212 Interpretability Jobs - Page 7

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

Omega Healthcare Management Services Private Limited TAMIL NADU Posted On 05 Jun 2025 End Date 19 Jun 2025 Required Experience 5 - 8 Years Basic Section No. Of Openings 1 Grade T2 Designation Technology Specialist - Technology Closing Date 19 Jun 2025 Organisational Country IN State TAMIL NADU City CHENNAI Location Chennai-I Skills Skill SDLC PL/SQL SOLUTION ARCHITECTURE JAVA UNIX .NET SOA MICROSOFT SQL SERVER SQL ASP.NET Education Qualification No data available CERTIFICATION No data available Job Description Role : Data Scientist Educational Qualification : ME/BE / MCA Experience Required : 4+ Years Shifts : Day shift Skills & Responsibilities: Experience: 4+ years in machine learning, deep learning, or generative AI. Programming & Frameworks: Strong Python scripting with Pandas, NumPy, OOPs concepts. Experience with PyTorch, TensorFlow, Keras, Hugging Face Transformers. Proficiency in SQL queries when needed. Generative AI & NLP: Experience with LLMs, GANs, VAEs, Diffusion Models. Familiarity with OpenAI GPT, DALL·E, Stable Diffusion. Deep knowledge of NLP techniques & deep learning architectures (RNN, CNN, LSTM, GRU). Machine Learning & Statistics: Understanding of ML/DL algorithms, statistical analysis, and feature engineering. Theoretical knowledge of Random Forest, SVM, Boosting, Bagging, Regression (Linear & Logistic), and Unsupervised Learning. MLOps & Deployment: Familiarity with Cloud platforms (AWS, Azure, GCP). Experience in MLOps, CI/CD, Docker, Kubernetes. Comfortable with Linux systems and GPU-based deep learning. Research & Ethics: Contributions to AI research, open-source projects, or Kaggle competitions. Awareness of AI ethics, bias mitigation, and model interpretability. Soft Skills & Work Environment: Ability to work independently and deliver results. Experience in an agile development environment. Knowledge of computer vision is a plus.

ob Title: AI Engineer ey Responsibilities: Architect, develop, and deploy advanced AI solutions, encompassing Machine Learning, Generative AI, NLP, and LLMs. Remain abreast of the latest AI advancements, actively researching and integrating emerging trends and technologies such as LLMOps, Large Model Deployments, LLM Security, Vector Databases, etc. Streamline data modeling processes to automate tasks, enhance data preparation, and facilitate data exploration to optimize business outcomes. Collaborate closely with cross-functional teams, including business units, accounts teams, researchers, and engineers, to translate business requirements into actionable AI solutions. Exhibit expertise in responsible AI practices, ensuring fairness, transparency, and interpretability in all models. Identify and mitigate potential risks related to AI and LLM development and deployment, emphasizing data trust and security. Contribute to the professional development of the AI team by mentoring engineers, fostering knowledge sharing, and promoting a culture of continuous learning. This role is based in a lab environment and involves hands-on, fast-paced, and high-intensity work. The ideal candidate should be proactive, adaptable, and comfortable working in a dynamic and demanding setting. ualifications: Minimum of 2 years of hands-on experience in developing and deploying AI solutions, with a proven track record of success. Master’s degree in Computer Science, Artificial Intelligence, or a related field (or equivalent experience). Proficiency in Machine Learning, NLP, Generative AI, and LLMs, including their architectures, algorithms, and training methodologies. Understanding of LLMOps principles, Prompt Engineering, In-Context Training, LangChain, and Reinforcement Learning. Familiarity with best practices for large model deployment, monitoring, management, and scalability. Experience with Azure Cloud services. Strong communication, collaboration, and problem-solving abilities. Commitment to ethical AI practices and security standards. Proficiency in deep learning frameworks and languages such as Azure ML platform, Python, PyTorch, etc. Hands-on experience with ML frameworks, libraries, and third-party ML models. Expertise in building solutions using AI/ML/DL open-source tools and libraries. Strong analytical and problem-solving skills. Ability to write optimized and clear code, and address complex technical challenges effectively. Self-motivated and fast learner, with a proactive approach to learning new technologies. Proficiency in data analysis and troubleshooting skills. Experience in building AI/ML/DL solutions for NLP/text applications, with familiarity in reinforcement learning being advantageous. Minimum of 2 years of experience on AI/ML/DL projects, with specialization or certification in Artificial Intelligence being a plus. Good knowledge of Azure AI/Cognitive Services tools. Show more Show less

