Key Accountabilities

- Design, develop, and implement AI-powered features, assistants, and agents for our MarTech products.

- Build intelligent agentic workflows for use cases such as campaign assistance, offer recommendations, segmentation support, customer insights, reporting assistance, troubleshooting, and knowledge discovery.

- Work with product managers, architects, developers, and business teams to understand requirements and convert them into scalable AI solutions.

- Develop and integrate solutions using Large Language Models, prompt engineering, embeddings, vector search, and retrieval-augmented generation techniques.

- Create and maintain AI workflows involving context handling, memory patterns, tool integration, guardrails, and response optimization.

- Build AI pipelines using enterprise product knowledge, technical documentation, support content, metadata, and domain data.

- Integrate AI services with backend applications, APIs, microservices, workflow engines, and enterprise systems.

- Develop reusable AI components and frameworks that can be adopted across multiple modules and use cases within our product lines.

- Evaluate AI solution quality using testing, benchmarking, observability, feedback loops, and output validation techniques.

- Ensure AI implementations are scalable, secure, reliable, and optimized for latency, cost, and performance in production environments.

- Work closely with DevOps and platform teams to deploy, monitor, and manage AI solutions in cloud-native environments.

- Collaborate with QA and security teams to validate AI features, reduce risks, and ensure compliance with enterprise standards.





- Support the end-to-end development lifecycle of AI-powered product capabilities, from ideation and prototyping to deployment and continuous improvement.

- Identify opportunities to improve product capability, team productivity, and customer value through the effective use of AI and intelligent automation.

- Stay updated on advancements in Generative AI, agent frameworks, LLMOps, and enterprise AI engineering practices.

Mandatory Skills

- Bachelor’s degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related field.

- Minimum of 4-8 years of experience in software engineering, with at least 2 years of relevant experience in AI/ML, Generative AI, or intelligent application development.

- Strong programming skills in Python and good understanding of production-grade software development practices.

- Hands-on experience in building applications using Large Language Models and Generative AI platforms.

- Proven experience in prompt engineering, embeddings, semantic search, vector databases, and retrieval-augmented generation architectures.

- Experience in designing and developing AI assistants, copilots, chatbots, or agent-based enterprise applications.

- Strong understanding of APIs, backend integrations, microservices, and enterprise application integration patterns.





- Ability to translate functional and business requirements into clear technical solutions and implementation designs.

- Knowledge of model evaluation, AI testing, hallucination reduction, grounding techniques, and response quality validation.

- Familiarity with cloud platforms such as AWS, Azure, or GCP and deployment practices for AI-enabled applications.

- Understanding of containerization, CI/CD, monitoring, observability, and production support practices.

- Strong analytical, problem-solving, and debugging skills.

- Good communication skills with the ability to collaborate effectively with both technical and non-technical stakeholders.

- Knowledge of Agile development methodologies and product engineering practices.

Desirable Skills

- Experience in telecom, MarTech, loyalty, customer engagement, campaign management, or customer data platforms.

- Familiarity with AI orchestration and agent development frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, or similar tools.

- Exposure to Java, Spring Boot, Node.js, Kafka, event-driven architecture, and microservices-based enterprise systems.

- Experience with vector stores, caching layers, and AI observability or LLMOps tools.

- Understanding of recommendation systems, personalization, decisioning platforms, or analytics-driven applications.

- Familiarity with enterprise AI governance, PII protection, security controls, and responsible AI practices.

- Experience working in cooperative and cross-functional product engineering teams.

- Understanding of end-to-end enterprise AI architecture and production deployment considerations.

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