Artificial Intelligence; AI Engineer

Position: Artificial Intelligence (AI) Engineer
Description

AI Lead Engineer

Skills:

- Proactive self-starter with excellent interpersonal, communication, and customer service skills.

- Expert-level AI/ML and full-stack development skills, with strong hands-on experience building and integrating backend services and frontend applications using modern frameworks such as Node.js and React. Strong emphasis on clean, maintainable, reproducible, well-tested, and well-documented code.

- Ability to manage multiple tasks and projects simultaneously.

- Collaborative team player with a focus on achieving common goals.

- Meticulous attention to detail.

- Quick learner with a passion for staying current with emerging technologies and industry trends.

Requirements

Required Experience:

- Deep expertise in RAG systems, LLMs, embeddings, vector databases, and AI infrastructure

- Experience designing semantic retrieval and knowledge platforms, including curated corpora and grounding/citation patterns (e.g., "show your sources" for internal auditability)

- Experience evaluating AI models for different tasks

- Strong ability and experience to leverage cloud infrastructure

- Experience with data quality and metadata management (data lineage, dataset versioning, business glossary/taxonomy) and implementing automated quality checks and anomaly detection

- Experience implementing secure GenAI platform controls, including prompt logging, red-teaming, content filtering/leakage prevention, and model access controls/tenant isolation

- Excellent collaboration skills and ability to work with non-technical stakeholders

- Proven ability to work in existing codebases, improve reliability, performance, and readability over time.

- 5+ years of hands-on experience in machine learning engineering, with significant work developing and maintaining AI systems in production.

- Strong software engineering practices including modular design, refactoring, and technical debt management; unit, integration, and regression testing; as well as code reviews and shared coding standards

- Strong expertise in machine learning fundamentals and statistical modeling, including, but not limited to:

o Supervised, unsupervised, and reinforcement learning

o Model evaluation, bias, overfitting, and error analysis

o Probabilistic and statistical reasoning

- Proficiency in Python and major ML libraries (e.g., Tensor Flow, PyTorch, scikit-learn, Hugginge Face, Lang Chain, Llama Index).

- Hands-on experience with cloud-based ML platforms (AWS Sage Maker, Azure ML, or Google AI Platform).

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