Description The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key Responsibilities Include Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. Engage in effective technical communication (written & spoken) with coordination across teams. Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. Publish research papers in internal and external venues of repute Support on-call activities for critical issues Basic Qualifications Master’s in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters Experience in deep learning, machine learning, and data science. Proficiency in coding and software development, with a strong focus on machine learning frameworks. Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications Track record of diving into data to discover hidden patterns and conducting error/deviation analysis Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations The motivation to achieve results in a fast-paced environment. Exceptional level of organization and strong attention to detail Comfortable working in a fast paced, highly collaborative, dynamic work environment Papers published in AI/ML venues of repute About The Team The team works on building Responsible AI alignment techniques for the Large Language Model (LLM) that drives Alexa+. We work on recent advances in LLM science including automated prompt optimisation, supervised fine tuning, Re-inforcement learning, model distillation, retrieval augmented generation, model & prompt interpretability and LLM driven data generation. The techniques are used to tune a foundational model to align with Alexa’s policies on Responsible AI across dimensions of Appropriateness, Safety, Security, Privacy and Fairness. We focus on building cross-lingual methods to support 100s of millions of Alexa customers across the world speaking 15+ languages. A scientist on the team works on the following - a) running experiments to evaluate promising ideas to achieve a customer experience goal; b) Identify a research theme and follow recent advances in the space (papers/articles); c) collaborate with other scientists (within and outside team) to identify a science direction to solve a problem; d) publish papers in peer-reviewed conferences & journals of repute; e) Collaborate with faculty members in Indian academia (e.g. IIT-B). The team’s work was part of the Alexa+ launch announcement: https://www.aboutamazon.com/news/devices/new-alexa-generative-artificial-intelligence Basic Qualifications Experience programming in Java, C++, Python or related language Experience building machine learning models or developing algorithms for business application Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning Experience researching about machine learning, deep learning, NLP, computer vision, data science Preferred Qualifications Have publications at top-tier peer-reviewed conferences or journals Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Company - ADCI - Karnataka Job ID: A2938440 Show more Show less

Responsibilities: • Design and develop GenAI-based solutions using LLMs (e.g., Bedrock, OpenAI, Claude) for text, image, table, diagram, and multi-modal applications. • Implement a multi-agent system that integrates structured and unstructured data sources, including knowledge graphs, embeddings, and vector databases. • Build and deploy agentic AI workflows capable of autonomous task completion, using frameworks like LangChain, LangGraph, or CrewAI. • Perform fine-tuning, retraining, or adaptation of open-source or proprietary LLMs for specific domain tasks. • Collaborate with data scientists and domain experts to curate and preprocess training datasets. • Integrate models with scalable backend APIs or pipelines (REST, FastAPI, gRPC) for real-world applications. • Stay updated with state-of-the-art research and actively contribute to enhancing model performance and interpretability. • Optimize inference, model serving, and memory management for deployment at scale. Qualifications: • Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, Data Science, or related field. • 5+ years of hands-on experience in Deep Learning, NLP, and LLMs. • Proven experience with at least one end-to-end project involving multi-modal RAG and Agentic AI. • Proficient in Python and ML/DL libraries such as PyTorch, TensorFlow, Transformers (HuggingFace), LangChain, LangGraph, Bedrock, or similar • Experience in fine-tuning or adapting LLMs (using LoRA, QLoRA, PEFT, or full fine-tuning). • Experience in building a multi-agent system. • Strong understanding of knowledge graphs, embeddings, vector databases (e.g., FAISS, Chroma, Weaviate), and prompt engineering. • Strong understanding and experience of a cloud platform like AWS. • Familiarity with containerization (Docker, Kubernetes) Preferred Skills • Experience in the Biopharma industry. • Design and implement user-friendly interfaces for AI applications. • Utilize modern web frameworks (e.g., React, Vue.js) to create engaging user experiences. • Develop scalable and efficient backend systems to support the deployment of AI models. • Integrate with cloud platforms (AWS) for infrastructure management. • Hands-on experience in vision-language models (e.g., CLIP, BLIP, LLaVA). • Publications, Kaggle competitions, or GitHub projects in GenAI Show more Show less

