Senior AI Engineer

Job description

Role Overview We are seeking a Senior AI Engineer to design, build, and deploy enterprise-grade Generative AI and Agentic AI solutions. The role requires strong hands-on engineering, architecture, stakeholder collaboration, and end-to-end ownership from PoC to production across cloud and on-premise environments. Key Responsibilities • Develop innovative Enterprise GenAI and Agentic AI solutions for complex business use cases. • Collaborate with business stakeholders to understand requirements and translate them into meaningful, scalable solutions. • Work with automotive domain experts to gain relevant insights and incorporate them into AI applications. • Design and build enterprise-grade RAG, GraphRAG, and multi-agent applications. • Architect cloud-agnostic AI/GenAI solutions across Azure, AWS, GCP, and on-premise environments. • Build and host AI applications and models on-premise using vLLM, Ollama, or similar platforms. • Own the complete application lifecycle, from PoC and MVP to production deployment and ongoing optimization. • Evaluate agents and implement efficient tracking of agent actions, tool calls, execution flows, quality, latency, cost, and failures. • Fine-tune and optimize open-source SLMs/LLMs for domain-specific use cases. Required Skills • Hands-on experience building agents with LangGraph, AutoGen, Semantic Kernel, Google ADK, Microsoft Agent Framework, or similar frameworks. • Experience building agents on Microsoft Copilot Studio, AWS Bedrock AgentCore, Azure AI Foundry, or comparable platforms. • Strong expertise in Python, with mandatory experience in FastAPI and Flask. • Experience developing and deploying enterprise applications on Azure, AWS, and/or GCP. • Experience architecting and delivering cloud-agnostic AI/GenAI solutions. • Experience hosting and operating models and applications in on-premise environments. • Experience with domain fine-tuning of open-source models such as Gemma, Llama, and Mistral. • Experience building production-grade RAG and GraphRAG applications. • Experience with agent evaluation, observability, benchmarking, tracing, and AgentOps practices. • Knowledge of Transformers, GANs, Vision-Language Models (VLMs), multimodal AI, SLMs, and LLMs. • Familiarity with machine learning algorithms, including classification, regression, and clustering. • Ability to work effectively in a fast-paced environment and take end-to-end ownership. Must to Have · Knowledge of GPUs and CUDA , including efficient GPU utilization for model hosting, inference , and fine-tuning. • Familiarity with LoRA, QLoRA, PEFT, and other parameter-efficient fine-tuning techniques . • Hands-on experience building agents with LangGraph, AutoGen, Semantic Kernel, Microsoft Agent Framework , or similar frameworks • Experience with Docker, Kubernetes, CI/CD, MLOps, and LLMOps . • Experience with SQL and NoSQL databases. Experience 5+ years of overall software engineering or AI/ML experience, with strong hands-on exposure to Generative AI, Agentic AI, and production-grade AI solutions.