Artificial Intelligence Engineer

We are seeking a highly motivated and experienced AI/ML Developer Level II to join our dynamicteam. In this role, you will be a key contributor to the design, development, and deployment ofsophisticated conversational AI systems, primarily using the RASA framework. Your deepexpertise in Python, coupled with hands-on experience in the Google Cloud Platform (GCP)ecosystem, will be essential for building, scaling, and maintaining robust, enterprise-grade virtualassistants and chatbots. You will move beyond prototyping to take ownership of components,optimize model performance, and ensure the reliability of our AI solutions in production.Key Responsibilities:(Must-have)RASA Framework Development: Design, build, and maintain advanced conversational AI agents using the RASA Open Source and/or RASA X/Pro platforms. This includes developing complex dialogue management with stories and rules, configuring the NLU pipeline, and creating custom actions.Model Training & Optimization: Train, evaluate, and fine-tune RASA NLU and dialogue models. Implement strategies for continuous improvement using conversation analytics and user feedback to enhance intent classification, entity recognition, and response quality.Python-Centric Solutioning: Write clean, eƯicient, and well-documented Python code forcustom actions, policies, and integrations. Develop scalable backend services and APIs to connect RASA agents with other business systems.Google Cloud Platform (GCP) Integration & Deployment: Architect, deploy, and manage RASA bots on GCP (using Google Kubernetes Engine - GKE, Pub/Sub for messaging, Cloud Run, or Compute Engine). Utilize GCP services like Vertex AI and Dialogflow CX for complementary use-cases or hybrid architectures, and Cloud Speech-to-Text / Text-to-Speech for voice-enabled bots.Nice-to-have:CI/CD & MLOps: Implement and maintain CI/CD pipelines for automated testing, building, and deployment of RASA models using tools like Git. Champion MLOps best practices for versioning, monitoring, and retraining models.Data Management: Leverage Google BigQuery for analyzing conversation logs and deriving insights. Use Cloud Storage for managing training data and model artifacts.Required Qualifications:Education: Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.Experience: 3+ years of professional experience in AI/ML development, with at least 2 years of hands-on, in-depth experience building and deploying production-level chatbots with the RASA framework.Programming: Strong proficiency in Python, with a solid understanding of software engineering principles, design patterns, and API development.Google Cloud Platform: Proven, hands-on experience with core GCP services, including:Compute: Google Kubernetes Engine (GKE), Cloud Run, or App Engine.AI/ML Services: Practical knowledge of Dialogflow and/or Cloud Natural Language API.Infrastructure: Cloud Storage, Cloud Build, IAM, and VPC networking.Machine Learning Fundamentals: Solid understanding of NLP fundamentals (intent detection, entity extraction, context management) and practical experience with machine learning libraries (e.g., scikit-learn, spaCy, Transformers).Version Control & Collaboration: High proficiency with Git in a collaborative team environment.Soft Skills & Other Requirements:Problem-Solving: Excellent analytical and problem-solving skills with the ability to troubleshoot complex technical issues in distributed systems.Ownership & Initiative: A proactive mindset with the ability to take ownership of projects from conception to deployment and beyond, working with minimal supervision.Communication: Strong verbal and written communication skills. Ability to clearly articulate technical concepts to both technical and non-technical stakeholders.Agile Methodology: Experience working in an Agile/Scrum development process.Team Player: A collaborative attitude, with a willingness to mentor junior developers and share knowledge with the team.Continuous Learning: A passion for staying up-to-date with the rapidly evolving fields ofConversational AI, MLOps, and cloud technologies.Preferred Qualifications (Bonus):GCP Professional Machine Learning Engineer or other GCP certifications.Experience with containerization technologies (Docker) and orchestration (Kubernetes).Knowledge of infrastructure-as-code tools like Terraform.