AI Engineer, AI Research Scientist, Applied Scientist

Job Title:

Remote Work:

Duration:

Job 1 Requirement (AI Engineer)

Education

• PhD degree in Computer Science, Applied Mathematics, or a related technical field
• Academic or applied focus on AI, deep learning, or intelligent systems is preferred

Experience

• 3–5 years of hands-on experience building and deploying AI solutions
• 2-3 years of experience in one or more domains: language model, computer vision, signal processing, generative AI, optimization programming, recommendation systems, or autonomous agents
• 1+ year of experience in agentic AI and/or AI reasoning, Digital Twins, Decision/Prescriptive AI
• 4+ years of experience designing, developing, and deploying AI solutions in production environments
• Experience with production-grade Python and/or C/C++
• Hands-on experience with large-scale software architecture, APIs, and model versioning systems

Skills

• Strong software engineering background and experience in production-grade AI delivery systems
• Proficient in at least one skill in C++/CUDA, Python/PySpark, Java/Scala
• Good experience in cloud-native AI tools (Azure, AWS, GCP), DL/LLM/ML frameworks (LangChain, LangGraph, TensorFlow, PyTorch, OpenCV, Hugging Face), and AIOps platforms
• Strong in GPU based accelerating computing technologies (CUDA, Rapids, NeMo, NIM, etc.)
• Strong in Graph Theory or Knowledge Graph related architecture and database (e.g. Neo4j, cuGraph)
• Proficiency in model evaluation, distributed training, and hyperparameter optimization
• Proficient in Big Data Theory based large scale data streaming and in-memory database technologies (Spark, Kafka, Redis, Elastic Search)
• Strong in automated workflow technologies (GitHub Actions, Terraform, Helmet) and containerization technologies (Docker, Kubernetes)
• Proficiency in model evaluation, distributed training, and hyperparameter optimization
• Get familiar with AI/ML lifecycle, model architectures (including deep reinforcement learning, LLMs, RAG, vector search, MoE, foundation models), and structured/unstructured data pipelines
• Effective communicator who can explain complex technical ideas to technical and business audiences
• Ability to work independently in fast-paced, cross-functional environments

Preferred Skills

• Experience in regulated industries (e.g., finance, healthcare, insurance)
• Excellent communication and stakeholder engagement skills
• Strong understanding of deep learning architectures (e.g. CNNs, RNNs, Transformers, GANs)
• Solid in AI/ML algorithms including Neural Network, Transformers, Diffusions, Generative Modeling, Bayesian Inference, Reinforcement Learning, BERT/CLIP
• Proficient in API, MCP and Microservices technologies
• Track records in large-scale, real-time AI/GenAI/AgenticAI/ML database and solution technologies
• Background in responsible AI/ML, model interpretability, and fairness auditing

Job 2 Requirement (AI Research Scientist)

Education

• PhD in Computer Science, Applied Mathematics, Engineering, or related field with AI/Optimization focus
• Proven experience in applied AI Research with deployment of AI solutions in real-world settings

Experience

• 3–5 years of hands-on experience developing AI/ML algorithms and solution frameworks
• 2-3 years of experience in one or more domains: language model, computer vision, signal processing, generative AI, optimization programming, recommendation systems, or autonomous agents
• 1+ year of experience in agentic AI and/or AI reasoning, Digital Twins, Decision/Prescriptive AI
• 3-5 years of programming in Python and C++. CUDA is a plus

