AI/ML Developer

Position Overview

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

for custom 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.

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.

Education

Bachelor’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 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 of

Conversational 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