Key Responsibilities Build and fine-tune Generative AI models (LLMs, diffusion models, etc.) for various applications. Work with agent and multi-agent frameworks to build task-specific or collaborative AI systems. Develop and deploy ML pipelines for training, inference, and evaluation. Collaborate with cross-functional teams (Product, Data Engineering, DevOps) to integrate ML models into products. Conduct data preprocessing, exploratory analysis, and feature engineering. Stay updated with state-of-the-art research in ML/GenAI and apply it to practical problems. Optimize models for performance, scalability, and efficiency. Work with APIs like OpenAI, AZURE OpenAI, and others for rapid prototyping and deployment. Contribute to internal tools and frameworks to support ML experimentation and monitoring. ________________________________________ Required Skills & Qualifications Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or related field. 3 to 5 years of hands-on experience in Machine Learning and/or NLP projects. Proficiency in Python and popular ML libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers). Practical experience with agent and/or multi-agent frameworks (e.g., LangGraph, CrewAI, AutoGen, AutoGPT, BabyAGI, etc.) is highly desirable. Experience working with LLMs (GPT, Claude, etc.). Familiarity with prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning techniques. Strong understanding of data structures, algorithms, and ML concepts. Experience in deploying models using tools like Docker, FastAPI, Flask, or MLflow. Knowledge of cloud platforms (AWS, GCP, or Azure) is a plus. Experience with vector databases (e.g., PG Vector, Pinecone, Weaviate). Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Airflow). Publications or contributions to open-source projects in ML/GenAI. Familiarity with ethical AI principles and responsible AI practices Show more Show less

SUMMARY We are hiring an ML Analyst / Project Technical Consultant for our Anthropometry Project funded by ICMR. They will be a part of one or more interdisciplinary solution teams. These teams consist of members from Machine Learning, Engineering, Solutions, and Programs to build artificial intelligence-based solutions addressing specific problems. ABOUT US Wadhwani AI is a nonprofit institute building and deploying applied AI solutions to solve critical issues in public health, agriculture, education, and urban development in underserved communities in the global south. We collaborate with governments, social sector organisations, academic and research institutions, and domain experts to identify real-world problems, and develop practical AI solutions to tackle these issues with the aim of making a substantial positive impact. We have over 30+ AI projects supported by leading philanthropies such as Bill & Melinda Gates Foundation, USAID and Google.org. With a team of over 200 professionals, our expertise encompasses AI/ML research and innovation, software engineering, domain knowledge, design and user research. In the Press: Our Founder Donors are among the Top 100 AI Influencers G20 India’s Presidency: AI Healthcare, Agriculture, & Education Solutions Showcased Globally. Unlocking the potentials of AI in Public Health Wadhwani AI Takes an Impact-First Approach to Applying Artificial Intelligence - data.org Winner of the H&M Foundation Global Change Award 2022 Sole Indian Winners of the 2019 Google AI Impact Challenge, and the first in the Asia Pacific to host Google Fellows Cultures page of Wadhwani AI - https://www.wadhwaniai.org/culture/ PRE-REQUISITES ML Analyst position is open to all with prior training in Engineering or any Quantitative Sciences discipline. No prior ML experience is required but a strong quantitative aptitude, intelligence, eagerness to learn, working smart and hard, are required ROLES & RESPONSIBILITIES Expected to work closely with data leading to developing ML solutions Create, prepare, and curate datasets, apply simple and straightforward ML models, visualize, analyze, and monitor the results Support training and inference of advanced ML and Deep Learning models, with guidance from Associate Machine Learning Scientist Contribute to the overall ML life-cycle working closely with data, models, and customers QUALIFICATIONS B.Tech./B.E./B.S. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics or a relevant quantitative field. Duration - Till 14th September 2025 (extendable for another 12 months based on performance and project objectives) For a detailed job description, please refer to this link : Detailed Job Description We are committed to promoting diversity and the principle of equal employment opportunity for all our employees and encourage qualified candidates to apply irrespective of religion or belief, ethnic or social background, gender, gender identity, and disability. If you have any questions, please email us at careers@wadhwaniai.org.

