AI Developer

Role Overview:

You will play a critical role in bringing our AI initiatives for products to life ensuring they

are not just functional but production-grade secure and maintainable. This role

requires a unique blend of Machine Learning Engineering expertise MLOps best

practices and a deep understanding of the practical challenges in deploying generative

AI systems in a secure enterprise environment.

Key Responsibilities:

Production Application Development: Lead the end-to-end lifecycle of LLM

applications transitioning functional prototypes into robust scalable and

resilient production systems.

LLM API Integration & Orchestration: Design and implement robust integrations

with various LLM APIs (e.g. OpenAI Anthropic internal models) optimizing

performance cost and reliability.

Prompt Engineering & Optimization: Develop test and refine advanced prompt

engineering techniques to ensure accurate relevant and reliable model outputs

tailored to specific business use cases.

Context Management & RAG Implementation: Implement strategies for effective

context management including Retrieval-Augmented Generation (RAG) systems

vector databases and memory structures to enhance model relevance and

accuracy.

Output Validation & Quality Assurance: Establish rigorous validation frameworks

to automatically check and verify LLM outputs against predefined constraints

minimizing hallucinations and ensuring compliance with quality standards.

AI Security & Risk Mitigation: Implement robust security protocols to protect

against adversarial attacks specifically focusing on prompt injection indirect

prompt injection and SQL injection vulnerabilities within the LLM application

stack.

Production Deployment & Monitoring: Utilize MLOps principles to deploy

applications across cloud infrastructures (e.g. AWS GCP Azure) setting up

comprehensive monitoring for performance metrics latency token usage and

drift using tools like MLflow Weights & Biases or Prometheus.

Required Skills and Qualifications:

Experience: 5 years of professional experience as an ML Engineer or MLOps

Engineer with significant experience specifically focused on deploying LLM

applications into production environments (beyond just demos).

Technical Proficiency:

o Strong programming skills in Python.

o Hands-on experience with ML frameworks (e.g. PyTorch TensorFlow) and

orchestration tools (e.g. Kubeflow Airflow).

o Proficiency with cloud platforms (AWS GCP or Azure) and

containerization technologies (Docker Kubernetes).

o Experience with vector databases (e.g. Pinecone Weaviate Chroma) and

RAG architecture patterns.

o Familiarity with MLOps tools for tracking deployment and monitoring.

LLM Domain Knowledge: Deep understanding of current LLM capabilities

limitations prompt engineering best practices and emerging security

vulnerabilities in generative AI.

Problem-Solving: Strong analytical skills with a proactive approach to

troubleshooting complex production issues related to model performance

latency and system stability.

Communication: Excellent collaboration and communication skills capable of

working effectively within cross-functional teams (Data Scientists Software

Engineers Security Teams).