About the Company Resources is the backbone of Publicis Groupe, the world’s third-largest communications group. Formed in 1998 as a small team to service a few Publicis Groupe firms, Re:Sources has grown to 5,000+ people servicing a global network of prestigious advertising, public relations, media, healthcare, and digital marketing agencies. We provide technology solutions and business services including finance, accounting, legal, benefits, procurement, tax, real estate, treasury, and risk management to help Publicis Groupe agencies do their best: create and innovate for their clients. In addition to providing essential, everyday services to our agencies, Re:Sources develops and implements platforms, applications, and tools to enhance productivity, encourage collaboration, and enable professional and personal development. We continually transform to keep pace with our ever-changing communications industry and thrive on a spirit of innovation felt around the globe. With our support, Publicis Groupe agencies continue to create and deliver award-winning campaigns for their clients. About the Role The main purpose of this role is to advance the application of business intelligence, advanced data analytics, and machine learning for Marcel. The Data Scientist will work with other data scientists, engineers, and product owners to ensure the delivery of all commitments on time and in high quality. Responsibilities Design and develop advanced data science and machine learning algorithms, with a strong emphasis on Natural Language Processing (NLP) for personalized content, user understanding, and recommendation systems. Work on end-to-end LLM-driven features, including fine-tuning pre-trained models (e.g., BERT, GPT), prompt engineering, vector embeddings, and retrieval-augmented generation (RAG). Build robust models on diverse datasets to solve for semantic similarity, user intent detection, entity recognition, and content summarization/classification. Analyze user behaviour through data and derive actionable insights for platform feature improvements using experimentation (A/B testing, multivariate testing). Architect scalable solutions for deploying and monitoring language models within platform services, ensuring performance and interpretability. Collaborate cross-functionally with engineers, product managers, and designers to translate business needs into NLP/ML solutions. Regularly assess and maintain model accuracy and relevance through evaluation, retraining, and continuous improvement processes. Write clean, well-documented code in notebooks and scripts, following best practices for version control, testing, and deployment. Communicate findings and solutions effectively across stakeholders — from technical peers to executive leadership. Contribute to a culture of innovation and experimentation, continuously exploring new techniques in the rapidly evolving NLP/LLM space. Qualifications Minimum Experience (relevant): 3 years Maximum Experience (relevant): 5 years Required Skills Proficiency in Python and NLP frameworks: spaCy, NLTK, Hugging Face Transformers, OpenAI, LangChain. Strong understanding of LLMs, embedding techniques (e.g., SBERT, FAISS), RAG architecture, prompt engineering, and model evaluation. Experience in text classification, summarization, topic modeling, named entity recognition, and intent detection. Experience deploying ML models in production and working with orchestration tools such as Airflow, MLflow. Comfortable working in cloud environments (Azure preferred) and with tools such as Docker, Kubernetes (AKS), and Git. Strong experience working with data science/ML libraries in Python (SciPy, NumPy, TensorFlow, SciKit-Learn, etc.) Strong experience working in cloud development environments (especially Azure, ADF, PySpark, DataBricks, SQL) Experience building data science models for use on front end, user facing applications, such as recommendation models Experience with REST APIs, JSON, streaming datasets Understanding of Graph data, Neo4j is a plus Strong understanding of RDBMS data structure, Azure Tables, Blob, and other data sources Understanding of Jenkins, CI/CD processes using Git, for cloud configs and standard code repositories such as ADF configs and Databricks Preferred Skills Bachelor's degree in engineering, computer science, statistics, mathematics, information systems, or a related field from an accredited college or university; Master's degree from an accredited college or university is preferred. Or equivalent work experience. Advanced knowledge of data science techniques, and experience building, maintaining, and documenting models Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases preferably Graph DB. Experience building and optimizing ADF and PySpark based data pipelines, architectures and data sets on Graph and Azure Datalake. Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement. Strong analytic skills related to working with unstructured datasets. Build processes supporting data transformation, data structures, metadata, dependency and workload management. A successful history of manipulating, processing and extracting value from large disconnected datasets. Strong project management and organizational skills. Experience supporting and working with cross-functional teams in a dynamic environment. Show more Show less

hackajob is collaborating with American Express to connect them with exceptional tech professionals for this role. You Lead the Way. We’ve Got Your Back. With the right backing, people and businesses have the power to progress in incredible ways. When you join Team Amex, you become part of a global and diverse community of colleagues with an unwavering commitment to back our customers, communities and each other. Here, you’ll learn and grow as we help you create a career journey that’s unique and meaningful to you with benefits, programs, and flexibility that support you personally and professionally. At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. As part of Team Amex, you'll experience this powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express. How will you make an impact in this role? We are seeking a highly motivated data scientist to join our team. The role offers the opportunity to contribute to cutting edge data science solutions in NLP, Anomaly detection and overall enterprise risk management. You will play a key role in developing proof of concepts, scalable tools, and models that protect company from (not limited to) financial, reputational, operations risks. Roles And Responsibilities Collaborate on NLP based solutions for identifying Key Risk Indicators (KRIs) across customer interactions Support the development of out of pattern detection systems, using time series models and statistical methods Contribute to the planned expansion of risk monitoring tools and models into new areas within enterprise Assist in building new age GenAI models Partner closely with global stakeholders and platforms team to deliver high impact analytics in a fast paced, regulated environment and ensure end to end completion of projects from ideation to production stages Apply innovative concepts to measure & manage risks consistently with regulatory and governance requirements Identify emerging risk themes along with ideas for solutions Ability to work on multiple projects and ad-hoc tasks simultaneously Minimum Qualifications Bachelor's/master's degree in computer science, Engineering, Statistics, Mathematics or a related field 1-3 years of hands-on experience in data science, analytics, or risk modeling Proficiency in SQL & Python Understanding of machine learning principles and model development lifecycle and steps Strong analytical, problem-solving and communication skills Preferred Qualifications Experience in working on NLP projects Experience in working on GenAI projects Exposure to risk domains Familiarity with model governance - model interpretability and hyperparameter tuning Experience working with large scale datasets and productionizing data science solutions Benefits We back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally: Competitive base salaries Bonus incentives Support for financial-well-being and retirement Comprehensive medical, dental, vision, life insurance, and disability benefits (depending on location) Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need Generous paid parental leave policies (depending on your location) Free access to global on-site wellness centers staffed with nurses and doctors (depending on location) Free and confidential counseling support through our Healthy Minds program Career development and training opportunities American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law. Offer of employment with American Express is conditioned upon the successful completion of a background verification check, subject to applicable laws and regulations. Show more Show less