Excellent Python programming and debugging skills. (Refer to Pytho JD given below) - Proficiency with SQL, relational databases, & non-relational databases - Passion for API design and software architecture. - Strong communication skills and the ability to naturally explain difficult technical topics to everyone from data scientists to engineers to business partners - Experience with modern neural-network architectures and deep learning libraries (Keras, TensorFlow, PyTorch). - Experience unsupervised ML algorithms. - Experience in Timeseries models and Anomaly detection problems. - Experience with modern large language model (Chat GPT/BERT) and applications. - Expertise with performance optimization. - Experience or knowledge in public cloud AWS services - S3, Lambda. - Familiarity with distributed databases, such as Snowflake, Oracle. - Experience with containerization and orchestration technologies, such as Docker and Kubernetes. Managing large machine learning applications and designing and implementing new frameworks to build scalable and efficient data processing workflows and machine learning pipelines. - Build the tightly integrated pipeline that optimizes and compiles models and then orchestrates their execution. - Collaborate with CPU, GPU, and Neural Engine hardware backends to push inference performance and efficiency - Work closely with feature teams to facilitate and debug the integration of increasingly sophisticated models, including large language models - Automate data processing and extraction - Engage with sales team to find opportunities, understand requirements, and translate those requirements into technical solutions. - Develop reusable ML models and assets into production. Show more Show less

AI Intern – Model Fine-Tuning, Video Intelligence, and Multilingual Media Tech Location: Remot e / India Duration: 3–6 Months Start: Immediate About Us We are a fast-moving tech team building cutting-edge solutions at the intersection of AI, video intelligence, and multilingual media. We’re now looking for AI Interns who are excited about working hands-on with the latest open-source models and taking them from prototype to production. This is your chance to build real-world, production-grade AI systems—not just toy models. If you love experimenting, optimizing, and seeing your work go live, you’ll love working with us. What You’ll Work On As an intern, you’ll help us build intelligent pipelines that: 🎬 Convert long-form videos into engaging short reels 📝 Auto-generate subtitles from video/audio 🌐 Translate video content into multiple languages (text + speech) 🗣️ Add dubbed audio with emotional tone matching across language s To achieve this, you’ll : Fine-tune and evaluate open-source AI models (Hugging Face, etc. ) Work with speech-to-text (STT), text-to-text (translation), and text-to-speech (TTS) system s Optimize models for real-time inference using tools like ONNX, quantization, etc . Collaborate closely with engineers to integrate AI into full-stack pipeline s Who We’re Looking Fo rStrong programming skills in C (essential for performance-critical modules ) Working knowledge of Python, PyTorch, Transformers, or similar framework s Basic understanding of model training, fine-tuning, and inference workflow s Hunger to learn, build, and solve open-ended problem s (Bonus) Familiarity with video/audio libraries (FFmpeg, OpenCV), Docker, or REST API s Why Join U s✅ Work on impactful, real-world AI project s✅ Get hands-on mentorship in applied AI & production engineerin g✅ See your work ship and make a differenc e✅ Flexible, remote-friendly work setu p Ready to apply ?Send your resume, GitHub/portfolio (if any), and a short note about why this excites you to hr@mantechventures.com . Show more Show less

We are looking for an experienced MLOPs Engineer with expertise in Spark/PySpark, MLOps/LLMops/DLOps, CI/CD, Kafka, Python, distributed computing, GitHub, data pipelines, cloud hosting, Azure services, Microsoft services, various data connectors, and more. This role will involve designing, implementing, and optimizing data science pipelines, deploying machine learning models, and ensuring smooth operation in production environments. Responsibilities Design, develop, and maintain data science pipelines for model training, evaluation, and deployment. Manage and optimize infrastructure resources (e.g., cloud services, containers) to support model deployment and inference Collaborate with data scientists, software engineers, and DevOps teams to deploy machine learning models using best practices in MLOps. Automate end-to-end ML workflows, including data preprocessing, model training, evaluation, and deployment, using tools like Kubeflow or Apache Airflow Implement CI/CD pipelines for automated model deployment, testing, and monitoring. Utilize Kafka and other messaging systems for real-time data processing and streaming analytics. Optimize distributed computing infrastructure for scalability, performance, and cost efficiency. Manage GitHub repositories for version control and collaboration on machine learning projects. Utilize various data connectors and integration tools to access and process data from different sources. Develop and maintain documentation for data science pipelines, infrastructure, and processes. Stay up to date on emerging technologies and best practices in machine learning operations and data engineering. Qualifications 5+ Years of prior experience in Data Engineering and MLOPs. 3+ Years of strong exposure in deploying and managing data science pipelines in production environments. Strong proficiency in Python programming language. Experience with Spark/PySpark and distributed computing frameworks. Hands-on experience with CI/CD pipelines and automation tools. Exposure in deploying a use case in production leveraging Generative AI involving prompt engineering and RAG Framework Familiarity with Kafka or similar messaging systems. Strong problem-solving skills and the ability to iterate and experiment to optimize AI model behavior. Excellent problem-solving skills and attention to detail. Ability to communicate effectively with diverse clients/stakeholders. Education Background Bachelor’s or master’s degree in computer science, Engineering, or a related field. Tier I/II candidates preferred. Show more Show less

