Role Description
Employees in this job function are responsible for developing intelligent programs, cognitive applications and algorithms for data analysis and automation, leveraging various AI techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming
Key Responsibilities:
1) Understand business requirements and develop AI algorithms, models and programs to solve
complex problems, generate recommendations, extract patterns, make predictions, interpret sensor data (images, sound), orchestrate automation and enable self-service capabilities
2) Perform large-scale experimentation and develop data driven applications that translate data into actionable intelligence
3) Drive innovative applications of Artificial Intelligence tools and techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming
4) Research and optimize AI technologies to enhance efficiency and accuracy of data analysis and create more efficient automation
Key Responsibilities / Additional Info
Enterprise Architecture Design: Create detailed blueprints for AI systems, including data ingestion pipelines, model deployment strategies, and integration with existing IT infrastructure.
Technology Selection: Evaluate and select the most suitable tools and platforms (Cloud-native or Open Source). You will balance the need for cutting-edge capabilities with enterprise-grade stability, cost-control, and security.
Enablement Frameworks: Rather than just overseeing model training, you will architect the automated pipelines and guardrails (MLOps) that ensure all models developed across the org meet performance and security metrics.
System Integration: Ensure AI services integrate seamlessly with enterprise identity (IAM), security protocols (VPC-SC), and central data repositories.
Cloud Computing: Deep expertise in major cloud platforms (GCP, AWS, or Azure). You should understand hyperscaler AI services (e.g., Vertex AI, SageMaker, or Azure AI) and how to architect across them.
Machine Learning & GenAI: Proficiency in ML algorithms and Deep Learning frameworks (TensorFlow, PyTorch). Experience with LLM orchestration (LangChain, LlamaIndex), RAG patterns, and model serving.
Big Data & Programming: Expertise in Python and familiarity with big data processing tools (e.g., Spark, Kafka, Snowflake/BigQuery).
Infrastructure as Code (IaC): Proficiency in Terraform or similar tools to build repeatable, automated environments.
Problem-Solving: A high degree of curiosity; the ability to analyze complex, "messy" organizational problems and devise elegant AI-driven architectural solutions.
Adaptability: A passion for staying ahead of the rapidly evolving AI landscape and a willingness to pivot strategies as new technologies (like Agentic AI or new LLMs) emerge.
Skills Required
AI, API, Full Stack, Java, Python, JavaScript, Spring Boot, GCP