DataScientist Remote Contract India Key Responsibilities • Develop and implement advanced data science models, including clustering, segmentation, random forest, gradient boosting (e.g., XGBoost), ensemble techniques, and constrained optimization methods. • Explore additional approaches such as neural networks, support vector machines, and Bayesian methods to address diverse business challenges. • Design and execute matched-pair analyses, predictive analytics, and other statistical approaches to address business challenges. • Collaborate with stakeholders to identify use cases for advanced modeling and provide data science-driven solutions. • Engineer features and preprocess data to optimize model performance and interpretability. • Work with large datasets from diverse sources, ensuring data quality and consistency. • Perform model evaluation, tuning, and validation using appropriate techniques and metrics. • Deploy machine learning models into production environments, ensuring scalability and reliability. • Mentor and collaborate with team members to share knowledge and best practices. • Stay current on advancements in data science, tools, and methodologies to bring innovation to the team. Qualifications • 4–8 years of work experience in classification, regression, clustering, association, dimension reduction, natural language processing (NLP), experiments, and optimization. • Proficiency in Python or R, with strong experience in libraries such as scikit-learn, TensorFlow, PyTorch, or similar. • Advanced knowledge of machine learning algorithms, statistical modeling, and data preprocessing techniques. • Strong foundation in mathematics, statistics, and computer science. • Proficiency in SQL for data extraction and manipulation. • Demonstrated ability to handle complex datasets and derive actionable insights. • Excellent communication and collaboration skills to work with cross-functional teams. • Experience deploying models into production environments is highly desirable Thanks, and Regards Snehil Mishra snehil@ ampstek.com linkedin.com/in/snehil-mishra-1104b2154 Desk-6093602673Extension-125 www.ampstek.com https://www.linkedin.com/company/ampstek/jobs/ Ampstek – Global IT Partner Registered Offices: North America and LATM: USA|Canada|Costa Rica|Mexico Europe:UK|Germany|France|Sweden|Denmark|Austria|Belgium|Netherlands|Romania|Poland|Czeh Republic|Bulgaria|Hungary|Ireland|Norway|Croatia|Slovakia|Portugal|Spain|Italy|Switzerland|Malta| Portugal APAC:Australia|NZ|Singapore|Malaysia|South Korea|Hong Kong|Taiwan|Phillipines|Vietnam|Srilanka|India MEA :South Africa|UAE|Turkey|Egypt Show more Show less