Description: Graviton is a privately funded quantitative trading firm striving for excellence in financial markets' research. We are seeking a Quantitative Researcher for our team in Gurgaon. This team trades across a multitude of asset classes and trading venues using a gamut of concepts and techniques ranging from time series analysis, filtering, classification, stochastic models, pattern recognition to statistical inference analysing terabytes of data to come up with ideas to identify pricing anomalies in financial markets. Responsibilities: Develop new or improve existing trading models using in-house platforms Use advanced mathematical techniques to model and predict market movements Analyse large financial datasets to identify trading opportunities Provide real time analytical support to experienced traders Requirements: Possess a degree in a highly analytical field, such as Engineering, Mathematics, Computer Science from IITs schools Quantitative bent of mind A working knowledge of Linux/Unix Programming experience, preferably in C++ or C No prior knowledge of financial markets is needed but must have a strong interest in learning about financial markets. Have a strong work ethic Hard Working Benefits: Our open and collaborative work culture gives you the freedom to innovate and experiment. Our cubicle free offices, non-hierarchical work culture and insistence to hire the very best creates a melting pot for great ideas and technological innovations. Everyone on the team is approachable, there is nothing better than working with friends! Our perks have you covered. Competitive compensation Annual international team outing Fully covered commuting expenses Best-in-class health insurance Delightful catered breakfasts and lunches A well-stocked kitchen 4 week annual leaves along with market holidays Gym and sports club memberships Regular social events and clubs After work parties Show more Show less

Graviton is a privately funded quantitative trading firm striving for excellence in financial markets' research. We are seeking a Network Engineer Trainee for our team in Gandhinagar . Graviton trades across a multitude of asset classes and trading venues using a gamut of concepts and techniques ranging from time series analysis, filtering, classification, stochastic models, pattern recognition to statistical inference, analyzing terabytes of data to identify pricing anomalies in financial markets. Responsibilities Install, configure, and troubleshoot Linux, Windows and macOS operating systems. Provide technical support for desktops, laptops, printers, and other peripherals. Assist users with software installations and updates. Troubleshoot network connectivity issues (LAN, Wi-Fi, VPN, DHCP, DNS, etc.). Handle basic hardware replacements (RAM, HDD, SSD, power supply, etc.). Install and configure antivirus and security updates. Respond to IT support requests via phone, email, or ticketing system. Perform routine system maintenance and software updates. Document troubleshooting steps and solutions. Assist in troubleshooting basic desktop and network connectivity issues. Help with hardware and software installations, system upgrades, and configurations. Support troubleshooting and analyzing basic network performance issues (Wi-Fi, LAN). Assist in the setup and maintenance of office networks under supervision. Document troubleshooting steps and standard operating procedures. Collaborate with teams to ensure smooth IT operations and user support. Qualifications The ideal candidate will have 0-1 years of experience and basic knowledge of: Networking concepts, including LAN, WAN, and TCP/IP fundamentals. Basic understanding of routing and switching concepts. Exposure to desktop/laptop hardware, operating systems, and software troubleshooting. Hands-on experience with Linux/Unix environments (a plus). CCNA certification or relevant coursework (a plus). Strong problem-solving skills and eagerness to learn in a fast-paced environment. Benefits: Our open and casual work culture gives you the space to innovate and deliver. Our cubicle free offices , disdain for bureaucracy and insistence to hire the very best creates a melting pot for great ideas and technology innovations. Everyone on the team is approachable, there is nothing better than working with friends! Our perks have you covered. Competitive compensation 6 Weeks of paid vacation Regular after work parties Travel Credits Catered in office meals Show more Show less