Skills

• Strong foundation in mathematics: linear algebra, probability, stochastics, optimization theory
• Expertise in mathematical programming, algorithm design, and optimization techniques
• Skilled in formulating complex problems and designing scalable algorithms (e.g. linear/non-linear programming, convex optimization, combinatorial algorithms, etc.) and experience improving algorithms for efficiency and scalability
• Deep knowledge of machine learning, deep learning, and statistical modeling
• Strong in Graph Theory or Knowledge Graph related architecture and database (e.g. Neo4j, cuGraph)
• Hands-on experience with neural networks, transformers, diffusion models, or generative modeling
• Familiarity with NLP, computer vision, or domain-specific AI applications
• Proficient in model evaluation, validation, and performance metrics
• Experience with AI/ML frameworks and libraries (e.g. TensorFlow, PyTorch)
• Familiarity with software development practices (version control, testing, GPU accelerated computing)
• Strong analytical thinking and problem-solving skills
• Ability to derive insights and prove algorithmic effectiveness through rigorous logic and math
• Demonstrated creativity in tackling open-ended research and real-world AI challenges
• Clear communicator, able to translate complex ideas for technical and non-technical audiences
• Effective collaborator in cross-functional teams with researchers, engineers, and business partners
• Skilled in writing technical documentation, reports, and academic publications is a plus
• Passion for AI advancement and continuous learning
• Active interest in emerging AI research, with contributions to publications, open-source projects or conference preferred

Preferred Skills

• Experience in regulated industries (e.g., finance, healthcare, insurance)
• Excellent communication and stakeholder engagement skills
• Strong in GPU based accelerating computing technologies (CUDA, Rapids, NeMo, NIM, etc.)
• Proficiency in model evaluation, distributed training, and hyperparameter optimization
• Proficient in Big Data Theory based large scale data streaming and in-memory database technologies (Spark, Kafka, Redis, Elastic Search)
• Strong in automated workflow technologies (GitHub Actions, Terraform, Helmet) and containerization technologies (Docker, Kubernetes)
• Proficient in API, MCP and Microservices technologies
• Track records in large-scale, real-time AI/GenAI/AgenticAI/ML database and solution technologies
• Background in responsible AI/ML, model interpretability, and fairness auditing

Job 3 requirement (Applied Scientist)

Education

• PhD degree in Computer Science, Applied Mathematics, Engineering, or a related quantitative discipline
• Specialization or research in applied machine learning, MLOps, or ML systems preferred

Experience

• 4+ years of experience designing, developing, and deploying ML models in production environments
• 1+ year of experience in areas such as recommendation systems, pattern recognition, NLP, or time series modeling
• Experience with production-grade Python (preferred), as well as Java or C/C++
• Hands-on experience with large-scale software architecture, APIs, and model versioning systems

Skills

• Expertise in Python and ML frameworks such as PyTorch, TensorFlow, or scikit-learn
• Proficient in cloud-based ML platforms (e.g., Azure ML, Google Cloud Platform, AWS SageMaker)
• Solid understanding of machine learning algorithms (e.g., classification, regression, SVMs, ARIMA, ensemble methods, deep learning, neural network)
• Strong foundation in probability theory and statistical modeling (generative and discriminative)
• Familiarity with DevOps/MLOps practices, CI/CD pipelines, GitHub Actions, Terraform Docker, and Kubernetes
• Ability to communicate technical concepts clearly to both technical and non-technical stakeholders
• Strong collaboration skills with cross-functional teams (engineering, analytics, product)
• Ability to independently manage tasks and thrive in a remote-first or hybrid environment

Preferred Skills

• Experience in regulated industries (e.g., finance, healthcare, insurance)
• Excellent communication and stakeholder engagement skills
• Strong understanding of deep learning architectures (e.g. CNNs, RNNs, Transformers, GANs)
• Strong in GPU based accelerating computing technologies (CUDA, Rapids, NeMo, NIM, etc.)
• Proficiency in model evaluation, distributed training, and hyperparameter optimization
• Proficient in Big Data Theory based large scale data streaming and in-memory database technologies (Spark, Kafka, Redis, Elastic Search)
• Strong in automated workflow technologies (GitHub Actions, Terraform, Helmet) and containerization technologies (Docker, Kubernetes)
• Proficient in API and Microservices technologies
• Track records in large-scale, real-time AI/GenAI/ML database and solution technologies
• Background in responsible AI/ML, model interpretability, and fairness auditing