Coditas Solutions is seeking a highly skilled and motivated Lead Data Scientist to join our dynamic team. As a Data Scientist, you will play a key role in designing, implementing, and optimizing machine learning models and algorithms to solve complex business challenges. If you have a passion for leveraging AI and ML technologies to drive innovation, this is an exciting opportunity to contribute to groundbreaking projects. Roles and Responsibilities Lead the design, implementation, and optimization of machine learning and AI models using Python and R. Develop and deploy scalable predictive models and decision-making systems for business-critical applications. Conduct advanced exploratory data analysis (EDA) to extract actionable insights from structured and unstructured data. Collaborate with data engineers to ensure high-quality, well-structured datasets for training and inference. Own the end-to-end model lifecycle, from development to deployment, monitoring, and continuous improvement. Guide the integration of AI/ML models into enterprise-level production systems in collaboration with software engineering teams. Provide technical leadership, mentoring junior data scientists and driving best practices in ML model development and deployment. Stay ahead of the curve by evaluating and implementing cutting-edge AI/ML advancements, including LLMs and GenAI models. Work closely with stakeholders, product teams, and clients to define business challenges and design AI-driven solutions. Drive model interpretability, explainability, and responsible AI practices within the organization. Technical Skills 6-14 years of hands-on experience in designing and implementing machine learning, deep learning, and AI solutions . Strong programming expertise in Python and R , with proficiency in implementing complex algorithms. Extensive experience with cloud platforms (AWS, Azure, GCP) for deploying scalable machine learning solutions. Proficiency in MLOps practices , ensuring robust model deployment, monitoring, and retraining workflows. Strong background in classical ML algorithms (e.g., linear regression, logistic regression, decision trees, random forests, SVM) and deep learning architectures (CNNs, RNNs, Transformers). Hands-on experience with ML/DL libraries such as Scikit-learn, TensorFlow, PyTorch, Keras, NLTK, OpenCV . Expertise in data preprocessing, feature engineering, and model evaluation for structured and unstructured data. Proven experience in selecting and engineering relevant features to enhance model performance. Understanding of LLMs (Large Language Models) and GenAI technologies , with the ability to optimize their usage for enterprise applications. Strong problem-solving, critical thinking, and analytical skills to drive AI/ML innovations. Exceptional communication and leadership skills , with experience in mentoring teams and collaborating with cross-functional stakeholders. Join our team and be part of a fast-paced and innovative work environment where your expertise will make a significant impact on our organization's growth and success. Show more Show less

Mandatory Skills ✅ Python – Minimum 4+ years of hands-on experience ✅ AI/ML – Minimum 5+ years of strong experience in designing and implementing machine learning models, algorithms, and AI-driven solutions ✅ SQL – Minimum 2+ years of experience working with large datasets and query optimization Key Responsibilities Lead the development of advanced AI/ML models for real-world applications Collaborate with data scientists, analysts, and software engineers to deploy end-to-end data-driven solutions Design scalable machine learning pipelines and automate model deployment Work on feature engineering, model tuning, and performance optimization Ensure best practices in AI/ML model governance, performance monitoring, and retraining Preferred Qualifications Experience with ML Ops tools (e.g., MLflow, Kubeflow) Strong knowledge of data preprocessing, feature extraction, and model interpretability Exposure to cloud platforms (AWS, Azure, or GCP) Familiarity with deep learning frameworks (TensorFlow, PyTorch, etc.) is a plus 💡 Work Mode: Flexible – Choose to work from our Cochin , Trivandrum office or fully remote ! 📅 Start ASAP! We’re only considering candidates with a notice period of 0–30 days. Skills: data science,deep learning frameworks,ai,python,feature extraction,ai/ml,sql,cloud platforms,model interpretability,data preprocessing,ml,ml ops tools Show more Show less

About Us: Senior Data Scientist – Kotak811 Kotak811 is a Neobank incubated by Kotak Mahindra Bank, with a view of providing completely digitized banking services in the convenience of the customer’s mobile phone. 811 is an early mover in the Indian fintech space that started off as a downloadable savings bank account in 2017, post demonetization, when India took one step closer to a digital economy. The Senior Data Scientist in Bangalore (or Mumbai) will be part of the 811 Data Strategy Group that comprises Data Engineers, Data Scientists and Data Analytics professionals. He/she will be associated with one of the key functional areas such as Product Strategy, Cross Sell, Asset Risk, Fraud Risk, Customer Experience etc. and help build robust and scalable solutions that are deployed for real time or near real time consumption and integrated into our proprietary Customer Data Platform (CDP). This is an exciting opportunity to work on data driven analytical solutions and have a profound influence on the growth trajectory of a super fast evolving digital product. Key Requirements of The Role Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 4 - 8 Years of relevant experience in the space Expertise in mining AI/ML opportunities from open ended business problems and drive solution design/development while closely collaborating with engineering, product and business teams Strong understanding of advanced data mining techniques, curating, processing and transforming data to produce sound datasets Create great data stories with expertise in robust EDA and statistical inference. Experience in Experimentation design a big plus but should understands fundamentals of A/B testing Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. Exposure to Deep Learning applications and tools like TensorFlow, Theano, Torch, Caffe is a big plus Experience with analytical programming languages, tools and libraries (Python a must) as well as Shell scripting Very proficient is SQL and other relational databases. Proficient in PySpark. Experience in using NoSQL databases. Candidate who is able to handle unstructured data with ease preferred Experience in working with AWS/Azure/GCP ecosystems. MLOps tools experience a plus Good understanding of programming best practices and building code artifacts for reuse. Should be comfortable with version controlling and collaborate comfortably in tools like git Ability to create frameworks that can perform model RCAs using analytical and interpretability tools. Should be able to peer review model documentations/code bases and find opportunities Experience in end-to-end delivery of AI driven Solutions (Deep learning , traditional data science projects) a big plus Strong communication, partnership and teamwork skills Ability to work in an extremely fast paced environment, meet deadlines, and perform at high standards with limited supervision A self-starter who is looking to build grounds up and contribute to the making of a potential big name in the space Experience in Banking and financial services is a plus. However, sound logical reasoning and first principles problem solving are even more critical A typical day in the life of the job role: 1. As a key partner at the table, attend key meetings with the business team to bring in the data perspective to the discussions 2. Perform comprehensive data explorations around to generate inquisitive insights and scope out the problem 3. Develop simplistic to advanced solutions to address the problem at hand. We believe in making swift (albeit sometimes marginal) impact to business KPIs and hence adopt an MVP approach to solution development 4. Build re-usable code analytical frameworks to address commonly occurring business questions 5. Perform 360-degree customer profiling and opportunity analyses to guide new product strategy. This is a nascent business and hence opportunities to guide business strategy are plenty 6. Guide team members on data science and analytics best practices to help them overcome bottlenecks and challenges 7. The role will be an approximate 60% IC – 40% leading and the ratios can vary basis need and fit 8. Develop Customer-360 Features that will be integrated into the Customer Data Platform (CDP) to enhance the single view of our customer Website: https://www.kotak811.com/