This role is for one of our clients Industry: Technology, Information and Media Seniority level: Associate level Min Experience: 2 years Location: India JobType: full-time About The Role We are looking for a proactive and skilled AWS Developer to join our dynamic team focused on cloud infrastructure and AI-driven solutions. In this role, you will architect, deploy, and maintain scalable and secure cloud environments on AWS, supporting the development and operationalization of machine learning models and AI applications. You will collaborate closely with data scientists, developers, and DevOps teams to ensure seamless integration and robust performance of AI workloads in the cloud. What You’ll Do Architect and build highly available, fault-tolerant, and scalable AWS infrastructure tailored for AI and machine learning workloads. Deploy, manage, and monitor AI/ML models in production using AWS services such as SageMaker, Lambda, EC2, ECS, and EKS. Partner with AI and ML teams to translate model requirements into effective cloud architectures and operational workflows. Automate infrastructure deployment and management through Infrastructure as Code (IaC) using Terraform, CloudFormation, or similar tools. Implement and optimize CI/CD pipelines to streamline model training, validation, and deployment processes. Monitor cloud environments and AI workloads proactively to identify and resolve performance bottlenecks or security vulnerabilities. Enforce best practices for data security, compliance, and governance in handling AI datasets and inference endpoints. Stay updated with AWS advancements and emerging tools to continuously enhance AI infrastructure capabilities. Support troubleshooting efforts, perform root cause analysis, and document solutions to maintain high system reliability. Who You Are 2+ years of hands-on experience working with AWS cloud services, especially in deploying and managing AI/ML workloads. Strong knowledge of AWS core services including S3, EC2, Lambda, SageMaker, IAM, CloudWatch, ECR, ECS, EKS, and CloudFormation. Experience deploying machine learning models into production environments and maintaining their lifecycle. Proficient in scripting and programming languages such as Python, Bash, or Node.js for automation and orchestration tasks. Skilled with containerization and orchestration tools such as Docker and Kubernetes (EKS). Familiar with monitoring and alerting solutions like AWS CloudWatch, Prometheus, or Grafana. Understanding of CI/CD methodologies and tools like Jenkins, GitHub Actions, or AWS CodePipeline. Bachelor’s degree in Computer Science, Engineering, or a related technical discipline. Bonus Points For AWS certifications such as AWS Certified Machine Learning – Specialty or AWS Solutions Architect. Hands-on experience with MLOps frameworks (Kubeflow, MLflow) and model version control. Familiarity with big data processing tools like Apache Spark, AWS Glue, or Redshift. Experience working in Agile or Scrum-based development environments. Show more Show less

Job Description Our company stands at the crossroads of healthcare as a leading entity, armed with a broad continuum of medicines, vaccines, and animal health products. But our ambition reaches beyond today; we’re constructing a progressive healthcare company prepared to foster a healthier tomorrow for all. The secret to our continued success? That’s where you come in. We believe that your integrity, knowledge, innovation, skill and diversity, together with an unwavering commitment to teamwork, are the engine of our excellence. We’re devoted to nurturing a work environment defined by mutual respect, encouragement, and collaboration. As part of our global ensemble, you’ll be given the chance to work with talented individuals while enhancing and expanding your own career. In the Data Science team in HR, your role is crucial. You’ll translate business needs into data-driven research projects and products, shedding light on our company’s talent. Your role blends data science with social science, unearthing workforce trends and enabling us to conduct scalable, reproducible research. Ready to be a part of this journey? Join us to catalyze a healthier, brighter future. Responsibilities Design, develop, enhance, and implement models that delve deep into our workforce data Boost AI/ML capability within the team, using the latest methods and tools to extract insights from text, activity, behavioral, and network data Develop and deploy solutions that meet defined and repeatable needs Work hand-in-hand with our client-facing teams to address business needs, providing solutions that are as insightful as they are actionable Collaborate with data scientists, data engineers, IT, Data Science community, and HR Operations to amplify data science capabilities and drive innovation Act as an AI/ML expert, advising HR colleagues and end users on the best usage within the HR domain Qualifications Required Minimum of 6 years of experience in data science or machine learning engineering role with a Bachelor’s degree from an accredited institution in Computer Science, Data Science, Machine Learning, Statistics or other related field (with Masters' degree, minimum experience is 4 years) 2 years of Natural Language Processing (NLP) Experience Expertise using Python, R, and SQL to execute a solid portfolio of data science projects involving statistical inference, classical machine learning and deep learning frameworks Solid understanding of NLP tools, methods, and pipeline design including experience using large language models Proven leadership in team project settings Experience with cloud computing platforms, such as AWS and Databricks, and related tools Familiarity with version control systems Openness to coaching and learning from team members with different specializations Exceptional initiative, curiosity, communication skills, and a team-first orientation Demonstrated interest in projects focused on the workforce Preferred MLOps experience is a big plus, and LLM app deployment experience is ideal Ability to manage relationships, influence non-technical stakeholders, and tell a great data story Understanding of HR data, processes, information systems, and governance Ability to conduct literature reviews and leverage external research to stay on top of best practices in AI/ML and data science in human capital management Our Offer (The primary location is Czechia, benefits in other countries may vary) Exciting work in a great team, global projects, international environment Opportunity to learn and grow professionally within the company globally Hybrid working model, flexible role pattern Pension and health insurance contributions Internal reward system plus referral programme 5 weeks annual leave, 5 sick days, 15 days of certified sick leave paid above statutory requirements annually, 40 paid hours annually for volunteering activities, 12 weeks of parental contribution Cafeteria for tax free benefits according to your choice (meal vouchers, Lítačka, sport, culture, health, travel, etc.), Multisport Card Vodafone, Raiffeisen Bank, Foodora, and Mall.cz discount programmes Up-to-date laptop and iPhone Parking in the garage, showers, refreshments, library, music corner Competitive salary, incentive pay, and many more Ready to take up the challenge? Apply now! Know anybody who might be interested? Refer this job! Current Employees apply HERE Current Contingent Workers apply HERE Search Firm Representatives Please Read Carefully Merck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails. Employee Status Regular Relocation VISA Sponsorship Travel Requirements Flexible Work Arrangements Hybrid Shift Valid Driving License Hazardous Material(s) Required Skills Business Intelligence (BI), Database Design, Data Engineering, Data Modeling, Data Science, Data Visualization, Machine Learning, Software Development, Stakeholder Relationship Management, Waterfall Model Preferred Skills Job Posting End Date 06/6/2025 A job posting is effective until 11 59 59PM on the day BEFORE the listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date. Requisition ID R346320 Show more Show less

