About The Role
We are seeking a highly skilled
AI Software Engineer / Architect to join our global delivery team. You will be responsible for designing, developing, and implementing AI-driven solutions for enterprise-scale applications. This role requires deep technical expertise in AI/ML, strong software engineering fundamentals, and proven experience leading end-to-end solution architecture.
Key Responsibilities
Architect Role (8+ years):
- Define and own the AI architecture strategy, ensuring scalability, performance, and security.
- Design AI/ML systems that integrate with existing enterprise platforms (cloud/on-premise).
- Lead technical discussions with clients and stakeholders to gather requirements and provide architectural guidance.
- Select appropriate AI/ML models, frameworks, and tools based on business needs.
- Collaborate with data scientists, engineers, and product teams to bring AI solutions from concept to deployment.
- Review code, mentor engineering teams, and enforce best practices in AI and software development.
- Create architectural documentation and support pre-sales or proposal activities when required.
Software Engineer Role (34 Years)
- Develop and maintain AI-powered applications using frameworks like TensorFlow, PyTorch, or scikit-learn.
- Work with REST APIs, cloud services (AWS, Azure, GCP), and integrate ML models into production systems.
- Collaborate with cross-functional teams to understand requirements and implement features.
- Optimize AI models for performance and scalability in production environments.
- Conduct unit and integration testing, ensure high code quality, and participate in code reviews.
Required Skills & Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, or related field.
- Proven experience in AI/ML solution architecture and full-stack AI software development.
- Strong knowledge of Python and AI frameworks (e.g., TensorFlow, PyTorch, Keras, etc.).
- Experience with MLOps, model deployment pipelines, and cloud-native architectures.
- Familiarity with containerization tools (Docker, Kubernetes).
- Exposure to data engineering tools and pipelines (Spark, Kafka, etc.) is a plus.
- Excellent communication and leadership skills, with experience in client-facing roles.
- Ability to work in a fast-paced, agile environment with global delivery teams.
