Senior AI Engineer

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 Lang. Graph, Auto. Gen, Semantic Kernel, Google ADK, Microsoft Agent Framework, or similar frameworks.
• Experience building agents on Microsoft Copilot Studio, AWS Bedrock Agent. Core, 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 Agent. Ops 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 Lang. Graph, Auto. Gen, 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.