Sr Consultant - AI Engineer

TransUnion LLC; Bengaluru; Hybrid; INR 1.800.000 - 3.200.000; Vollzeit

Zusammenfassung

TransUnion's Data Science Innovation team seeks a skilled AI/ML engineer to design and build AI-powered intelligent systems. The role covers search, ranking, and knowledge management, plus building scalable backend services and data pipelines for production-ready AI applications.

You will collaborate with stakeholders, evaluate techniques for reliability and business impact, and participate in design and code reviews.

Qualifikationen

• Solid understanding of classical AI concepts including state-space search, heuristic search (A*, BFS), planning, constraint satisfaction, and optimization.
• Experience building AI applications in semantic search, document intelligence, information extraction, recommendation systems, or enterprise decision support.
• Strong programming skills in Python; working knowledge of Java or similar language.
• Good understanding of distributed systems, databases, concurrency, and scalable backend architecture.

Aufgaben

• Design and build AI-powered intelligent systems for real enterprise problems.
• Develop scalable backend services, APIs, data pipelines, and orchestration layers for production-ready AI applications.
• Build and maintain search, ranking, recommendation, and knowledge management systems to support intelligent automation and enterprise knowledge use cases.
• Integrate AI capabilities with enterprise applications, including APIs, databases, document repositories, and workflow platforms, ensuring reliability, security, and observability.
• Apply understanding of context management, memory systems, and human-in-the-loop patterns to design AI systems that behave correctly in production.
• Collaborate with business and technology stakeholders to translate ambiguous problems into well-scoped, measurable technical solutions.
• Evaluate AI techniques, architectures, and tools based on reliability, scalability, and business impact.

Jobbeschreibung

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Team Overview

The Data Science Innovation team is the R&D engine of the Data Science organisation - a team dedicated to exploring, prototyping, and engineering the next generation of AI-powered intelligent systems. We are a team that invents, experiments, and builds things that do not yet exist within the organisation. Our work spans a wide spectrum of problem domains - from intelligent search and autonomous planning systems to knowledge engineering, decision support, and agentic automation - reflecting the deliberate breadth of challenges we take on.

The team operates at the intersection of classical AI research, applied engineering, machine learning, and enterprise platform delivery.

This is a hybrid position and involves regular performance of job responsibilities virtually as well as in-person at an assigned TU office location for a minimum of two days a week.

Role Overview And Core Responsibilities

• Design and build AI-powered intelligent systems: applying classical AI concepts - search, planning, ranking, retrieval, reasoning, constraint satisfaction, and knowledge representation - to solve real enterprise business problems.
• Develop scalable backend services, APIs, data pipelines, and orchestration layers: for AI-driven applications that are production-ready from day one.
• Build and maintain search, ranking, recommendation, and knowledge management systems: that support intelligent automation and enterprise knowledge use cases.
• Integrate AI capabilities with enterprise applications: including APIs, databases, document repositories, and workflow platforms, ensuring reliability, security, and observability.
• Apply understanding of context management, memory systems, and human-in-the-loop patterns: to design AI systems that behave correctly and safely in production environments.
• Collaborate with business and technology stakeholders: to understand ambiguous problem areas and translate them into well-scoped, measurable technical solutions.
• Evaluate AI techniques, architectures, and tools: based on practical value, reliability, scalability, and business impact - reasoning from first principles rather than relying solely on frameworks or pre-built tools.
• Participate in design reviews and code reviews: and contribute to the team's engineering standards and technical culture.

Required Knowledge And Experiences

• Classical AI and Intelligent Systems: Solid understanding of core classical AI concepts: state-space search, heuristic search (A*, beam search), forward and backward planning, partial-order planning, constraint satisfaction, and optimization. Hands-on experience in one or more applied AI domains - search and ranking, planning and scheduling, recommendation engines, rule engines, expert systems, or knowledge-based systems. Understanding of knowledge representation, context management, memory systems, and intelligent automation patterns.
• AI Application Development: Experience building AI applications in areas such as semantic search, document intelligence, information extraction, recommendation systems, conversational AI, or enterprise decision support. Ability to evaluate AI techniques based on reliability, scalability, and business impact.
• Software Engineering: Strong programming skills in Python; working knowledge of Java or a similar language. Strong understanding of data structures, algorithms, computational complexity, object-oriented design, and design patterns. Experience building production-grade APIs, microservices, data pipelines, or event-driven systems. Familiarity with Git, CI/CD, automated testing, logging, and monitoring.
• Systems and Platform: Good understanding of distributed systems, databases, concurrency, and scalable backend architecture. Awareness of security, reliability, observability, and maintainability requirements for enterprise-scale systems.

Required Technical Skills

• Programming: Python (strong); Java or equivalent general-purpose language (working knowledge).
• Classical AI and Search: Search algorithms (A*, BFS, beam search), heuristic planning, constraint satisfaction, ranking and optimization systems.
• AI Systems: Knowledge representation, rule-based systems, semantic systems, planning and scheduling frameworks, recommendation and retrieval systems.
• Data Engineering and Storage: SQL and relational databases; NoSQL databases; Vector databases, Graph databases (e.g. Neo4j, Ontop); data pipeline design and ETL/ELT patterns.
• Cloud and Infrastructure: Azure, AWS, or GCP; Docker; Kubernetes; Kafka, message queues; cloud-native application design.
• Engineering Practices: REST APIs, microservices, event-driven architectures, CI/CD pipelines, automated testing, logging, monitoring, and observability tooling.

Preferred Skills (Good to Have)

• Experience with Retrieval-Augmented Generation (RAG), vector search, embedding-based retrieval,
• Familiarity on Ontology & Knowledge graph integration.
• Familiarity with </div>