106 Model Deployment Jobs - Page 5

Work mode – Currently this is remote (WFH) but it’s not permanent WFH , once business ask the candidate to come to office, they must relocate. Mandatory:- ML Engineer, MLOPS , end to end deployment, CI/CD, Docker and Kubernetes, model deployment Required Candidate profile Lead end-to-end development & delivery of Machine-Learning applications, emphasizing operations and monitoring. Deployment and operation of ML applications, following CI/CD best practices.

We are seeking a highly experienced and strategic Senior Manager Applied Science to lead and scale our applied research and machine learning initiatives. This role involves working at the intersection of cutting-edge AI/ML technologies and real-world business challenges to drive measurable impact. Key Responsibilities: Lead a team of applied scientists and machine learning engineers to develop scalable AI/ML models. Define and drive the scientific vision, strategy, and roadmap aligned with business goals. Collaborate cross-functionally with product, engineering, and analytics teams. Stay ahead of emerging technologies and apply them to solve complex business problems. Publish research, file patents, or present findings in internal/external forums as needed. Requirements: 10+ years of experience in applied science, machine learning, or data science roles. Strong background in statistics, optimization, deep learning, NLP, or related fields. Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. Proven experience in leading high-performance technical teams. Advanced degree (Ph.D. or Master’s) in Computer Science, Mathematics, Statistics, or a related field. Excellent communication and leadership skills. Preferred Qualifications: Experience in a product-led tech company or research lab environment. Track record of deploying ML models in production. Publications or patents in relevant areas.

Notice Period: Candidates with up to 1-month notice preferred Findability Sciences is looking for a hands-on AI expert with experience in developing and implementing comprehensive predictive and generative AI solutions. This is a core technical role rather than a support-based one. What we are looking for: 3+ years of experience in AI/ML with a Masters or Ph.D. in a relevant field Strong knowledge of Python, SQL, Machine Learning, LLMs, RAG, and Prompt Engineering Experience in predictive modeling, time series forecasting, and generative AI use cases Experience with ML Ops workflows model deployment, monitoring, versioning, and pipeline automation Key Responsibilities: Design and deploy predictive & GenAI solutions for real-world business use cases Build and optimize LLM-based workflows for summarization, retrieval, and Q&A Collaborate with cross-functional teams to define use cases and develop proof-of-concepts Translate business problems into ML/GenAI solutions and deliver outcomes Validate, compare, and optimize models for performance and scalability Apply basic ML Ops practices to ensure smooth deployment and monitoring of models Contribute to reusable pipelines, prompt tuning strategies, and production integration Present actionable insights and models to stakeholders clearly and effectively

What would you do? System Design: Architect and design end-to-end speech processing pipelines, from data acquisition to model deployment. Ensure systems are scalable, efficient, and maintainable. Advanced Modeling: Develop and implement advanced machine learning models for speech recognition, speaker diarization, and related tasks. Utilize state-of-the-art techniques such as deep learning, transfer learning, and ensemble methods. Research and Development: Conduct research to explore new methodologies and tools in the field of speech processing. Publish findings and present at industry conferences. Performance Optimization: Continuously monitor and optimize system performance, focusing on accuracy, latency, and resource utilization. Collaboration: Work closely with product management, data science, and software engineering teams to define project requirements and deliver innovative solutions. Customer Interaction: Engage with customers to understand their needs and provide tailored speech solutions. Assist in troubleshooting and optimizing deployed systems. Documentation and Standards: Establish and enforce best practices for code quality, documentation, and model management within the team. Required Skills 2+ years of experience in speech processing, machine learning, and model deployment. Demonstrated expertise in leading projects and teams. Technical skills: • Excellent knowledge in Python / Java programming. • In-depth knowledge of speech processing frameworks like, Wave2vec, Kaldi, HTK, DeepSpeech and Whisper. • Experience with NLP, STT, Speech to Speech LLMs and frameworks like Nvidia NEMO, PyAnnote. • Proficiency in Python and machine learning libraries such as TensorFlow, PyTorch, or Keras. • Experience with large-scale ASR systems, speaker recognition, and diarization algorithms. • Strong understanding of neural networks, sequence-to-sequence models, transformers and attention mechanisms. • Familiarity with NLP techniques and their integration with speech systems. • Expertise in deploying models on cloud platforms and optimizing for real-time applications. Good to have: • Experience with low-latency streaming ASR systems. • Knowledge of speech synthesis, STT (Speech-to-Text) and TTS (Text-to-Speech) systems. • Experience in multilingual and low-resource speech processing.

