Senior GenAI Engineer - 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.

Please visit our website - for more details

Our AI Operations SaaS platform – on linkedin ->

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.

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 :

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.

Effective leadership in guiding a team while contributing hands-on to solution development.

Strong collaboration across product, engineering, and business teams.

Continuous improvement in solution performance, reliability, and user adoption.