Overview: We are looking for a hands-on, full-cycle AI/ML Engineer who will play a central role in developing a cutting-edge AI agent platform. This platform is designed to automate and optimize complex workflows by leveraging large language models (LLMs), retrieval-augmented generation (RAG), knowledge graphs, and agent orchestration frameworks. As the AI/ML Engineer, you will be responsible for building intelligent agents from the ground up — including prompt design, retrieval pipelines, fine-tuning models, and deploying them in a secure, scalable cloud environment. You’ll also implement caching strategies, handle backend integration, and prototype user interfaces for internal and client testing. This role requires deep technical skills, autonomy, and a passion for bringing applied AI solutions into real-world use. Key Responsibilities: Design and implement modular AI agents using large language models (LLMs) to automate and optimize a variety of complex workflows Deploy and maintain end-to-end agent/AI workflows and services in cloud environments, ensuring reliability, scalability, and low-latency performance for production use Build and orchestrate multi-agent systems using frameworks like LangGraph or CrewAI, supporting context-aware, multi-step reasoning and task execution Develop and optimize retrieval-augmented generation (RAG) pipelines using vector databases (e.g., Qdrant, Pinecone, FAISS) to power semantic search and intelligent document workflows Fine-tune LLMs using frameworks such as Hugging Face Transformers, LoRA/PEFT, DeepSpeed, or Accelerate to create domain-adapted models Integrate knowledge graphs (e.g., Neo4j, AWS Neptune) into agent pipelines for context enhancement, reasoning, and relationship modeling Implement cache-augmented generation strategies using semantic caching and tools like Redis or vector similarity to reduce latency and improve consistency Build scalable backend services using FastAPI or Flask and develop lightweight user interfaces or prototypes with tools like Streamlit, Gradio, or React Monitor and evaluate model and agent performance using prompt testing, feedback loops, observability tools, and safe AI practices Collaborate with architects, product managers, and other developers to translate problem statements into scalable, reliable, and explainable AI systems Stay updated on the latest in cloud platforms (AWS/GCP/Azure), software frameworks, agentic frameworks, and AI/ML technologies Prerequisites: Strong Python development skills, including API development and service integration Experience with LLM APIs (OpenAI, Anthropic, Hugging Face), agent frameworks (LangChain, LangGraph, CrewAI), and prompt engineering Experience deploying AI-powered applications using Docker, cloud infrastructure (Azure preferred), and managing inference endpoints, vector DBs, and knowledge graph integrations in a live production setting Proven experience with RAG pipelines and vector databases (Qdrant, Pinecone, FAISS) Hands-on experience fine-tuning LLMs using PyTorch, Hugging Face Transformers, and optionally TensorFlow, with knowledge of LoRA, PEFT, or distributed training tools like DeepSpeed Familiarity with knowledge graphs and graph databases such as Neo4j or AWS Neptune, including schema design and Cypher/Gremlin querying Basic frontend prototyping skills using Streamlit or Gradio, and ability to work with frontend teams if needed Working knowledge of MLOps practices (e.g., MLflow, Weights & Biases), containerization (Docker), Git, and CI/CD workflows Cloud deployment experience with Azure, AWS, or GCP environments Understanding of caching strategies, embedding-based similarity, and response optimization through semantic caching Preferred Qualifications: Bachelor’s degree in Technology (B.Tech) or Master of Computer Applications (MCA) is required; MS in similar field preferred 7–10 years of experience in AI/ML, with at least 2 years focused on large language models, applied NLP, or agent-based systems Demonstrated ability to build and ship real-world AI-powered applications or platforms, preferably involving agents or LLM-centric workflows Strong analytical, problem-solving, and communication skills Ability to work independently in a fast-moving, collaborative, and cross-functional environment Prior experience in startups, innovation labs, or consulting firms a plus Compensation: The compensation structurewill be discussed during the interview Show more Show less

