Senior GenAI Engineer (Leadership + Development Role) - URGENT HIRING - 5+ years of experience

About Senzcraft :

Founded by IIM Bangalore and IEST Shibpur Alumni, Senzcraft is a hyper-automation company. Senzcraft vision is to Radically Simplify Today's Work. And Design Business Process For The Future. Using intelligent process automation technologies.

We have a suite of SaaS products and services, partnering with automation product companies.

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Our AI Operations SaaS platform –

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Senzcraft is awarded by Analytics India Magazine in it’s report “State of AI in India” as a “Niche AI startup”. Senzcraft is also recognized by NY based SSON as a top hyper-automation solutions provider.

About the Role (Senior GenAI Engineer)

Role Overview :

We are seeking a highly skilled Senior GenAI Engineer with 5+ years of experience in AI / ML and automation engineering to design, develop, and deploy production-ready Generative AI solutions. This role requires strong hands-on expertise in LLMs, orchestration frameworks, RAG, embeddings, and MCP integration. You will work across both infrastructure and solution design, translating business needs into robust, scalable GenAI applications.

The ideal candidate should not only bring technical depth but also have a holistic view of GenAI application development , with the ability to lead a team while remaining hands-on with coding and solution building .

Key Responsibilities :

• Design & Development : Build and deploy GenAI / LLM-powered applications, integrating frameworks such as CrewAI, LangChain, AutoGen, Semantic Kernel, or LlamaIndex.
• Orchestration & Integration : Develop and maintain Model Context Protocol (MCP) interfaces and servers to enable interoperability across AI / simulation components.
• RAG & Embeddings : Implement efficient Retrieval-Augmented Generation (RAG) pipelines, vector DB integration (Pinecone, FAISS, Chroma), and embedding strategies.
• Prompting & Fine-tuning : Apply prompt engineering, model fine-tuning, and optimization techniques to improve GenAI solutions.
• Application Development : Collaborate with business and application teams to design, prototype, and deploy AI-driven workflows and user-facing features.
• Production-Readiness : Architect, build, and scale agent-based systems for enterprise use cases, ensuring performance, security, and maintainability.
• Cloud & Deployment : Deploy AI / ML workloads on AWS, Azure, and GCP, leveraging services like DynamoDB, storage, compute, and container runtimes.
• Leadership & Collaboration : Lead a team of engineers, providing technical guidance and ensuring alignment with business objectives while also contributing actively to coding and solution development.
• Stakeholder Communication : Engage with both technical and non-technical stakeholders to align AI solutions with business goals.
• Continuous Learning : Stay current with the latest in Generative AI, LLM research, orchestration frameworks, and MLOps / LLMOps practices.

Qualifications :

What Success Looks Like :