We are looking for an AI & Machine Learning Lead Engineer to lead the development and deployment of advanced AI and machine learning models. The ideal candidate will have a strong background in precision learning, large language models (LLMs), retrieval-augmented generation (RAG), natural language processing (NLP), and statistical methods for output analysis. This role involves leading a team of engineers and data scientists, setting strategic directions for AI/ML initiatives, and ensuring the delivery of impactful solutions that align with our business objectives. Key Responsibilities: Leadership and Strategy: Lead and manage a team of AI and machine learning engineers, providing technical guidance and mentorship. Oversee the entire machine learning lifecycle, from data collection and preprocessing to model training, evaluation, and deployment. Develop and drive the AI/ML strategy, aligning it with overall business goals. Collaborate with cross-functional teams, including data engineering, product management, and software development, to integrate ML models into production environments. Precision Learning and Large Language Models (LLMs): Design, develop, and optimize precision learning algorithms for specific business applications. Lead efforts in developing, fine-tuning, and deploying large language models (LLMs) to address various use cases such as text generation, summarization, translation, and conversational AI. Retrieval-Augmented Generation (RAG): Implement and optimize RAG systems to improve the performance and accuracy of AI solutions. Develop retrieval strategies that effectively integrate large-scale knowledge bases with LLMs to generate more accurate and contextually relevant outputs. Natural Language Processing (NLP): Develop and implement NLP models for tasks such as text classification, sentiment analysis, named entity recognition, summarization, and question-answering. Stay current with NLP research trends and advancements, implementing best practices to enhance model efficiency and performance. Output Analysis through Statistical Methods: Analyse model outputs using advanced statistical methods to ensure reliability, accuracy, and explainability. Implement A/B testing, hypothesis testing, and other statistical techniques to validate model performance and derive actionable insights. Research and Development: Stay abreast of the latest research in machine learning, deep learning, and AI. Propose and implement novel approaches to solve challenging problems. Collaborate with academic and research institutions to contribute to the machine learning community through publications, open-source projects, and conferences MLOps and Model Deployment: Collaborate with DevOps and data engineering teams to implement MLOps practices for CI/CD pipelines, model monitoring, and governance. Ensure scalable deployment of AI/ML models on cloud platforms (AWS, GCP, Azure) or on-premises environments. Mentorship and Team Leadership: Mentor and guide a team of machine learning engineers and data scientists, fostering a culture of continuous learning and innovation. Conduct regular code reviews, provide constructive feedback, and ensure adherence to best practices in machine learning. Required Qualifications: 8-10 years Experience with Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field; PhD is a plus. 5+ years of experience in machine learning and AI, with a strong focus on NLP, LLMs, RAG, and precision learning. Proven track record of leading AI/ML teams and projects from conception to deployment. Expertise in Python and relevant ML libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers. Strong understanding of NLP techniques, including transformer architectures (e.g., BERT, GPT). Experience in RAG techniques, knowledge retrieval systems, and integrating LLMs with external data sources. Proficiency in statistical analysis and hypothesis testing, with a strong foundation in experimental design. Excellent problem-solving skills, with the ability to articulate complex technical concepts to non-technical stakeholders. Preferred Qualifications: Experience with cloud services (AWS, GCP, Azure) for model deployment and scaling. Familiarity with MLOps practices, including model versioning, monitoring, and CI/CD pipelines for ML. Knowledge of advanced AI techniques such as reinforcement learning, meta-learning, and unsupervised learning. Strong publication record or contributions to the AI/ML community.

MLE/Sr. MLE Chennai, Bangalore, Hyderabad Who we are Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow. Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence. We are a Great Place to Work-Certified (2022-25), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the Best and Fastest Growing analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine. Curious about the role? What your typical day would look like? We are looking for a Machine Learning Engineer/Sr MLE who will work on a broad range of cutting-edge data analytics and machine learning problems across a variety of industries. More specifically, you will Engage with clients to understand their business context. Translate business problems and technical constraints into technical requirements for the desired analytics solution. Collaborate with a team of data scientists and engineers to embed AI and analytics into the business decision processes. What do we expect? 6+ years of experience with at least 4+ years of relevant MLOps experience. Proficient in a structured Python (Mandate) Proficient in Azure Databricks Follows good software engineering practices and has an interest in building reliable and robust software. Good understanding of DS concepts and DS model lifecycle. Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/ PySpark is desirable. Model deployment / model monitoring experience is desirable. CI/CD pipeline creation is good to have. Excellent written and verbal communication skills. B.Tech from Tier-1 college / M.S or M. Tech is preferred. You are important to us, lets stay connected! Every individual comes with a different set of skills and qualities so even if you dont tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow. We are an equal-opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire. Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry. Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities.