Job Summary:
As an AI Developer II, you will contribute to the design, development, and deployment of AI-driven solutions in collaboration with AI lead and cross-functional teams. You will focus on building, improving, and scaling machine learning and AI models that address real business needs, while staying current with emerging AI technologies and best practices.
In this role, you will support key phases of the AI development lifecycle, including data preparation, feature engineering, model validation, and performance optimization. You will assist with the deployment, monitoring, and maintenance of AI solutions in production environments as needed. Working with both onshore and offshore partners, you will help ensure smooth delivery of prioritized AI initiatives, maintain visibility across project pipelines, and support adherence to security, data privacy, and compliance standards prior to release.
Key Responsibilities:
Design, build, and deploy AI and machine learning solutions that solve real business problems
Support the full AI development lifecycle, including data preparation, feature engineering, model training, validation, and optimization
Develop and enhance scalable AI models using modern ML frameworks and cloud-based platforms
Deploy, monitor, and maintain AI solutions in production to ensure performance, reliability, and scalability
Collaborate with AI leads, engineers, and cross-functional stakeholders to translate business requirements into technical solutions
Partner with onshore and offshore teams to support timely delivery of prioritized AI initiatives
Apply security, data privacy, and compliance best practices throughout the development and release process
Contribute to shared AI components, reusable assets, and development standards
Stay current with emerging AI technologies and incorporate relevant advancements into solutions
Support knowledge sharing by mentoring or enabling team members on AI tools and best practices
Education
Bachelor’s degree in Computer Science, Engineering, Information Technology, Mathematics, Statistics, Physics, or a related field.
Nice to have: Master’s or Ph.D. in a relevant field
Nice to have: Industry certifications in AI, machine learning, or cloud platforms
Experience
3+ years of experience developing and deploying AI / machine learning solutions
Demonstrated experience deploying AI systems into production environments
Experience collaborating with cross-functional teams to translate business requirements into scalable AI solutions
Experience mentoring, training, or enabling other developers, analysts, or business users on AI tools, models, or best practices
Core Technical Skills
Strong proficiency in Python for AI and machine learning development
Hands-on experience with ML frameworks and libraries such as PyTorch, TensorFlow, and scikit-learn
Experience with NLP and document-centric AI, including: OCR and document processing, Document classification and entity extraction, Text summarization
Solid experience with data preprocessing, feature engineering, and analysis using tools such as: Pandas, NumPy, Matplotlib, Seaborn
Experience working with cloud-based AI platforms, preferably Microsoft Azure or AWS
Proficiency in SQL and experience with relational and/or NoSQL databases
Familiarity with containerization technologies such as Docker and/or Kubernetes
Familiary with building LLM-based solutions: Chatbots and conversational AI, Prompt engineering and evaluation, Retrieval-Augmented Generation (RAG), Intelligent or semantic search
Exposure to MLOps practices, including: CI/CD pipelines for ML models, Model versioning, deployment automation, and monitoring
Familiarity with REST API development and integrating AI capabilities into backend systems
Experience contributing to shared AI components, development standards, or reference architectures
Ways of Working
Experience working in Agile and/or Waterfall delivery environments
Experience collaborating with onshore and offshore teams
Strong communication skills, including the ability to explain AI concepts and trade-offs to non-technical stakeholders