Senior GenAI Engineer

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.

Please visit our website - for more details

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.

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.
• Collaboration: Communicate effectively 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

• Experience: 5+ years in AI/ML engineering, including at least 2+ years in GenAI/LLM applications.
• Programming: Strong proficiency in Python (async, API integration); experience with Java, JavaScript/Node.js, React.js, and front-end technologies is a plus.
• LLMs & Frameworks: Hands-on experience with OpenAI, Anthropic, Mistral, or similar LLMs; orchestration with LangChain, CrewAI, AutoGen, Semantic Kernel, or LlamaIndex.
• Vector DBs & RAG: Expertise in embeddings and vector databases (Pinecone, FAISS, Chroma) and retrieval-based architectures.
• MCP & Agent Systems: Experience with MCP server implementation and building agent-based GenAI solutions.
• Cloud Platforms: Practical experience deploying AI/ML systems on AWS, Azure, or GCP.
• Version Control: Strong understanding of Git and collaborative SDLC practices.
• MLOps/LLMOps: Knowledge of CI/CD, monitoring, and ML/LLM operationalization best practices.
• Nice to Have: Database expertise (SQL, NoSQL, DynamoDB), containerization (Docker/Kubernetes), Infra-as-Code (Terraform), and experience with front-end integrations.

What Success Looks Like

• Delivery of scalable, production-ready GenAI solutions with measurable impact on business workflows.
• Demonstrated ability to translate business problems into structured ML/GenAI solutions.
• Strong collaboration across product, engineering, and business teams.
• Continuous improvement in solution performance, reliability, and user adoption.