Senior AI/ML Engineer

• 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
• Perform large-scale experimentation and develop data driven applications that translate data into actionable intelligence
• 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
• Research and optimize AI technologies to enhance efficiency and accuracy of data analysis and create more efficient automation

• Artificial Intelligence & Expert Systems, Machine Learning, Data Science, Data Modeling, Software Development Lifecycle

Experience Required:

• Specialist Exp: 5+ experience in relevant field

Experience Preferred:

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
• 3+ years of progressive experience in AI/ML, data science, or advanced analytics, with a proven track record of delivering production-grade solutions in large enterprise environments.
• Strong proficiency in Python and SQL. Familiarity with Graph Query Languages (e.g., Cypher).
• Demonstrated experience with MLOps principles and tools (e.g., Azure ML, AWS SageMaker, GCP AI Platform, Kubeflow, MLflow) and designing / implementing AI-specific SDLCs.
• Strong technical expertise in cloud services (GCP/Vertex AI) and data integration patterns
• Strong analytical, problem-solving, and critical thinking skills.
• Exceptional communication, interpersonal skills, and stakeholder management skills., * AI-SDLC Experience: Proven track record of using AI tools to enhance personal or team productivity (e.g., Agentic workflows, RAG-based requirement synthesis).
• Requirement Engineering: Experience in a product engineering role with proven track record of translating business needs into technical specifications for applied AI implementation.
• Knowledge Graph: Understanding semantic ontologies and how they enable advanced analytics.
• COTS Integration: Experience integrating COTS AI solutions into an enterprise tech stack.
• Supply Chain Domain Knowledge: Functional understanding of supply chain operations, including demand & capacity planning, logistics, sustainability & risk management, resilience, etc.