About Us: Data Scientist – 3 – Kotak811 Kotak811 is a Neobank incubated by Kotak Mahindra Bank, with a view of providing completely digitized banking services in the convenience of the customer’s mobile phone. 811 is an early mover in the Indian fintech space that started off as a downloadable savings bank account in 2017, post demonetization, when India took one step closer to a digital economy. The Data Scientist-3 in Bangalore (or Mumbai) will be part of the 811 Data Strategy Group that comprises Data Engineers, Data Scientists and Data Analytics professionals. He/she will be associated with one of the key functional areas such as Product Strategy, Cross Sell, Asset Risk, Fraud Risk, Customer Experience etc. and help build robust and scalable solutions that are deployed for real time or near real time consumption and integrated into our proprietary Customer Data Platform (CDP). This is an exciting opportunity to work on data driven analytical solutions and have a profound influence on the growth trajectory of a super fast evolving digital product. Key Requirements of The Role Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 7 - 10 Years of relevant experience in the space Expertise in mining AI/ML opportunities from open ended business problems and drive solution design/development while closely collaborating with engineering, product and business teams Strong understanding of advanced data mining techniques, curating, processing and transforming data to produce sound datasets. Strong experience in NLP, time series forecasting and recommendation engines preferred Create great data stories with expertise in robust EDA and statistical inference. Should have at least a foundational understanding in Experimentation design Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. Exposure to Deep Learning applications and tools like TensorFlow, Theano, Torch, Caffe preferred Experience with analytical programming languages, tools and libraries (Python a must) as well as Shell scripting. Should be proficient in developing production ready code as per best practices. Experience in using Scala/Java/Go based libraries a big plus Very proficient is SQL and other relational databases along with PySpark or Spark SQL. Proficient is using NoSQL databases. Experience in using GraphDBs like Neo4j a plus. Candidate should be able to handle unstructured data with ease. Candidate should have experience in working with MLEs and be proficient (with experience) in using MLOps tools. Should be able to consume the capabilities of said tools with deep understanding of deployment lifecycle. Experience in CI/CD deployment is a big plus. Knowledge of key concepts in distributed systems like replication, serialization, concurrency control etc. a big plus Good understanding of programming best practices and building code artifacts for reuse. Should be comfortable with version controlling and collaborate comfortably in tools like git Ability to create frameworks that can perform model RCAs using analytical and interpretability tools. Should be able to peer review model documentations/code bases and find opportunities Experience in end-to-end delivery of AI driven Solutions (Deep learning , traditional data science projects) Strong communication, partnership and teamwork skills Should be able to guide and mentor teams while leading them by example. Should be an integral part of creating a team culture focused on driving collaboration, technical expertise and partnerships with other teams Ability to work in an extremely fast paced environment, meet deadlines, and perform at high standards with limited supervision A self-starter who is looking to build grounds up and contribute to the making of a potential big name in the space Experience in Banking and financial services is a plus. However, sound logical reasoning and first principles problem solving are even more critical A typical day in the life of the job role: 1. As a key partner at the table, attend key meetings with the business team to bring in the data perspective to the discussions 2. Perform comprehensive data explorations around to generate inquisitive insights and scope out the problem 3. Develop simplistic to advanced solutions to address the problem at hand. We believe in making swift (albeit sometimes marginal) impact to business KPIs and hence adopt an MVP approach to solution development 4. Build re-usable code analytical frameworks to address commonly occurring business questions 5. Perform 360-degree customer profiling and opportunity analyses to guide new product strategy. This is a nascent business and hence opportunities to guide business strategy are plenty 6. Guide team members on data science and analytics best practices to help them overcome bottlenecks and challenges 7. The role will be an approximate 60% IC – 40% leading and the ratios can vary basis need and fit 8. Develop Customer-360 Features that will be integrated into the Customer Data Platform (CDP) to enhance the single view of our customer Website: https://www.kotak811.com/

