AI/ML Developer

Position OverviewWe 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 ResponsibilitiesMust-have● RASA Framework Development: Design, build, and maintain advanced conversational AIagents using the RASA Open Source and/or RASA X/Pro platforms. This includesdeveloping complex dialogue management with stories and rules, configuring the NLUpipeline, and creating custom actions.● Model Training & Optimization: Train, evaluate, and fine-tune RASA NLU and dialoguemodels. Implement strategies for continuous improvement using conversation analyticsand user feedback to enhance intent classification, entity recognition, and responsequality.● Python-Centric Solutioning: Write clean, eƯicient, and well-documented Python codefor custom actions, policies, and integrations. Develop scalable backend services andAPIs to connect RASA agents with other business systems.● Google Cloud Platform (GCP) Integration & Deployment: Architect, deploy, and manageRASA 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 forcomplementary 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, anddeployment 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 derivinginsights. Use Cloud Storage for managing training data and model artifacts.Technical Skills● 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.EducationBachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.Soft Skills & Other Requirements● Problem-Solving: Excellent analytical and problem-solving skills with the ability to troubleshootcomplex 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 articulatetechnical 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 shareknowledge 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