Apexanalytix is a longstanding innovator in procure-to-pay solutions, seeking an AI Engineer to drive the development of advanced GenAI applications. The role involves building robust AI systems and collaborating with cross-functional teams to implement innovative solutions.

Responsibilities:

Fine-tune and deploy open-source general purpose LLMs and code-generation models (e.g. Llama 4, qwen2.5) using PEFT, LoRA, QLoRA, instruction tuning, RLHF, etc
Design GenAI applications with agentic capabilities—autonomous, goal-driven agents that can plan, reason, and execute multi-step workflows with minimal human input
Implement function calling in workflows, enabling models to invoke external tools, APIs, databases, and services to perform actions or access real-time data
Build agentic RAG pipelines, where LLMs coordinate data retrieval and reasoning dynamically via retrieval-augmented generation
Create and maintain scalable pipelines, including data ingestion, labeling, feature engineering, vector DB management, and MLOps frameworks (MLflow, Kubeflow)
Containerize applications using Kubernetes and Docker
Optimize and monitor model performance, including latency, drift, bias, privacy, and security compliance
Collaborate cross-functionally, mediating between data, product, and engineering teams to translate requirements into agentic GenAI solutions
Stay current with research trends like agentic RL frameworks (e.g., ARTIST), multi-agent orchestration, MCP, and secure function calling

Requirements:

B.S or M.S. in Computer Science, Data Science, ML/AI or equivalent
3-5 years building and deploying AI/ML solutions with demonstrable production experience
Extensive experience fine-tuning LLMs and code models with advanced techniques (PEFT/LoRA/QLoRa/RLHF)
Proven track record designing or delivering agentic GenAI applications—function calling, autonomous workflows, RAG pipelines
Proficient in Python, PyTorch/TensorFlow, Hugging Face, LangChain, LangGraph or similar frameworks
Experience in Document and Image Analysis and extracting insights from unstructured data, including text documents and images
Strong infrastructure skills: Docker, Kubernetes, CI/CD, MLOps tooling
Sharp analytical mindset for system optimization, error mitigation, and security best practices
Excellent problem-solving and ability to translate business needs into technical design

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Engineering

AI Engineer