16 Tts Jobs

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.

We are hiring "Senior Speech & Language R&D Engineer" for one of our Product based-MNC client @Pune location EXP-4+ Mode-Permanent Mandatory Skills: Computational Linguist 4+ years of working experience 2+ years of experience in speech domain/technologies. Experiences in Python programming . Exposure to different speech toolkit (HTK, SLTK). Working knowledge of Linux/Unix and shell scripting . Fluent in English both written and spoken. Experience in TTS voice building using Festvox or any other technology ( preferred). Programming experience with scripting languages Perl and/or AWK (preferred). Knnowledge of Natural Language Processing and Machine Learning (preferred).

We're looking for an experienced AI/ML Technical Lead to architect and drive the development of our intelligent conversation engine. Youll lead model selection, integration, training workflows (RAG/fine-tuning), and scalable deployment of natural language and voice AI components. This is a foundational hire for a technically ambitious platform. Key Responsibilities AI System Architecture: Design the architecture of the AI-powered agent including LLM-based conversation workflows, voice bots, and follow-up orchestration. Model Integration & Prompt Engineering: Leverage APIs from OpenAI, Anthropic, or deploy open models (e.g., LLaMA 3, Mistral). Implement effective prompt strategies and retrieval-augmented generation (RAG) pipelines for contextual responses. Data Pipelines & Knowledge Management: Build secure data pipelines to ingest, embed, and serve tenant-specific knowledge bases (FAQs, scripts, product docs) using vector databases (e.g., Pinecone, Weaviate). Voice & Text Interfaces: Implement and optimize multimodal agents (text + voice) using ASR (e.g., Whisper), TTS (e.g., Polly), and NLP for automated qualification and call handling. Conversational Flow Orchestration: Design dynamic, stateful conversations that can take actions (e.g., book meetings, update CRM records) using tools like LangChain, Temporal, or n8n. Platform Scalability: Ensure models and agent workflows scale across tenants with strong data isolation, caching, and secure API access. Lead a Cross-Functional Team: Collaborate with backend, frontend, and DevOps engineers to ship intelligent, production-ready features. Monitoring & Feedback Loops: Define and monitor conversation analytics (drop-offs, booking rates, escalation triggers), and create pipelines to improve AI quality continuously. Qualifications Must-Haves: 5+ years of experience in ML/AI, with at least 2 years leading conversational AI or LLM projects. Strong background in NLP, dialog systems, or voice AI preferably with production experience. Experience with OpenAI, or open-source LLMs (e.g. LLaMA, Mistral, Falcon) and orchestration tools (LangChain, etc.). Proficiency with Python and ML frameworks (Hugging Face, PyTorch, TensorFlow). Experience deploying RAG pipelines, vector DBs (e.g. Pinecone, Weaviate), and managing LLM-agent logic. Familiarity with voice processing (ASR, TTS, IVR design). Solid understanding of API-based integration and microservices. Deep care for data privacy, multi-tenancy security, and ethical AI practices. Nice-to-Haves: Experience with CRM ecosystems (e.g. Salesforce, HubSpot) and how AI agents sync actions to CRMs. Knowledge of sales pipelines and marketing automation tools. Exposure to calendar integrations (Google Calendar API, Microsoft Graph). Knowledge of Twilio APIs (SMS, Voice, WhatsApp) and channel orchestration logic. Familiarity with Docker, Kubernetes, CI/CD, and scalable cloud infrastructure (AWS/GCP/Azure). What We Offer Founding team role with strong ownership and autonomy Opportunity to shape the future of AI-powered sales Flexible work environment Competitive salary Access to cutting-edge AI tools and training resources Post your resume and any relevant project links (GitHub, blog, portfolio) to career@sourcedeskglobal.com. Include a short note on your most interesting AI project or voicebot/conversational AI experience.