Job Title: Senior Python Developer Company: Darwix AI Location: Gurgaon (On-site) Type: Full-Time Experience: 3–8 years About Darwix AI Darwix AI is one of India’s fastest-growing AI startups, transforming enterprise sales with our GenAI-powered conversational intelligence and real-time agent assist suite. Our platform is used by high-growth enterprises across India, MENA, and Southeast Asia to improve sales productivity, personalize customer conversations, and unlock revenue intelligence in real-time. We are backed by marquee VCs, 30+ angel investors, and led by alumni from IITs, IIMs, and BITS with deep experience in building and scaling products from India for the world. Role Overview As a Senior Python Developer at Darwix AI, you will be at the core of our engineering team, leading the development of scalable, secure, and high-performance backend systems that support AI workflows, real-time data processing, and enterprise-grade integrations. This role requires deep technical expertise in Python, a strong foundation in backend architecture, and the ability to collaborate closely with AI, product, and infrastructure teams. You will take ownership of critical backend modules and shape the engineering culture in a rapidly evolving, high-impact environment. Key Responsibilities System Architecture & API Development Design, implement, and optimize backend services and microservices using Python frameworks such as FastAPI, Django, or Flask Lead the development of scalable RESTful APIs that integrate with frontend, mobile, and AI systems Architect low-latency, fault-tolerant services supporting real-time sales analytics and AI inference Data Pipelines & Integrations Build and optimize ETL pipelines to manage structured and unstructured data from internal and third-party sources Integrate APIs with CRMs, telephony systems, transcription engines, and enterprise platforms like Salesforce, Zoho, and LeadSquared Lead scraping and data ingestion efforts from large-scale, dynamic web sources using Playwright, BeautifulSoup, or Scrapy AI/ML Enablement Work closely with AI engineers to build infrastructure for LLM/RAG pipelines , vector DBs , and real-time AI decisioning Implement backend support for prompt orchestration , Langchain flows , and function-calling interfaces Support model deployment, inference APIs, and logging/monitoring for large-scale GenAI pipelines Database & Storage Design Optimize database design and queries using MySQL , PostgreSQL , and MongoDB Architect and manage Redis and Kafka for caching, queueing, and real-time communication DevOps & Quality Ensure continuous delivery through version control (Git), CI/CD pipelines, testing frameworks, and Docker-based deployments Identify and resolve bottlenecks related to performance, memory, or data throughput Adhere to best practices in code quality, testing, security, and documentation Leadership & Collaboration Mentor junior developers and participate in code reviews Collaborate cross-functionally with product, AI, design, and sales engineering teams Contribute to architectural decisions, roadmap planning, and scaling strategies Qualifications 4–8 years of backend development experience in Python, with a deep understanding of object-oriented and functional programming Hands-on experience with FastAPI , Django , or Flask in production environments Proven experience building scalable microservices, data pipelines, and backend systems that support live applications Strong command over REST API architecture , database optimization, and data modeling Solid experience working with web scraping tools , automation frameworks, and external API integrations Knowledge of AI tools like Langchain , HuggingFace , Vector DBs (Pinecone, Weaviate, FAISS) , or RAG architectures is a strong plus Familiarity with cloud infrastructure (AWS/GCP) , Docker, and containerized deployments Comfortable working in fast-paced, high-ownership environments with shifting priorities and dynamic problem-solving Show more Show less

