We are seeking a hands-on R&D; Lead to drive applied research and technology innovation for large-scale enterprise software solutions. The role will focus on Artificial Intelligence (AI), generative AI, agentic AI, and emerging technologies, translating promising ideas into secure, reusable, and production-ready capabilities. The successful candidate will combine strong software engineering and architecture expertise with practical AI implementation skills and work closely with engineering and delivery teams to ensure research delivers measurable value across projects.

Responsibilities:

- Lead applied research in Generative AI, agentic workflows, machine learning, intelligent automation and other emerging enterprise technologies, prioritizing practical use cases.
- Assess LLMs and SLMs, retrieval-augmented generation (RAG), semantic search, document intelligence, natural-language interfaces and AI-assisted decision support for integration into enterprise applications.
- Design and build proofs of concept, prototypes, reference implementations and benchmarks; validate feasibility, accuracy, security, performance, cost and operational suitability.
- Transform validated ideas into reusable APIs, libraries, frameworks, components and development accelerators that can be adopted across multiple software projects.
- Guide AI integration with existing business workflows, rules engines, databases, document repositories, identity systems and enterprise APIs.
- Evaluate open-source frameworks and emerging architectures for maturity, licensing, community health, maintainability, interoperability and total cost of ownership.
- Explore modern application architecture, data engineering, analytics, enterprise search, cloud-native platforms and automation to improve solution quality and delivery efficiency.
- Architect AI deployment for controlled environments, including on-premises data centres and approved cloud infrastructure, with appropriate model hosting, inference, access controls and observability.
- Apply responsible AI engineering practices,



including model evaluation, grounding, human oversight, privacy safeguards, auditability, and LLMOps/MLOps where relevant.
- Investigate AI-assisted engineering practices for coding, testing, documentation, migration and operations; measure productivity and quality improvements.
- Own an outcome-driven R&D; roadmap, collaborate with solution architects and project teams, and guide successful handover of validated technologies into delivery.
- Mentor technical staff, document technical decisions and findings, and present demonstrations, recommendations and adoption plans to leadership.

Qualifications and Experience:

- Bachelor’s or Master’s degree in Computer Science, Information Technology, Software Engineering or a related discipline.
- 7+ years in software engineering, enterprise application development, solution architecture or applied R&D;, including experience leading technical initiatives.
- Substantial hands-on experience developing AI-enabled applications or integrating AI/ML capabilities into software solutions; recent practical work with contemporary Generative AI is strongly preferred.
- Proven ability to take technology initiatives from evaluation and prototyping to validated, production-oriented implementation.
- Strong communication, technical decision-making, mentoring and cross-functional collaboration skills.

Core Technical Skills:

- Strong programming and engineering skills in Python, Java or comparable enterprise technologies, with sound understanding of APIs, integration patterns and modular/distributed architectures.
- Practical knowledge of LLMs/SLMs, RAG, embeddings, vector search, tool calling,



agent orchestration and AI evaluation; experience with relevant frameworks such as LangChain, LangGraph, LlamaIndex or equivalents.
- Understanding of model serving, self-hosted inference, retrieval pipelines, fine-tuning trade-offs and model lifecycle practices.
- Working knowledge of relational databases, enterprise search, data processing, secure software development and application observability.
- Experience with Linux, containers, Kubernetes, CI/CD and deployment of reliable enterprise applications in cloud or on-premises environments.
- Ability to compare technical alternatives using measurable criteria such as accuracy, throughput, latency, scalability, security, maintainability and cost.

Preferred Experience

- Production implementation of AI-enabled workflows, conversational interfaces, intelligent document processing or enterprise knowledge retrieval.
- Private or self-hosted AI deployments, GPU inference, open-source model ecosystems, or regulated and security-sensitive infrastructure.
- Experience with large, mission-critical enterprise platforms involving complex workflows, substantial data volumes, multiple integrations and high availability.
- Contribution to reusable internal platforms, engineering accelerators, reference architectures or open-source projects.

Expected Outcomes

- A prioritized R&D; roadmap linked to real software delivery needs and measurable business or engineering value.
- Validated AI capabilities and reusable components adopted by project teams rather than remaining isolated demonstrations.
- Documented benchmarks, technical standards and implementation guidance supporting sustainable production deployment.
- Demonstrable improvements in solution capability, engineering productivity, reliability or implementation efficiency.

Pay: ₹600,000.00 - ₹1,700,000.00 per year

Benefits:

- Food provided
- Health insurance
- Provident Fund

Experience:

- Software Engineer: 7 years (Required)

Work Location: In person

📌 R&D Lead (Peroorkada)
🏢 Kran Consulting Private
📍 Peroorkada