize tone, reduce hallucinations, and align responses to business goals. Instrumentation & QA Looping: Building robust observability, running real-time call QA pipelines, and analyzing interruptions, hallucinations, and fallbacks. Cross-functional Collaboration: Working closely with product, infra, and leadership to scale this bot to thousands of US dealerships. To be successful in this role, you should possess: Architect-level thinking: Understanding how ASR, LLMs, memory, and tools fit together and having the ability to design modular, observable, and resilient systems. LLM Tooling Mastery: Implementing tool calling, retrieval pipelines, function calls, or prompt chaining across multiple workflows. Fluency in Vector Search & RAG: Knowing how to chunk, embed, index, and retrieve, while avoiding prompt bloat and token overflow. Latency-First Mindset: Debugging token delays, understanding the cost of each API hop, and optimizing round-trip time to maintain human-like interactions. Grounding > Hallucination: Tracing hallucinations back to weak prompts, missing guardrails, or lack of tool access and effectively addressing them. Prototyper at heart: Being unafraid of building from scratch and iterating quickly, utilizing open-source or hosted tools as necessary. The ideal candidate will have: 5+ years of experience in AI/ML or voice/NLP systems with real-time experience. Deep knowledge of LLM orchestration, RAG, vector search, and prompt engineering. Experience with MCP-style architectures or structured context pipelines between LLMs and APIs/tools. Experience integrating ASR (Whisper/Deepgram), TTS (ElevenLabs/Coqui), and OpenAI/GPT-style models. Solid understanding of latency optimization, streaming inference, and real-time audio pipelines. Hands-on experience with Python, FastAPI, vector DBs (Pinecone, Weaviate, FAISS), and cloud infra (AWS/GCP). Strong debugging, logging, and QA instincts for hallucination, grounding, and UX behavior. Working at Spyne offers real-world AI impact at scale, a high-performing team that balances speed with technical depth, high autonomy and visibility from day one, rapid career acceleration, access to MacBook and all necessary tools and compute, a flat structure with real work focus, and no BS. Join us in redefining how cars are marketed and sold with cutting-edge Generative AI.,

Job Responsibilities We are seeking a highly strategic and execution-driven person to join the CEO's office as a (Program and Strategy Manager) and drive the adoption of Agentic AI (autonomous, goal-driven AI systems) across all functionsProduct, Tech, Marketing, Data, Sales, Customer Success, Delivery, Onboarding, HR, Finance, PR, Branding and more. You will act as a bridge across cross-functional teams to ensure alignment and drive the strategic direction of our AI-powered product portfolio. What will you do Cross-Functional Agentic AI Transformation Define and execute the company-wide high-impact agentic AI automation across functions.(e.g., AI-driven sales bots, automated customer onboarding, HR talent matching, finance forecasting). Develop metrics and KPIs to track AI-driven efficiency gains. Lead no-code AI tooling initiatives (e.g., GPT-based automation, AI agents, RPA, AutoML) to empower non-technical teams. Partner with Engineering & Data teams to integrate AI into existing workflows. Program Management and Strategy Develop, implement, and monitor key strategic initiatives that align with the company's overall business objectives. Define, track, and own key business KPIs, ensuring execution of high-impact priorities. Design and lead cross-functional projects to drive business outcomes, such as revenue growth, customer acquisition, and operational efficiency Prepare executive reports, investor decks, and MBR presentations. Provide strategic assistance and support to the senior leadership team Team & Stakeholder Management Act as the bridge between the CEO's Office and department heads to drive AI adoption. Conduct workshops to upskill teams on AI tools and best practices. Manage vendor partnerships (OpenAI, Microsoft, Google AI, etc.) for AI tooling. What you must have 3+ years of experience, preferably in Product, Program Management, or Strategy roles. Expertise in analytics, excel, SQL, and BI tools (Tableau, Looker, Power BI, etc.) Basic familiarity with LLM APIs (e.g., OpenAI, Anthropic, Hugging Face) Technical background with ability to collaborate effectively with ML/AI engineering teams. Exceptional communication skills to explain technical AI concepts to non-technical stakeholders. Excellence in strategic thinking, problem-solving, and decision-making. Analytical mindset with the ability to define and measure success metrics. Ability to thrive in a fast-paced, ambiguous environment.

