AI/ML Engineer

Key Responsibilities

• Build agent-based features using LLMs and frameworks such as LangChain and LangGraph, including tool calling, orchestration and memory.
• Develop AI asset discovery for models, agents, prompts and AI services across cloud and AI platforms.
• Implement guardrail signals and enforcement, including PII detection and redaction, toxicity and content moderation, and prompt-injection and jailbreak detection.
• Build RAG pipelines, prompt templates and evaluation harnesses.
• Evaluate and benchmark models and guardrail classifiers for accuracy, latency and cost.
• Take new AI features from prototype to production for new product initiatives.
• Integrate AI services with backend microservices through well-defined APIs.
• Add observability, logging and monitoring to AI components.
• Work closely with the AI Architect on design and technical decisions.

Mandatory Skills

• Strong proficiency in Python, with experience in FastAPI or Flask.
• Experience in LLM application development: prompt engineering, RAG, embeddings and vector databases (e.g., pgvector, OpenSearch, Pinecone, Azure AI Search).
• Hands-on experience with agent frameworks such as LangChain, LangGraph or Semantic Kernel, and familiarity with the Model Context Protocol (MCP).
• Experience with AWS Bedrock/SageMaker and Azure OpenAI/Azure AI Foundry.
• Experience implementing AI safety controls, such as PII detection (e.g., Presidio), content moderation and prompt-injection defense (e.g., NeMo Guardrails, Guardrails AI, Llama Guard).
• Good understanding of ML and NLP fundamentals, including Hugging Face Transformers.
• Experience with LLM evaluation frameworks and methods.
• Familiarity with Git, Docker and CI/CD pipelines.

Good to Have

• Working knowledge of Java and Spring Boot.
• Knowledge of AI governance frameworks such as NIST AI RMF and the EU AI Act.
• Experience with MLOps practices and model fine-tuning.