Job Title: AI/ML Intern (Paid Internship) Location: Bengaluru, India Number of Openings: 10 Duration: 6 Months Company Overview: IAI Solutions ( https://www.iaisolution.com/ ) is a leading innovator in artificial intelligence and machine learning, delivering next-generation solutions that impact industries globally. Our mission is to drive AI advancement through cutting-edge research, enterprise-ready AI systems, and scalable deployments. We’re currently seeking passionate and skilled AI/ML Interns who are eager to contribute to real-world AI development, have hands-on experience with model fine-tuning, and are adept in prompt engineering. If you're ready to work on impactful projects and expand your expertise in AI, we’d love to hear from you. Position Summary: As an AI/ML Intern , you will work with state-of-the-art AI technologies to design, build, and deploy intelligent systems. The ideal candidate will have strong proficiency in Python , Flask or Fast API and OOP , with real-world experience in prompt engineering , Finetuning and Hugging Face , LangChain. Key Responsibilities: Apply prompt engineering techniques in different LLM applications Work with LangChain , Langgraph , Hugging Face , OpenAI , and similar frameworks for model inference Develop tools and apply tool calling techniques to LLM, get structured response from LLMs using Pydantic models. Utilize object-oriented programming for scalable and reusable code design Contribute to internal documentation, share findings and best practices Keep up-to-date with the latest advancements in AI and integrate them into existing system Qualifications: Strong experience in Python Practical knowledge in prompt engineering and LLM optimization Proficiency with Langchain, Hugging Face Transformers , datasets , Background in AI/ML research or academic projects Solid grasp of OOP principles and strong problem-solving abilities Ability to work independently and within a collaborative team Strong communication and documentation skills Preferred Qualifications: Previous experience in academic or industrial research in LLM. Proven track record of successful AI LLM model deployments and optimizations. Perks & Benefits: Hands-on experience with real-world AI deployments Work with state-of-the-art AI tools and platforms Mentorship from top AI engineers and researchers Flexible, innovation-driven work culture Stipend: ₹25,000/month After completion of the internship period, there is an opportunity to get a full-time opportunity as an AI/ML Engineer (Up to 15 LPA) Selection Process: Step 1: Application Submission Visit our Career Page Fill in the required details and upload your resume Step 2: Task Assignment (48 Hours Deadline) Download the AI/ML Assignment from the Career Page Complete and submit within 48 hours Record and attach a 2-minute video explaining your solution (mandatory) Step 3: Review & Shortlisting Our technical team will evaluate your assignment submission Candidates with outstanding performance will be shortlisted for interviews Step 4: Interview Showcase your technical and problem-solving abilities Highlight your prior work, research, and understanding of AI/ML principles Step 5: Onboarding Post a successful interview, we’ll welcome you aboard! Get ready to dive into exciting projects and contribute to the field of AI. Compensation: Stipend: ₹25,000/month Full-Time Opportunity: Up to ₹15 LPA (based on performance) You can also apply directly via our Career Portal Ensure submission includes: Updated Resume Project Portfolio or GitHub (if available) Completed Assignment + Video explanation of 2 minutes Show more Show less

Job Title: Senior Python Generative AI Engineer Location: 100% Remote Duration: 6-Month Contract Experience Required: 6+ Years Required Qualifications: 6+ years of professional experience in software engineering with a strong focus on Python 2+ years of hands-on experience working with Generative AI / LLMs (Large Language Models) Deep understanding of transformer-based architectures, tokenization, embeddings, and fine-tuning techniques Experience building and deploying RAG (Retrieval-Augmented Generation) pipelines Proficient with prompt engineering, few-shot prompting, and API-based integration (OpenAI, Anthropic, etc.) Strong knowledge of LangChain, LLM orchestration tools, and vector databases like FAISS, Pinecone, or Chroma Experience deploying models using Docker, FastAPI, or serverless platforms; familiarity with Kubernetes is a plus Skilled in using Hugging Face Transformers, Diffusers, and other open-source LLM libraries Hands-on experience with cloud services (AWS/GCP/Azure) for hosting models or serverless inference Familiar with PowerShell, Bash, and infrastructure-as-code tools (Terraform, ARM, or similar) Solid understanding of CI/CD pipelines, model testing, and performance optimization techniques Show more Show less

