Artificial Intelligence Engineer
TechDoQuest
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Irvine, CA
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Associate
Posted April 2, 2026
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Responsibilities
On the Associate AI/ML role at TechDoQuest, you'll collaborate with a multidisciplinary team to design, build, train, and optimize machine learning and deep learning models for high-impact solutions across NLP, computer vision, and predictive analytics. You will handle end-to-end model development including data collection and preprocessing, feature engineering, evaluation and monitoring, and deployment/integration using tools such as Python, PyTorch, agentic frameworks, LangChain, RAG, Kubernetes, Flask, Ray Serve, Azure DevOps, ONNX, and cloud or on-prem compute. You’ll also drive research and innovation, create clear technical documentation and presentations, and ensure ethical, compliant, and secure AI practices.
Responsibilities
- This position will work closely with multidisciplinary team, product managers, Data engineers, data scientists, and business stakeholders to bring AI solutions to production.
- Key Responsibilities Design and Develop ML Models for high impact engineering solutions.
- Build, train, and optimize machine learning and deep learning models to solve complex problems using natural language processing, computer vision, and predictive analytics.
- Knowledge of python, Pytorch, agentic frameworks, Langchain, RAG, capability to understand and build deep learning models on cloud or on premises compute.
- Data Collection and Preprocessing: Python preprocessing for structured and unstructured data [numeric, images, videos and documents) Feature Engineering: Identify, extract, and transform relevant features from raw data to improve model performance and interpretability.
- Model Evaluation and monitoring: Assess model accuracy and robustness using statistical metrics and validation techniques.
- Deployment and Integration: Knowledge of Kubernetes, Flask, Ray Serve, Azure Devops, ONNX, or cloud-based solutions. .
- Research and Innovation: Stay abreast of the latest developments in AI/ML research and technologies.
- Experiment with new algorithms, tools, and frameworks to drive innovation and maintain a competitive edge.
- Documentation and Reporting: Create comprehensive documentation of model architecture, data sources, training processes, and evaluation metrics.
- Present findings and recommendations to both technical and non-technical audiences.
- Ethics and Compliance: Uphold ethical standards and ensure compliance with regulations governing data privacy, security, and responsible AI deployment.
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Requirements
- Knowledge of python, Pytorch, agentic frameworks, Langchain, RAG, capability to understand and build deep learning models on cloud or on premises compute.
- Data Collection and Preprocessing: Python preprocessing for structured and unstructured data [numeric, images, videos and documents) Feature Engineering: Identify, extract, and transform relevant features from raw data to improve model performance and interpretability.
- Model Evaluation and monitoring: Assess model accuracy and robustness using statistical metrics and validation techniques.
- Deployment and Integration: Knowledge of Kubernetes, Flask, Ray Serve, Azure Devops, ONNX, or cloud-based solutions. .
- Research and Innovation: Stay abreast of the latest developments in AI/ML research and technologies.
- Documentation and Reporting: Create comprehensive documentation of model architecture, data sources, training processes, and evaluation metrics.
- 3+ years of professional experience in machine learning, artificial intelligence, or related fields.
- Hands-on experience with neural networks, deep learning, and architectures such as CNNs, RNNs, Transformers and Generative AI.
- Exposure to MLOps practices: monitoring, scaling, and automating ML workflows Experience with big data platforms: Databricks, Hadoop, Spark, Dataflow, etc.
- Familiarity with advanced topics such as reinforcement learning, generative models, or explainable AI Technical Skills: Proficiency in programming languages such as Python (preferred), Java, Csharp, or C++ Deep understanding of machine learning frameworks: PyTorch, scikit-learn, Keras, etc.
- Experience with data manipulation tools: NumPy, SQL, Pandas Solid grasp of statistics, probability theory, and linear algebra Familiarity with cloud and Data computing platforms: Azure, Azure DevOps, DataBricks GCP Knowledge of containerization and orchestration: Docker, Kubernetes Experience in deploying machine learning models to production Understanding of software engineering best practices: version control (Git), unit testing, CI/CD pipelines
Preferred Skills
- Deployment and Integration: Knowledge of Kubernetes, Flask, Ray Serve, Azure Devops, ONNX, or cloud-based solutions. .
