Artificial Intelligence Engineer

We are seeking a highly motivated and experienced AI/ML Developer Level II to join our dynamic

team. In this role, you will be a key contributor to the design, development, and deployment of

sophisticated conversational AI systems, primarily using the RASA framework. Your deep

expertise 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 virtual

assistants 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-hav

• e: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 model
• s.Data Management: Leverage Google BigQuery for analyzing conversation logs and deriving insights. Use Cloud Storage for managing training data and model artifact

s.
Required Qualificatio

• ns:Education: Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experien
• ce.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 framewo
• rk.Programming: Strong proficiency in Python, with a solid understanding of software engineering principles, design patterns, and API developme
• nt.Google Cloud Platform: Proven, hands-on experience with core GCP services, includi
• ng:Compute: Google Kubernetes Engine (GKE), Cloud Run, or App Engi
• ne.AI/ML Services: Practical knowledge of Dialogflow and/or Cloud Natural Language A
• PI.Infrastructure: Cloud Storage, Cloud Build, IAM, and VPC networki
• ng.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, Transformer
• s).Version Control & Collaboration: High proficiency with Git in a collaborative team environme

nt.
Soft Skills & Other Requireme

• nts:Problem-Solving: Excellent analytical and problem-solving skills with the ability to troubleshoot complex technical issues in distributed syst
• ems.Ownership & Initiative: A proactive mindset with the ability to take ownership of projects from conception to deployment and beyond, working with minimal supervis
• ion.Communication: Strong verbal and written communication skills. Ability to clearly articulate technical concepts to both technical and non-technical stakehold
• ers.Agile Methodology: Experience working in an Agile/Scrum development proc
• ess.Team Player: A collaborative attitude, with a willingness to mentor junior developers and share knowledge with the t
• eam.Continuous Learning: A passion for staying up-to-date with the rapidly evolving field
• s ofConversational AI, MLOps, and cloud technolog

ies.
Preferred Qualifications (Bo

• nus):GCP Professional Machine Learning Engineer or other GCP certificat
• ions.Experience with containerization technologies (Docker) and orchestration (Kuberne
• tes).Knowledge of infrastructure-as-code tools like Terra

form.