AI Engineer (4+ Years Experience) - Web Data Focus

We are seeking a highly motivated and experienced AI Engineer to join our team. The ideal candidate will have a proven track record of designing, implementing, and deploying machine learning and AI solutions, with specific experience in capturing, processing, and leveraging data sourced from web search platforms. This role is crucial for developing our next generation of intelligence products.

Desired Skills (Must-Haves) :

These skills are essential for the role and should be demonstrated through professional experience :

- Core AI/ML Expertise : Strong foundational knowledge and hands-on experience with classical Machine Learning algorithms and modern Deep Learning frameworks (e.g., PyTorch, TensorFlow).

- Web Data Acquisition : 3+ years of professional experience working on projects where data was programmatically captured, scraped, or accessed via APIs from web search engines (e.g., Google, Bing, custom search indexes). This includes handling challenges like rate limits, CAPTCHAs, and data structuring.

- Natural Language Processing (NLP) : Expertise in key NLP techniques, including Named Entity Recognition (NER), Topic Modeling, Text Classification, Sentiment Analysis, and working with large Transformer-based models (e.g., BERT, RoBERTa).

- Programming Proficiency : Expert-level proficiency in Python and relevant libraries (pandas, NumPy, scikit-learn).

- Data Engineering Fundamentals : Solid experience with data cleaning, preprocessing, feature engineering, and working with structured and unstructured data, particularly JSON and HTML/XML output from web sources.

- Deployment and MLOps : Practical experience with deploying ML models into production environments using tools like Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).

- Database Knowledge : Experience with both SQL (e.g., PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, Redis) databases for managing large datasets.

Good to Have Skills :

These skills are advantageous and will differentiate candidates :

- Scalability & Distributed Computing : Experience with big data technologies such as Spark (PySpark) or Dask for handling web-scale data volumes.

- Search and Information Retrieval (IR) : Familiarity with open-source IR systems like Elasticsearch or Solr for indexing, searching, and querying the captured web data.

- Cloud-Specific Services : Hands-on experience with specialized cloud AI/ML services (e.g., AWS Sagemaker, Google AI Platform).

- Software Engineering Practices : Knowledge of Git for version control, CI/CD pipelines, and writing unit/integration tests for robust code.

- Graph Databases : Exposure to Graph Databases (e.g., Neo4j) for modeling relationships and connections derived from web entities.

Responsibilities :

The AI Engineer will be responsible for the full lifecycle of AI product development, focusing heavily on web-sourced data :

Data Capture & Pipeline Development :

- Design, build, and maintain robust, fault-tolerant data pipelines specifically for extracting and ingesting data from web search results and related sources at scale.

- Develop and optimize scraping and access strategies to ensure high-quality, up-to-date data is consistently available.

Model Research & Development :

- Lead the research, prototyping, and experimentation of state-of-the-art AI/ML models to solve complex business problems using the acquired web data (e.g., knowledge graph construction, competitive intelligence, trend prediction).

- Apply advanced NLP techniques to extract, structure, and synthesize key information from unstructured web search content.

Deployment & Optimization :

- Work closely with the DevOps team to productionize, monitor, and maintain deployed AI services and models.

- Optimize model performance (latency, throughput, accuracy) and computational efficiency in a production environment.

Collaboration & Documentation :

- Collaborate with product managers and other engineering teams to define technical requirements and project roadmaps.

- Write clean, well-documented, and testable code.

- Stay current with the latest advancements in AI, ML, NLP, and data acquisition technologies.