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. Show more