Senior Software Engineer

Bajaj Finance · Banking & Financial Services

Pune, Maharashtra, India
On-site
Posted today
Software Engineering

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About the job

Job Purpose

The Product Engineer – VoiceNext AI Platform is responsible for developing, deploying, and supporting AI-powered conversational and generative AI solutions for the VoiceNext product ecosystem. The incumbent will work closely with Product, Business, AI, and Technology teams to build scalable AI applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), APIs, and cloud-native platforms.

The role holder will support end-to-end product engineering activities including solution development, prompt engineering, AI application deployment, system integration, performance optimization, and platform monitoring. The role ensures VoiceNext AI solutions are scalable, secure, business-aligned, and deliver superior customer and employee experiences across channels.

Duties and Responsibilities

AI Application Development

a. Develop AI-powered applications using GPT, Gemini, and other Large Language Models (LLMs).

b. Build backend services, APIs, and integrations using Python.

c. Support development of customer-facing and employee-facing AI solutions within the VoiceNext platform.

d. Collaborate with Product and AI teams to translate requirements into scalable technical solutions.

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Prompt Engineering & Optimization

a. Design, develop, and optimize prompts to improve LLM performance and response quality.

b. Evaluate prompt effectiveness through testing and performance analysis.

c. Implement prompt frameworks to improve consistency, reliability, and business outcomes.

d. Support experimentation and continuous improvement of LLM interactions.

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RAG & Knowledge Integration

a. Build and maintain Retrieval-Augmented Generation (RAG) pipelines.

b. Integrate enterprise knowledge repositories using vector databases and search services.

c. Support implementation of embeddings, semantic search, and contextual retrieval mechanisms.

d. Ensure AI models generate accurate and business-relevant responses.

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Deployment & Platform Engineering

a. Deploy and manage AI applications on Linux environments and Azure cloud platforms.

b. Containerize applications using Docker and deploy workloads on Azure Kubernetes Service (AKS).

c. Support CI/CD pipelines and release management activities.

d. Monitor application performance, uptime, scalability, and platform reliability.

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Testing & Quality Assurance

a. Perform application testing, performance validation, and troubleshooting activities.

b. Support User Acceptance Testing (UAT) and issue resolution.

c. Ensure solution quality, accuracy, and production readiness before deployment.

d. Assist in post-production monitoring and stabilization activities.

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Security, Governance & Monitoring

a. Implement security, access control, and data privacy standards for AI applications.

b. Monitor system performance, latency, and operational metrics.

c. Maintain implementation trackers, technical documentation, and deployment reports.

d. Ensure compliance with enterprise governance and cloud standards.

Key Decisions / Dimensions

DIMENSIONS

Financial Dimensions

Supports VoiceNext AI product development initiatives.

Contributes to platform efficiency, automation, and operational optimization.

Supports scalable deployment of enterprise AI capabilities.|DECISIONS

AI Solution Development

Recommend technical approaches for implementing VoiceNext AI use cases using LLMs, RAG, and cloud-native technologies.

Prompt & Model Optimization

Evaluate prompt effectiveness and recommend improvements to enhance business outcomes and user experience.

Deployment Readiness

Assess application readiness for testing, deployment, and production release.

Platform Performance Improvement

Recommend actions for performance tuning, scalability enhancement, and operational optimization.

Major Challenges

. MAJOR CHALLENGES

Deploying scalable AI solutions while maintaining performance and response quality.

Managing integrations across multiple enterprise systems and APIs.

Optimizing LLM outputs for business-specific use cases.

Balancing model accuracy, latency, and infrastructure cost.

Ensuring secure and compliant handling of enterprise data.

Supporting high availability and reliability requirements for VoiceNext applications.

Keeping pace with the rapidly evolving Generative AI ecosystem.

Required Qualifications and Experience

. SKILLS AND KNOWLEDGE

Educational Qualifications

Bachelor's Degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Electronics, or related discipline.

Master's Degree or specialization in AI/ML preferred.

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Work Experience

3-5 years of experience in AI Engineering, Software Development, Product Engineering, Cloud Engineering, or Technology Consulting.

Hands-on experience with GPT, Gemini, Azure OpenAI, or similar LLM platforms.

Experience building AI applications using Python.

Experience with Retrieval-Augmented Generation (RAG) architectures.

Strong understanding of Generative AI concepts and AI application development.

Exposure to Agile development methodologies.

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Technical Competencies

Large Language Models (GPT, Gemini)

Prompt Engineering

Python Development

Retrieval-Augmented Generation (RAG)

Vector Databases (Azure AI Search, Pinecone, FAISS)

REST APIs & Microservices

Docker & Containerization

Azure Kubernetes Service (AKS)

Linux Administration

CI/CD Pipelines

Azure AI Services

Cloud-native Application Development

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Preferred Certifications

Microsoft Azure AI Engineer Associate

Azure Fundamentals (AZ-900)

Azure OpenAI / Generative AI Certifications

Certified Kubernetes Application Developer (CKAD)

Docker Certified Associate

Certified Scrum Master (CSM)

PMP

Generative AI Professional Certifications

AI/ML Engineering Certifications

This version positions the role as a GB04 Product Engineer focused on the VoiceNext AI Platform, with responsibilities centered on LLM development, prompt engineering, RAG implementation, Python engineering, AKS deployment, and AI product delivery, while remaining appropriately scoped for a GB04 individual contributor role.

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