Machine Learning 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 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.

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 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.