AIML Engineer

We are seeking an AI/ML Engineer Agents to help build the core intelligence layer of our AI-powered MarTech and AdTech platform. This role will focus on designing developing and deploying agentic AI systems LLM-powered applications and real-time inference pipelines that drive intelligent automation personalization and decision-making at scale.

This is a high-impact hands-on engineering role for someone who thrives in fast-moving environments and wants to help shape next-generation AI products from the ground up.

Key Responsibilities

• Design and deploy agentic AI systems using frameworks such as LangGraph AutoGen CrewAI or similar.
• Build and scale LLM/GenAI applications for MarTech and AdTech use cases including content generation campaign optimization audience segmentation personalization and workflow automation.
• Fine-tune and optimize LLMs/SLMs for domain-specific tasks such as classification extraction recommendation and intent understanding.
• Develop and manage training and inference pipelines for production-scale ML systems.
• Build scalable infrastructure for real-time and batch inference experimentation and deployment.
• Implement best practices for evaluation A/B testing model monitoring and continuous improvement.
• Partner closely with product data and engineering teams to turn prototypes and research into production-ready AI services.
• Rapidly prototype and apply emerging research to practical product use cases.

Qualifications

• 3 years of experience in AI/ML engineering machine learning or applied NLP roles.
• Masters degree in Computer Science Engineering or a related field required; PhD preferred.
• Hands-on experience with agent-based AI frameworks such as LangGraph AutoGen CrewAI or similar.
• Strong expertise in deep learning and natural language processing using frameworks such as PyTorch or TensorFlow.
• Experience fine-tuning and deploying foundation models such as LLaMA Mistral or similar.
• Strong experience with Hugging Face LoRA PEFT and modern fine-tuning workflows.
• Experience with RAG pipelines vector databases semantic search and embedding optimization tools such as FAISS Pinecone or PGVector.
• Experience deploying ML systems in cloud environments such as AWS GCP or Azure.
• Strong software engineering skills in Python APIs microservices and containerized environments such as Docker and Kubernetes.
• Solid understanding of GPU optimization distributed training and inference performance tuning.

Preferred Qualifications

• Experience in MarTech AdTech personalization targeting or attribution systems.
• Experience with real-time decisioning or ad-serving systems.
• Contributions to open-source AI/LLM frameworks or published research.
• Prior experience in a startup or high-growth environment.