Position Overview:
This role focuses on leading the consultation, design, implementation, product engineering and research. As a Product and Research analyst, you will play a critical role in enhancing and implementing product releases, collaborating with operational teams, and providing post-implementation support.

Key Responsibilities:

• You will execute application-driven research and prototype development drawing from engineering disciplines including Machine Learning, Computer Science, and Computer Vision / Signal Processing.
• Aid in the design and development of research programs that achieve application-driven objectives.
• Execute research and development activities, implementing solutions that deliver transformational capability.
  Communicate the outcomes of your work to customers and the community.
• Maintain the currency of your knowledge of the cutting edge, and develop your technical and professional expertise to ensure the novelty of your solutions.
• AI Model Development: Train, fine-tune, and optimize machine learning (ML) and deep learning (DL) models for real-world applications.
• Prototype and Product Development: Build end-to-end AI solutions, from proof-of-concept prototypes to production-ready systems.
• Software Engineering: Implement AI-driven applications using Python, C++, TensorFlow, PyTorch, and cloud-based ML services (AWS, Azure, GCP).
• AI Integration: Embed AI models into web applications, embedded systems, robotics, and industrial software.
  Performance Optimization: Improve model efficiency through quantization, pruning, and distributed computing.
• Data Engineering: Preprocess and clean large datasets, implement data pipelines and ETL (Extract, Transform, Load) workflows.
• MLOps & Deployment: Develop CI/CD pipelines, automate model retraining, and deploy ML models to cloud or edge environments.
• Algorithm Research & Development: Explore cutting-edge ML models, statistical techniques, and signal processing methods.
• Data Analysis & Experimentation: Perform feature engineering, hypothesis testing, and uncertainty quantification.
• Technical Documentation & Reporting: Publish research findings, write whitepapers, and communicate insights to stakeholders.
• Computer Vision & Signal Processing: Research self-supervised learning, generative AI, and deep learning techniques.
• Security & Ethical AI Research: Investigate bias mitigation, explainability, and AI safety in applications like defense, healthcare, and cybersecurity.

Collaboration with AI Teams: Work with software engineers, data scientists, and product managers to transition AI research into deployable systems.

Required Skills:

• Machine Learning & Deep Learning: Hands-on experience with supervised, unsupervised, and reinforcement learning.
• Computer Vision & NLP: Experience with image processing, object detection, transformer models, and natural language understanding.
• Cloud Computing & DevOps: Strong knowledge of Kubernetes, Docker, and ML model deployment on AWS/GCP/Azure.
• Programming & Software Development: Expertise in Python, C++, and AI-specific frameworks (TensorFlow, PyTorch, OpenCV).
• Big Data & Analytics: Familiarity with SQL, NoSQL, Spark, Hadoop, and large-scale data processing.
• Scientific Research & Experimentation: Strong background in probability, statistics, and machine learning evaluation metrics.
• Computer Vision & Signal Processing: Knowledge of Fourier transforms, wavelets, feature extraction, and object tracking.
• Mathematical Modeling & Optimization: Experience with Bayesian inference, Markov models, and genetic algorithms.
• Technical Writing & Communication: Ability to write research papers, technical reports, and present findings to stakeholders.
• Programming & ML Frameworks: Proficiency in Python, MATLAB, Scikit-learn, TensorFlow, and OpenCV.a permanent basis.