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 - https://www.senzcraft.com for more detailsOur AI Operations SaaS platform – https://MagicOps.aiSenzcraft on linkedin -> https://www.linkedin.com/company/senzcraftSenzcraft 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 skilledSenior GenAI Engineerwith 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.