Build and own the full voice bot pipeline including ASR, NLU, dialog management, tool calling, and TTS. Architect systems using MCP to connect ASR, memory, APIs, and LLMs in real-time. Implement RAG to ground responses using data from knowledge bases, inventory, and FAQs. Design scalable vector search systems for memory embedding and FAQ handling. Engineer low-latency ASR and TTS pipelines, optimizing for natural turn-taking. Apply fine-tuning, LoRA, and instruction tuning to reduce hallucinations and align model tone. Build observability systems and QA pipelines to monitor calls and analyze model behavior. Collaborate with cross-functional teams to scale the voice bot to thousands of users. Design modular, observable, and resilient AI systems. Implement retrieval pipelines, function calls, and prompt chaining across workflows. Expertly chunk, embed, and retrieve documents in RAG systems. Debug latency issues and optimize for low round-trip time. Trace hallucinations to root causes and fix via guardrails or tool access. Build prototypes using open-source or hosted tools with speed and flexibility. 5+ years in AI/ML or voice/NLP with real-time experience. Deep knowledge of LLM orchestration, vector search, and prompt engineering. Experience with ASR (Whisper, Deepgram), TTS (ElevenLabs, Coqui), and OpenAI models. Skilled in latency optimization and real-time audio pipelines. Hands-on with Python, FastAPI, vector DBs, and cloud platforms.

Job Summary Shaip.ai is seeking experienced linguistics professionals who bring a blend of deep linguistic expertise and team management capabilities. As a Manager, you will play a pivotal role in supporting our LLM, ASR, and TTS models, and managing linguistic teams working across multilingual AI data projects. You will work closely with machine learning engineers, data sourcing teams, and project stakeholders to ensure linguistic quality and on-time delivery of high-quality annotated language datasets. Key Responsibilities Linguistic Expertise & Support • Collaborate with ML engineers to understand project goals, data specifications, and linguistic requirements. • Embed linguistic rules into model architecture and data processing workflows. • Design, document, and review annotation guidelines for linguistic, speech, and conversational AI projects. • Conduct linguistic evaluations and provide detailed feedback to improve model performance and output quality. Team & Project Management • Partner with the data sourcing and project teams to define and align on delivery timelines and milestones. • Set up efficient data workflowsfrom raw data intake to final annotation delivery. • Source, on board, and manage team of linguists across multiple languages and regions. • Allocate tasks, track productivity, maintain annotation quality, and ensure delivery SLAs are met. Required Qualifications • Masters or PhD in Linguistics, Computational Linguistics, or a closely related field. • Minimum 2 years of hands-on experience working in NLP, GenAI, or speech data projects. • Proven experience in designing annotation workflows and linguistic guideline creation. • At least 2 years of experience managing teams or coordinating projects. • Experience with low-resource or regional languages is highly desirable. Technical & Core Competencies • Linguistic Analysis: Strong foundation in syntax, morphology, semantics, and phonetics. • Language Proficiency: Fluency in two or more Indian languages (spoken, written, and grammar). • Project Setup: Familiarity with NLP data workflows, annotation tools, and delivery pipelines. • People Management: Experience managing freelancers, task assignments, and quality assurance. • Collaboration: Ability to act as a bridge between linguistic and technical teams. • Problem Solving : Analytical mindset for handling language model and annotation challenges

SoftwareEngineer-L2Chatbot Engg Role! 3+yrs exp, Python (must), Java/Golang (good), Django, AWS, Postgres, NLP, LLMs/GenAI, ML, NLU, NLG, TTS, voice bots. Full-stack exp a plus. C2H with TE Infotech (Exotel). Loc: BLR. Apply: ssankala@toppersedge.com

Job Opening: Conversational AI Lead Location : Kalyani Nagar, Pune (Work from Office) Company : AM Infoweb Pvt. Ltd. Notice Period: Immediate Joiners or Maximum 1 Month About Us AM Infoweb is a fast-growing digital transformation partner delivering next-gen tech solutions. We are looking for a Conversational AI Lead to join our team and drive the development of advanced voice bot solutions from scratch using cutting-edge technologies like LLM and Machine Learning. Role: Conversational AI Lead Key Responsibilities: Lead the design and development of voice bots from scratch. Work extensively with Conversational AI technologies and tools. Utilize LLMs (Large Language Models) and Machine Learning for intelligent dialogue flow creation. Implement Text-to-Speech (TTS) and Speech-to-Text (STT) systems. Collaborate with cross-functional teams to integrate AI solutions with business needs. Write clean and scalable code, primarily in Python. Ensure performance, scalability, and security of voice applications. Requirements: Minimum 2 years of hands-on experience in Conversational AI. Proven experience in developing and deploying voice bots. Strong knowledge of LLM and ML frameworks. Expertise in TTS and STT systems and their integrations. Proficient in Python programming. Bachelors degree in Computer Science, Engineering, or a related field is mandatory. Strong problem-solving and communication skills. Must be open to work from office (Kalyani Nagar). Comfortable with self-travel (No cab facility provided). Why Join Us? Opportunity to lead innovation in Conversational AI. Work with a talented and tech-savvy team. Be part of impactful projects shaping the future of voice technology. If you're passionate about AI, voice tech, and Python and ready to lead we’d love to hear from you. Contact: Ruben Pawar- 7030396637 ruben.pawar@aminfoweb.co.in