Job Description We are seeking a talented Generative AI Developer to join our team and build cutting-edge generative models. The ideal candidate will work on developing AI systems that can create innovative, human-like content, ranging from natural language generation (NLG), image generation, and even video or music synthesis. The candidate requires to carry expertise in machine learning, deep learning, and natural language processing (NLP), as well as an understanding of specific generative models like GPT, GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), or other types of neural networks. Responsibilities Develop and fine-tune generative models using techniques like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, Diffusion models, etc. Work on tasks such as text generation, image generation, music or sound generation, video creation, and other creative AI applications. Design, build, and deploy models using frameworks like TensorFlow, PyTorch, Hugging Face, OpenAI's GPT, or similar tools. Optimize AI models for performance, accuracy, and scalability in real-world production environments. Research and stay updated on the latest trends, papers, and breakthroughs in generative AI. Collaborate with other teams (data scientists, machine learning engineers, product teams) to integrate models into production systems. Work on data preprocessing, augmentation, and designing pipelines to ensure high-quality input for model training. Document your code, process, and model outputs for team-wide visibility and knowledge sharing. Required Qualifications & Skills Bachelor's or Master's degree in Computer Science or related fields. 3-4 years of hands-on experience in developing Generative AI solutions Strong understanding of deep learning algorithms, especially in generative models like GANs, VAEs, Diffusion models, or large-scale language models like GPT. Hands-on experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or Hugging Face. Experience working with neural networks, unsupervised learning, and natural language processing (NLP). Strong programming skills in Python, including deep learning libraries (e.g., TensorFlow, Keras, PyTorch). Familiarity with cloud platforms (AWS, GCP, or Azure) for model training and deployment. Strong mathematical and statistical knowledge, particularly in probability theory, linear algebra, and optimization. Experience in large-scale model training, fine-tuning, or distributed computing. Knowledge of reinforcement learning (RL) and self-supervised learning. Familiarity with AI ethics, bias mitigation, and interpretability in generative models. Show more Show less

Position : Environmental Data Scientist Salary: upto ₹50000 pm Location: Ahmedabad [Only preferring candidates from Gujarat] Experience: 2+ Years we are seeking a research-driven Environmental Data Scientist to lead the development of advanced algorithms that enhance the accuracy, reliability, and performance of air quality sensor data. This role goes beyond traditional data science — it focuses on solving real-world challenges in environmental sensing, such as sensor drift, cross-interference, and data anomalies. Key Responsibilities: Design and implement algorithms to improve the accuracy, stability, and interpretability of air quality sensor data (e.g., calibration, anomaly detection, cross-interference mitigation, and signal correction) Conduct in-depth research on sensor behavior and environmental impact to inform algorithm development Collaborate with software and embedded systems teams to integrate these algorithms into cloud or edge-based systems Analyze large, complex environmental datasets using Python, R, or similar tools Continuously validate algorithm performance using lab and field data; iterate for improvement Develop tools and dashboards to visualize sensor behavior and algorithm impact Assist in environmental research projects with statistical analysis and data interpretation Document algorithm design, testing procedures, and research findings for internal use and knowledge sharing Support team members with data-driven insights and code-level contributions as needed Assist other team members with writing efficient code and overcoming programming challenges Education/Experience Required Skills & Qualifications Bachelor’s or Master’s degree in one of the following fields: Environmental Engineering / Science, Chemical Engineering, Electronics / Instrumentation Engineering, Computer Science / Data Science, Physics / Atmospheric Science (with data or sensing background) 1-2 years of hands-on experience working with sensor data or IoT-based environmental monitoring systems Strong knowledge of algorithm development, signal processing, and statistical analysis Proficiency in Python (pandas, NumPy, scikit-learn, etc.) or R, with experience handling real-world sensor datasets Ability to design and deploy models in a cloud or embedded environment. Excellent problem-solving and communication skills. Passion for environmental sustainability and clean-tech. Preferred Qualifications: Familiarity with time-series anomaly detection, sensor fusion, signal noise reduction techniques or geospatial data processing. Exposure to air quality sensor technologies, environmental sensor datasets, or dispersion modeling. For Quick Response, please fill out this fo rm https://docs.google.com/forms/d/e/1FAIpQLSeBy7r7b48Yrqz4Ap6-2g_O7BuhIjPhcj-5_3ClsRAkYrQtiA/viewform?usp=sharing&ouid=106739769571157586077 Show more Show less

