About the Role:

The Applied AI Engineer will design, develop, build and deploy on-premise AI solutions, integrating large language models (LLMs), retrieval-augmented generation (RAG), and intelligent agents into enterprise systems. This role combines machine learning engineering, generative AI development, and prompt design to deliver secure, scalable, and business-aligned AI applications.

Key Responsibilities:

• Model Engineering :
• Fine-tune and optimize open-source LLMs using techniques such as LoRA/QLoRA.
• Quantize and deploy models efficiently using frameworks like vLLM, Triton, or TensorRT.
• Evaluate model performance with domain-specific benchmarks and metrics.

• RAG & AI Development :
• Implement embeddings, vector databases, and retrieval-augmented generation pipelines.
• Develop AI agents and workflows using frameworks such as LangChain, LangGraph, or Semantic Kernel.
• Connect AI systems securely with enterprise platforms (ERP, CRM, IoT, PLM, file systems).

• Prompt & Workflow Design :
• Engineer prompts, structured templates, and chaining logic to improve reliability.
• Collaborate with business teams to tailor AI outputs to specific use cases and compliance requirements.
• Implement guardrails, grounding, and refusal rules to reduce hallucinations and ensure safe outputs.

• Collaboration & Delivery :
• Work with Data Engineers and MLOps teams to integrate clean data pipelines.
• Partner with domain SMEs to align AI solutions with business processes.
• Document workflows, best practices, and provide internal training on AI usage.

Required Skills:

• Strong proficiency in Python and ML/AI libraries (PyTorch, HuggingFace, Transformers).
• Experience with LLM fine-tuning, optimization, and inference serving (LoRA, vLLM, Triton).
• Hands-on expertise with vector databases (Qdrant, Milvus, pgvector) and RAG pipelines.
• Knowledge of agent frameworks (LangChain, LangGraph, Semantic Kernel) and tool integration.
• Practical experience in prompt engineering and structured output design.
• Familiarity with on-prem AI deployment, containerization (Docker/Kubernetes), and API integrations.
• Strong problem-solving mindset and ability to translate business needs into AI solutions.