JOB DESCRIPTION JOB TITLE Maintenance Executive-BHS LEVEL & GRADE B1 & B DIVISION/ DEPARTMENT Engineering & Maintenance /Mechanical COST CENTER 72180 REPORTS TO P Mohana Sundaram DATE 17/03/25 JOB PURPOSE (Provide an overview of the job, its context in the organization and the contribution that it makes) Responsible for day-to-day operation and maintenance/Comprehensive Maintenance activities, repair, troubleshooting, Inspection work of various Mechanical Installations. Having specialization skill & experience in Baggage handling system (BHS) in any large industry/organization. Also, having experience in one or more of the Mechanical Airport installations such as Passenger Boarding Bridges, Elevators & Escalators, Travelator (VHT) , Fire Protection System, Heating Ventilation and Air conditioning etgc. Co-ordination & Involving in both T1 & T2 Mechanical Maintenance activities. PRINCIPAL ACCOUNTABILITIES (List the responsibilities/ duties associated with the job and the major activities associated with each responsibility. For each responsibility/ duty listed, give the factors on which an individuals performance is judged) Accountabilities Major Activities Timely Review of Installations and maintenance task Compliance of Mechanical department activities with national and international standards Effective Maintenance planning and ensure timely Completion Having specialist knowledge & experience on Baggage Handling System (BHS) Having expertise on BHS SCADA, Self Bag Drop (SBD), Departure & Arrival Conveyor Systems, Baggage Sortation Process, Check-in Conveyors, Baggage Diverter system, Tilt Tray Sorter (TTS), Baggage Conveyor, Power Curve System, High Speed Diverter (HSD), Vertical Sorter Unit (VSU), Baggage Carousel etc Having exposure in trouble shooting of BHS system Carrying out scheduled maintenance, breakdown maintenance & predictive maintenance of Mechanical Installations (i.e. VHT, FFS, BHS, PBB & HVAC). Contract management of Mechanical Installations like HVAC, FFS, BHS, PBB & VHT Ensure serviceability of all Mechanical Installations as per Contract agreement including HVAC, FFS, VHT, PBB & BHS Co-ordination with the Project team/EPC/Design Team for any DLP & other additional improvement activities on Mechanical Installations. Effective Implementation of IMS (QMS, EnMS, EMS etc) Ensure that suitable Planned Preventative Maintenance (PPM) Plans are developed and implemented on Mechanical Installations Ensure that the Maintenance plan is fully documented and uploaded into SAP, ePalm. Ensures timely closing of entire mechanical department maintenance work orders(in SAP) during shifts Ensure updation of all maintenance documents Contract Management: efficiently and professionally manage contracts To ensure the labour regulations in the contract, legal & Statutory compliance Compliance; coordinates with Daily operation and contractors Training to Staff Training to staff in the troubleshooting & personal safety Develops, implements and trains staff; instructs staff in regulations, codes and work techniques and the proper use and maintenance of all equipment Effective Spare Parts Managements Availability and proper storage of all identified key spare parts Spare parts Planning –cost, criticality Analysis Spare parts control-Regularly/irregularly used spare parts Ensures availability of adequate materials to conduct work activities; approves and/or initiates orders for new/replacement materials. DIMENSIONS (Financial or Non-financial parameters which are directly impacted/controlled by the role or indirectly influences/contributes to in a measurable way) Financial (Eg: Budgets, project costs, capex etc.,) Non-Financial (Eg: No. of direct/ indirect reports, headcount in projects, no. of clients handled etc.,) OPERATING NETWORK (Internal/ External contact groups with which the position holder interact/ work for achieving the organization’s objectives) Internal External External Contractors Concessionaries Suppliers, vendors JOB SPECIFICATION 5.1.Education qualification and certifications (Indicate the level of education and certifications required) Degree/Diploma in Mechanical/Electrical/Electronics Engineering or Equivalent 5.2. Years of Experience ( Years of relevant experience required) Degree Holders: Should have Minimum 3 years of relevant working Experience in a large industry/Airport/corporate sector. Diploma Holders: Should have Minimum 6 years of relevant working Experience in large industry/Airport/corporate sector. 5.3. Computer skills (Indicate the required knowledge on software, applications, hardware etc., that are required) MS-Office (Word, Excel, Power point, Outlook) 5.4. Knowledge and work skills [Indicate what knowledge (machines, equipment, processes, systems etc.,) and work skills are required] Working experience in operation & maintenance of Baggage Handling System. Having expertise/experience in BHS SCADA, Self Bag Drop (SBD), Departure & Arrival Conveyor Systems, Baggage Sortation Process, Check-in Conveyors, Baggage Diverter system, Tilt Tray Sorter (TTS), Baggage Conveyor, Power Curve System, High Speed Diverter (HSD), Vertical Sorter Unit (VSU), Baggage Carousel etc Good knowledge in contract management like processing of bills ensures labour legal compliance, ensures proper wage disbursement to contract staffs by contractor etc. Good knowledge in preparation preventive maintenance schedule, execution as per schedule, maintaining of maintenance records, allocation of works to subordinates etc. Good knowledge in identify, procure & maintenance of spares Should have knowledge in Airport Operations in its totality - like movement of aircrafts, Check in process etc. Excellent Communication Skills in English (Verbal & Written)