We are seeking a seasoned AI Engineer with a strong emphasis on building AI agents to join our rapidly growing team. The ideal candidate will have a demonstrated history of designing and deploying cutting-edge AI solutions that leverage large language models, machine learning techniques, and cloud-based infrastructures. This role involves leading AI-driven initiatives—ranging from building custom intelligent agents that automate data processing workflows to creating scalable NLP solutions that optimize business outcomes. Proficiency in automation platforms such as Make.com, Relevance.ai, or Zapier is essential for streamlining operational processes across various enterprise applications.
Key Responsibilities
1. AI Agent Development
- Design, build, and deploy AI agents capable of automating and optimizing tasks such as web scraping, customer support, knowledge retrieval (RAG chatbots), and database queries.
- Utilize LLM-based approaches (e.g., GPT, GPT fine-tuning) to create and optimize conversational AI experiences.
- Integrate AI agents into enterprise applications, ensuring seamless performance, reliability, and scalability.
2. Automation & Integration
- Set up and maintain workflows using Make.com, Relevance.ai, Zapier, or similar platforms.
- Collaborate with cross-functional teams to identify repetitive processes and implement AI-driven automation solutions.
- Troubleshoot, optimize, and document integration flows to ensure minimal downtime and maximum efficiency.
3. Machine Learning & NLP Solutions
- Apply advanced machine learning, deep learning, and statistical methods for classification, regression, and anomaly detection.
- Develop and refine natural language processing pipelines for text summarization, sentiment analysis, and entity extraction.
- Drive innovation in real-time analytics systems (e.g., real-time sentiment analysis, service ticket classification) using Spark, Docker, and NoSQL/SQL databases.
4. Data Engineering & MLOps
- Establish and maintain ETL pipelines for diverse data sources (web scraping, SQL databases, streaming data), ensuring data quality and integrity.
- Orchestrate data workflows with Apache Airflow or similar tools for automated data collection, transformation, and loading.
- Implement containerized microservices with Docker for deploying AI applications at scale and integrate CI/CD best practices.
5. Cloud & Infrastructure
- Deploy machine learning models on AWS, Google Cloud Platform, or Azure, leveraging cloud-native services for ML model hosting and data storage.
- Manage and optimize infrastructure for large-scale data processing and high-volume AI workloads.
- Ensure robust security and compliance standards within cloud environments.
6. Technical Leadership & Collaboration
- Mentor junior team members and guide them in best practices for AI research, model development, and deployment.
- Collaborate closely with cross-functional teams (Data Engineers, Product Managers, DevOps, etc.) to align technical strategies with business objectives.
- Contribute to roadmaps and stakeholder presentations, translating complex AI concepts into actionable business insights.
7. Continuous Innovation
- Stay up to date with the latest advancements in AI, machine learning, and cognitive technologies, especially in Generative AI and LLMs.
- Drive POCs for new AI-based products, ensuring timely experimentation and agile methodologies for quick iteration.
- Participate in industry conferences, workshops, or research initiatives to keep the team at the forefront of the AI landscape.
Required Qualifications & Experience
1. Education:
- PhD in Computer Science, Artificial Intelligence, Data Science, or a related field (Master’s degree with equivalent industry experience considered).
2. Experience:
- Minimum 3 years of overall experience in AI engineering, with at least 5 years focused on data science and machine learning.
- Demonstrated proficiency in Make.com, Relevance.ai, Zapier, or similar platforms for workflow automation.
3. Technical Proficiency:
- Programming: Python (NumPy, Pandas, Scikit-learn); familiarity with frameworks like PyTorch or TensorFlow is a plus.
- Cognitive Technologies: Generative AI, LLMs (GPT), NLP, Computer Vision.
- MLOps & Deployment: Apache Airflow, Docker, Git-based CI/CD pipelines.
- Cloud Platforms: AWS, GCP, or Azure.
- Data Visualization: Matplotlib, Plotly, or PowerBI for quick data insights.
4. Soft Skills:
- Proven track record of leading technical projects and teams to successful outcomes.
- Strong communication, presentation, and mentoring abilities.
- Ability to work collaboratively in a fast-paced, cross-functional team environment.
