R&D Engineer – AI & Emerging Technologies

Description :

We are seeking a hands-on R&D Engineer to research, prototype, build and validate AI-enabled capabilities and emerging technologies for large-scale enterprise software solutions. Working closely with the R&D Lead and engineering teams, the role will convert promising ideas into working proofs of concept, reusable components, integration patterns and production-ready implementations. The ideal candidate has strong software development fundamentals, curiosity about new technologies and the discipline to build secure, maintainable solutions suitable for controlled data-centre and cloud environments.

Responsibilities:

• Develop and test AI-enabled application features using LLMs/SLMs, retrieval-augmented generation (RAG), embeddings, semantic search, intelligent document processing and agentic workflows where appropriate.
• Build proofs of concept, prototypes, reference implementations, APIs, libraries and reusable accelerators based on R&D priorities; document findings and limitations.
• Integrate AI models and services with enterprise applications, databases, document repositories, workflows and existing APIs while respecting access controls and data boundaries.
• Experiment with open-source models, AI frameworks, model-serving tools and vector search technologies; compare quality, latency, resource usage, security and cost.
• Develop, test and optimize backend services, data pipelines and integrations using sound software engineering practices, automated testing and version control.
• Contribute to evaluation of emerging technologies across cloud-native development, automation, enterprise search, data engineering, analytics and application modernization.
• Package and deploy prototypes in Linux, containers and Kubernetes-based environments; support private, on-premises and approved cloud deployment scenarios.
• Apply secure development practices, responsible AI safeguards, logging, monitoring and basic model evaluation, including checking for hallucinations and data leakage.
• Collaborate with solution architects and project teams to refine requirements, conduct technical demonstrations and transition successful R&D outcomes into delivery projects.
• Maintain clear technical notes, architecture diagrams, benchmark results, setup instructions and reusable examples; share learning with the wider engineering team.

Qualification s & Experience

• Bachelor’s degree in Computer Science, Information Technology, Software Engineering or a related discipline.
• 3+ years of hands-on software development experience, with demonstrable projects or practical exposure in AI/GenAI application development.
• Strong programming skills in Python, Java or comparable languages, with experience building REST APIs, services and database-backed applications.
• Experience using Git, testing frameworks, debugging tools and modern development workflows; ability to independently implement and troubleshoot solutions.

Skills/Requirements :

Technical Skills

• Working knowledge of LLM APIs or self-hosted models, prompting, RAG, embeddings, vector databases and AI application evaluation.
• Exposure to AI frameworks and libraries such as Hugging Face, LangChain, LangGraph, LlamaIndex, PyTorch or equivalent; depth in selected tools matters more than familiarity with all.
• Understanding of SQL databases, search systems, data processing, API integration and modular application design.
• Working familiarity with Linux, Docker, CI/CD and cloud-native deployment; Kubernetes exposure is an advantage.
• Ability to assess performance, scalability, security, maintainability and open-source licensing when evaluating technology choices.

Preferred Experience

• Implementation of AI-powered document processing, knowledge assistants, workflow automation, semantic search or decision-support features.
• Experience running open-source models locally or on private infrastructure, including basic GPU inference and model-serving concepts.
• Experience contributing to shared frameworks, developer tools, internal platforms or open-source projects.
• Exposure to mission-critical enterprise systems, regulated environments or deployment in private data centres.

Expected Outcomes

• Working, demonstrable prototypes with reproducible benchmarks and clear recommendations.
• Reusable, tested components and implementation guides that engineering teams can adopt.
• Secure, maintainable integrations that progress from research into real enterprise software projects.
• Consistent technical learning, documentation and collaboration that strengthen the R&D function.

Posted On :

Oct 17, 2026

Last date of Submission :

Dec 31, 2026