Greetings from Synergy Resource Solutions, a leading Recruitment Consultancy. Our client is a Smart Air Quality Monitoring Solutions company offering data-driven environmental solutions for better decision making. Using our sensor-based hardware, we monitor various environmental parameters related to air quality, noise, odour, weather, radiation etc. Designation: - Environmental Data Scientist (Ahmedabad) Location: - Ahmedabad Experience : - 1 - 3 years Work timings: 10:00 am to 6:30 pm (5 days working) Job Description: We are seeking a research-driven Environmental Data Scientist to lead the development of advanced algorithms that enhance the accuracy, reliability, and performance of air quality sensor data. This role goes beyond traditional data science — it focuses on solving real-world challenges in environmental sensing, such as sensor drift, cross-interference, and data anomalies. Key Responsibilities: ● Design and implement algorithms to improve the accuracy, stability, and interpretability of air quality sensor data (e.g., calibration, anomaly detection, cross-interference mitigation, and signal correction) ● Conduct in-depth research on sensor behavior and environmental impact to inform algorithm development ● Collaborate with software and embedded systems teams to integrate these algorithms into cloud or edge-based systems ● Analyze large, complex environmental datasets using Python, R, or similar tools ● Continuously validate algorithm performance using lab and field data; iterate for improvement ● Develop tools and dashboards to visualize sensor behavior and algorithm impact ● Assist in environmental research projects with statistical analysis and data interpretation ● Document algorithm design, testing procedures, and research findings for internal use and knowledge sharing ● Support team members with data-driven insights and code-level contributions as needed ● Assist other team members with writing efficient code and overcoming programming challenges Required Skills & Qualifications ● Bachelor’s or Master’s degree in one of the following fields: Environmental Engineering / Science, Chemical Engineering, Electronics / Instrumentation Engineering, Computer Science / Data Science, Physics / Atmospheric Science (with data or sensing background) ● 1-2 years of hands-on experience working with sensor data or IoT-based environmental monitoring systems ● Strong knowledge of algorithm development, signal processing, and statistical analysis ● Proficiency in Python (pandas, NumPy, scikit-learn, etc.) or R, with experience handling real-world sensor datasets ● Ability to design and deploy models in a cloud or embedded environment. ● Excellent problem-solving and communication skills. ● Passion for environmental sustainability and clean-tech. Preferred Qualifications: ● Familiarity with time-series anomaly detection, sensor fusion, signal noise reduction techniques or geospatial data processing. ● Exposure to air quality sensor technologies, environmental sensor datasets, or dispersion modeling. Benefits: Competitive salary and benefits package Opportunities for professional growth and development A dynamic and collaborative work environment If your profile is matching with the requirement & if you are interested for this job, please share your updated resume with details of your present salary, expected salary & notice period. Show more Show less

🚀 What we’re building CodeRound AI matches top 5% tech talent to fastest growing VC funded AI startups in Silicon Valley and India. Candidates apply once and get UPTO 20 remote as well as onsite interview opportunities IF selected! T op-tier product startups in US, UAE & India have hired top engineers & ML folk using CodeRound 🧩 What you'll do Design, build, and iterate on LLM-based systems that score freeform answers, generate real-time feedback, and assist. Optimize prompt workflows and build tool-augmented agents that evaluate coding, reasoning, and system design responses. Implement and tune RAG pipelines to personalize interviews, fetch dynamic context, and reduce hallucinations. Define LLM evaluation frameworks — accuracy, consistency, bias, and interpretability. Collaborate with backend and product to ship fast, test with users, and refine in production. Keep up with SOTA papers, models, and tools — and bring them to life in real applications. ✅ You’ll thrive here if you: Have built LLM-based applications in production — not just toy projects. Are strong in Python, familiar with LangChain/LlamaIndex, and have worked with OpenAI, Claude, or open-weight models. Understand LLM failure modes — and how to prompt, chain, or retrieve around them. Think like a product engineer — fast, iterative, user-focused, and not afraid to ship experiments. Are excited about building LLM features that go beyond chat — evaluation, scoring, ranking, summarization. ⚡ Bonus if you: Have experience with vector DBs (Pinecone, Weaviate, etc.), semantic search, and evaluation frameworks. Have worked on AI in edtech, hiring, developer tools, or assessment. Have finetuned models or built systems around LLM agents + tools. Want to grow into a Founding AI Lead role. ✨ Why CodeRound? You’ll own mission-critical LLM systems used by top startups You’ll work with a fast team of engineers and founders who’ve built and scaled startups before. You’ll ship fast, see real user feedback, and influence product + infra decisions from Day 1. Show more Show less