- A PhD or relevant research experience would be a plus.
- Hands-on experience with neural networks, deep learning, and architectures such as CNNs, RNNs, Transformers and Generative AI.
- Exposure to MLOps practices: monitoring, scaling, and automating ML workflows Experience with big data platforms: Databricks, Hadoop, Spark, Dataflow, etc.
- Familiarity with advanced topics such as reinforcement learning, generative models, or explainable AI Technical Skills: Proficiency in programming languages such as Python (preferred), Java, Csharp, or C++ Deep understanding of machine learning frameworks: PyTorch, scikit-learn, Keras, etc.
- Experience with data manipulation tools: NumPy, SQL, Pandas Solid grasp of statistics, probability theory, and linear algebra Familiarity with cloud and Data computing platforms: Azure, Azure DevOps, DataBricks GCP Knowledge of containerization and orchestration: Docker, Kubernetes Experience in deploying machine learning models to production Understanding of software engineering best practices: version control (Git), unit testing, CI/CD pipelines
Education
- (Not required) – Qualifications Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
- (Not required) – A PhD or relevant research experience would be a plus.
Seeking a passionate, hands on technical AI /ML engineer to join our growing team and solve exciting engineering problems in semiconductor space. The nature of problems are wide ranging giving an opportunity to grow continuously. This position will work closely with multidisciplinary team, product managers, Data engineers, data scientists, and business stakeholders to bring AI solutions to production. Key Responsibilities Design and Develop ML Models for high impact engineering solutions. Build, train, and optimize machine learning and deep learning models to solve complex problems using natural language processing, computer vision, and predictive analytics. Knowledge of python, Pytorch, agentic frameworks, Langchain, RAG, capability to understand and build deep learning models on cloud or on premises compute. Data Collection and Preprocessing: Python preprocessing for structured and unstructured data [numeric, images, videos and documents) Feature Engineering: Identify, extract, and transform relevant features from raw data to improve model performance and interpretability. Model Evaluation and monitoring: Assess model accuracy and robustness using statistical metrics and validation techniques. Deployment and Integration: Knowledge of Kubernetes, Flask, Ray Serve, Azure Devops, ONNX, or cloud-based solutions. . Research and Innovation: Stay abreast of the latest developments in AI/ML research and technologies. Experiment with new algorithms, tools, and frameworks to drive innovation and maintain a competitive edge. Documentation and Reporting: Create comprehensive documentation of model architecture, data sources, training processes, and evaluation metrics. Present findings and recommendations to both technical and non-technical audiences. Ethics and Compliance: Uphold ethical standards and ensure compliance with regulations governing data privacy, security, and responsible AI deployment. Qualifications Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field. 3+ years of professional experience in machine learning, artificial intelligence, or related fields. A PhD or relevant research experience would be a plus. Hands-on experience with neural networks, deep learning, and architectures such as CNNs, RNNs, Transformers and Generative AI. Exposure to MLOps practices: monitoring, scaling, and automating ML workflows Experience with big data platforms: Databricks, Hadoop, Spark, Dataflow, etc. Familiarity with advanced topics such as reinforcement learning, generative models, or explainable AI Technical Skills: Proficiency in programming languages such as Python (preferred), Java, Csharp, or C++ Deep understanding of machine learning frameworks: PyTorch, scikit-learn, Keras, etc. Experience with data manipulation tools: NumPy, SQL, Pandas Solid grasp of statistics, probability theory, and linear algebra Familiarity with cloud and Data computing platforms: Azure, Azure DevOps, DataBricks GCP Knowledge of containerization and orchestration: Docker, Kubernetes Experience in deploying machine learning models to production Understanding of software engineering best practices: version control (Git), unit testing, CI/CD pipelines