Location: Hyderabad Role: Permanent Mode: WFO JOB RESPONSIBILITIES: Tracks the various Machine learning projects and their data needs. Tracks and improves Kanban process of product maintenance Drives complex technical discussions both within company and outside data partners Actively Contributes to the design of machine learning solutions by having a deep understanding of how the data is used and how new sources of data can be introduced Advocates for investments in tools and technologies to streamline data workflows and reduce technical debt Continuously explores and adopts emerging technologies and methodologies in data engineering and machine learning Develops and maintains scalable data pipelines to support machine learning models and analytics Collaborates with data scientists to ensure efficient data processing and model deployment Ensures data quality, integrity, and security across all stages of the data pipeline Implements monitoring and alerting systems to detect anomalies in data processing and model performance Enhances data versioning, data lineage, and reproducibility practices to improve model transparency and auditing . QUALIFICATION 5+ years of experience in data engineering or related fields, with a strong focus on building scalable data pipelines to support machine learning workflows. Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or other relevant fields. Specific experience in Kafka needed . Snowflake and data bricks would be huge plus. Proven expertise in designing, implementing, and maintaining large-scale, high-performance data architectures and ETL processes managing 1TB a day. Strong knowledge of database management systems (SQL and NoSQL), distributed data processing (e.g., Hadoop, Spark), and cloud platforms (AWS, GCP, Azure). Experience working closely with data scientists and machine learning engineers to optimize data flows for model training and real-time inference with latency requirements. Hands-on experience with data wrangling, data preprocessing, and feature engineering to ensure clean, high-quality data for machine learning models. Solid understanding of data governance, security protocols, and compliance requirements (e.g., GDPR, HIPAA) to ensure data privacy and integrity. Preferred Experience in data pipelines and analytics for video-game development Experience in Advertising industry Experience in online businesses where transactions happen without human intervention. Show more Show less

interface.ai is the industry's-leading specialized AI provider for banks and credit unions, serving over 100 financial institutions. The company's integrated AI platform offers a unified banking experience through voice, chat, and employee-assisting solutions, enhanced by cutting-edge proprietary Generative AI. Our mission is clear: to transform the banking experience so every consumer enjoys hyper-personalized, secure, and seamless interactions, while improving operational efficiencies and driving revenue growth. interface.ai offers pre-trained, domain-specific AI solutions that are easy to integrate, scale, and manage, both in-branch and online. Combining this with deep industry expertise, interface.ai is the AI solution for banks and credit unions that want to deliver exceptional experiences and stay at the forefront of AI innovation. About Interface.ai interface.ai is the most advanced AI platform for financial institutions. We serve over 100 credit unions and community banks, enabling millions of intelligent conversations every day through voice, chat, and internal copilots. As a fast-growing, AI-native company, data is at the heart of how we build, measure, and scale our products. From intelligent conversation design to customer automation analytics, we apply machine learning and statistical modeling to deliver real-time, measurable outcomes. About The Role We are seeking a Senior Data Scientist to lead the development of scalable, production-grade models and analytics systems that power core platform that our Products run on This is a high-impact role where you'll work on problems at the intersection of language understanding, user behavior prediction, decision optimization, and platform-level intelligence . You will be embedded in product-driven teams, while also collaborating with infrastructure and research to shape the future of intelligence at interface.ai. Key Responsibilities Develop and deploy machine learning models for use cases like intent recognition, conversation scoring, outcome prediction, and next-best-action systems Design and run A/B and multivariate experiments to validate hypotheses and measure product impact Build real-time and batch inference pipelines in collaboration with engineering Define, instrument, and maintain data pipelines for user interaction modeling, longitudinal engagement, and behavioral segmentation Develop intelligence layers for customer-facing analytics products (e.g., AI explainability, task attribution, feature impact modeling) Partner with product managers, engineers, and UX teams to define data-informed product features Translate complex model outcomes into actionable insights for internal and external stakeholders Stay current with research and best practices in Voice models, decision modeling, time-series analysis, and agentic AI architectures What Success Looks Like Within your first 6–12 months, you will: Launch production-grade models that are actively used in product features or operations workflows Define and validate key behavioral or predictive models that influence roadmap direction Improve accuracy, performance, or interpretability of existing AI systems across voice and chat products Drive measurable lift in engagement, resolution rate, or automation through data-driven product iterations Collaborate across departments to establish trusted experimentation and measurement frameworks Required What You Bring 5+ years of experience in applied data science, including end-to-end model development and deployment Strong knowledge of Python, R, SQL, and experience with ML libraries such and deep learning frameworks. Experience with statistical testing, experiment design, and causal inference Understanding of production ML pipelines and collaboration with data engineering teams Experience with speech models, conversational systems, or classification models in user-facing applications Strong product thinking—able to translate model insights into product impact and roadmap trade-offs Preferred Experience working in B2C environments especially in regulated industries (e.g., financial services, healthcare) Exposure to retrieval-augmented generation (RAG), embedding-based search, or LLM evaluation frameworks Familiarity with tools like Airflow, MLflow, dbt, or feature stores Prior work in chatbots, IVRs, or user feedback systems Why Join Us Data science is central to our product innovation strategy You’ll have a direct, measurable impact on customer outcomes and platform intelligence You’ll work on real-world AI applications with scaled deployment and product visibility You’ll collaborate with a cross-disciplinary team of engineers, designers, and product leaders moving at startup speed At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each person's unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible. 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

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