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.