The Opportunity: We are seeking a talented and experienced Senior AI Engineer to play a pivotal role in developing and enhancing our conversational AI capabilities across multiple modalities (text, voice/telephony) and building intelligent agents to drive workflow automation. You will work closely with our Director of AI Engineering, product managers, and other engineering teams to bring our AI vision to life, primarily focusing on fine-tuning off-the-shelf Large Language Models (LLMs) and developing agentic systems for end-users (members, CBO staff, healthcare providers). This is a unique opportunity to apply your AI expertise to solve meaningful problems in the social and healthcare space, directly impacting people's lives. What You'll Do: Conversational AI Development & Fine-Tuning (Text & Voice): Lead the fine-tuning, evaluation, and deployment of pre-trained LLMs (e.g., Gemini, GPT series, open-source models) to create natural, empathetic, and effective conversational experiences for various user interactions via text-based channels (chat, SMS) and voice-based telephonic systems (IVR chatbots). Develop and implement strategies for data collection, preparation, and augmentation to support model fine-tuning and continuous improvement for both text and voice modalities. Design and implement robust evaluation frameworks to measure conversational AI performance, including metrics for accuracy, fluency, empathy, task completion, and call handling efficiency for telephonic agents. Work on prompt engineering, context management, and dialogue flow design to optimize conversational AI interactions across channels. Integrate and manage speech-to-text (STT) and text-to-speech (TTS) services for telephonic AI solutions. Agentic System Development: Design, build, and deploy AI agents that can reason, make decisions, and take actions to automate and optimize key workflows within the platform (e.g., intelligent referral initiation, proactive follow-ups, task management assistance). Develop agents capable of interacting with internal platform APIs, external data sources, and potentially third-party tools to achieve their goals. Explore and implement techniques for agent planning, tool usage, and multi-step reasoning. Collaboration & Technical Leadership: Collaborate closely with the Director of AI Engineering to define AI strategy, architecture, and technical roadmap for conversational AI and agentic systems. Partner with Product Managers to understand user needs and translate them into technical requirements for AI features. Work with platform and application engineers to integrate AI models and agents into the broader ecosystem. Mentor junior engineers and contribute to building a strong AI engineering culture. Stay up-to-date with the latest advancements in LLMs, conversational AI (text and voice), agent-based systems, and MLOps. MLOps & Productionization: Contribute to the development and maintenance of our MLOps infrastructure for training, deploying, monitoring, and iterating on AI models and agents in production. Ensure AI systems are scalable, reliable, and maintainable. Back-end Development: Solid understanding of back-end development principles and experience building or integrating with APIs (e.g., RESTful services) to connect AI models and agents with broader application systems. Familiarity with database technologies (SQL and/or NoSQL) and practical experience in how AI systems interact with data storage and retrieval for training, inference, and logging. What You'll Bring: Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Experience: 5+ years of hands-on experience in AI/ML engineering, with a significant focus on Natural Language Processing (NLP) and conversational AI. Demonstrable experience developing and deploying AI-powered telephonic chatbots or Interactive Voice Response (IVR) systems, including integration with STT/TTS technologies. Proven experience fine-tuning and deploying Large Language Models (LLMs) for specific tasks and domains. Strong understanding of model architectures, training techniques, and evaluation metrics. Demonstrable experience designing and building AI agents or systems that exhibit autonomous behavior, decision-making, and/or tool usage. Proficiency in Python and common AI/ML frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers, LangChain, LlamaIndex). Experience with cloud platforms (GCP preferred, AWS/Azure acceptable) and their AI/ML services (e.g., Vertex AI, SageMaker, cloud telephony APIs). Solid understanding of MLOps principles and experience with tools for model deployment, monitoring, and CI/CD for ML. Skills: Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. Ability to translate complex technical concepts to non-technical stakeholders. Proactive, self-starter with a passion for building impactful AI solutions. Nice to Haves: Experience working in the healthcare or social care domain. Familiarity with data privacy and security considerations in regulated environments (e.g., HIPAA). Experience with specific telephony platforms or APIs (e.g., Twilio, Vonage, Google Dialogflow CX). Contributions to open-source AI/ML projects.