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.

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.

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.

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.

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.